artifacts/transcript.md · VPS访问小红书受限解法

content

# Transcript · VPS访问小红书受限解法

## user · 2026-07-03 06:07:02

帮我想个解决一个需求，我发现中国大陆的小红书是一个本地经验平台，他的有效通常不在帖子，而是帖子下方的评论区。比如我去医院拔智齿，搜索相关话题，地区+医院名称+xxx医生/xxx功能（拔智齿）

然后通常会有很多网友亲身经历，避雷或者多个推荐的医生。这能很好的帮我去筛选资源。挂号到一些很不错的医生。（比如我原先选择的医生，一搜才知道他的口碑非常差，技术也比较差。有负面信息避雷贴，当然这个过程中，评论区也找到了不少推荐贴）

虽然有不少广告，但是广告托的特征和正常网友还是有明显区别（至少广告号点进去账号主页，发现全都是清一色的推销帖子或者该话题的测评贴，这种就是非正常账号）

因为vps端访问小红书，好像会被限制，你有什么好的解决方案吗？或者社区，开源项目有什么最新策略可以借鉴？

## assistant · 2026-07-03 06:07:48

### tool calls

- `hindsight_recall` `{"query": "小红书 VPS 访问 限制 方案 开源 爬取 评论"}`
- `web_search` `{"query": "小红书 VPS 访问限制 解决方案 2025 2026 开源项目 反爬"}`

## tool · 2026-07-03 06:07:48

Tool 'hindsight_recall' does not exist. Available tools: clarify, cronjob, delegate_task, patch, process, read_file, search_files, session_search, skill_manage, skill_view, skills_list, terminal, todo, tool_slimmer_hydrate_tools, tool_slimmer_request_full_tools, tool_slimmer_select, tool_slimmer_status, write_file

## tool · 2026-07-03 06:07:48

Tool 'web_search' does not exist. Available tools: clarify, cronjob, delegate_task, patch, process, read_file, search_files, session_search, skill_manage, skill_view, skills_list, terminal, todo, tool_slimmer_hydrate_tools, tool_slimmer_request_full_tools, tool_slimmer_select, tool_slimmer_status, write_file

## assistant · 2026-07-03 06:07:48

### tool calls

- `terminal` `{"command": "find /root -maxdepth 3 -type f \$ -name \"*.md\" -o -name \"*.py\" -o -name \"*.json\" \$ 2>/dev/null | grep -i -E \"(xhs|xiaohongshu|redbook|crawl|scrape|proxy)\" | head -30"}`
- `search_files` `{"path": "/root", "pattern": "xhs|xiaohongshu|redbook|crawl|scrape|proxy|反爬|小红书", "target": "content", "limit": 20}`

## tool · 2026-07-03 06:07:48

{"output": "/root/aliyun-bailian-proxy/SKILL.md\n/root/hermes-agent-configuration/references/xhs-mcp-http-api.md\n/root/hermes-agent-configuration/references/aliyun-model-proxy.md\n/root/hermes-agent-configuration/references/xhs-mcp-setup.md\n/root/hermes-agent-configuration/references/xhs-cli-alternatives.md\n/root/browser-data-extraction/references/xiaohongshu-extraction.md\n/root/social-media/xiaohongshu-mcp/SKILL.md\n/root/.xiaohongshu-cli/cookies.json\n/root/.xiaohongshu-cli/search_sessions.json\n/root/hermes_config_backups/20260624_223531_before_remove_cliproxyapi/CONFIG_OWNERSHIP.md\n/root/hermes_config_backups/20260623_175247_before_cliproxyapi_aux_fast/CONFIG_OWNERSHIP.md\n/root/.hermes/scripts/temp_oauth_connect_proxy.py\n/root/.hermes/temp-oauth-proxy/proxy.py\n/root/.hermes/xhs-data/cookies.json\n/root/check_xhs_config.py\n/root/aliyun-model-proxy-deploy.md\n/root/test_xhs_mcp.py\n/root/test_xhs_simple.py\n/root/devops/proxy-client-configuration/SKILL.md\n/root/devops/caddy-reverse-proxy/SKILL.md", "exit_code": 0, "error": null}

## tool · 2026-07-03 06:07:48

{"total_count": 20, "matches_format": "path-grouped: each file path on its own line, followed by indented '<line>: <content>' rows for matches in that file", "matches_text": "/root/marketing_management_public_case_pool.md\n 194: #### A. 汽车销售：懂车帝/小红书/抖音种草 + 4S 店成交\n 205: #### B. 新消费品牌：小红书种草 + 天猫/抖音成交\n 239: | 渠道归因 | BBVA Compass | 汽车购买链路 / 小红书种草链路 | 不能只看最后成交渠道 |\n 304: ### 汽车购买链路 / 小红书种草\n 307: - `小红书 种草 天猫 成交 归因`\n 326: - 用汽车购买链路或小红书种草链路替换 BBVA Compass。\n/root/hermes-skills-overview.html\n 87: <td>proxy-client-configuration 进化</td>\n 92: <td>caddy-reverse-proxy 进化</td>\n/root/racknerd-final.yaml\n 99: proxy-groups:\n/root/import_sessions_to_hindsight.py\n 87: \"20260609_102612_fb038291\": \"小红书/高德MCP接入 + 汕头牛肉火锅 (6月9日10:26)\",\n 88: \"20260609_160201_994b7a01\": \"小红书MCP安装测试 (6月9日16:02)\",\n 89: \"20260609_170052_8b7c4456\": \"净水器搜索 + xhs-mcp故障 (6月9日17:00)\",\n/root/devops/hermes-dashboard-maintenance/SKILL.md\n 9: related_skills: [hermes-webui-tunnel-debug, caddy-reverse-proxy]\n 30: After ~2 days of sustained uptime, uvicorn's h11 HTTP parser enters a state where it rejects all requests. This is a known quirk of Python h11's state machine under long-running `--insecure` mode with proxy_headers enabled.\n 51: # 2. HTTPS via domain (Caddy reverse proxy)\n 108: **A. Caddy reverse_proxy flush_interval -1**\n 110: In Caddy v2, `reverse_proxy` handles WebSocket transparently, but data buffering can exacerbate disconnect errors. Add `flush_interval -1` to force immediate data flushing:\n 114: \treverse_proxy 127.0.0.1:9119 {\n 278: # 2. HTTPS via domain (Caddy reverse proxy)\n 294: When containerized services expose ports to Caddy reverse proxy, always bind to `127.0.0.1` — not `0.0.0.0`. Caddy is on the same machine and can reach localhost; 0.0.0.0 exposes the port to the public internet."}

## assistant · 2026-07-03 06:07:48

### tool calls

- `skill_view` `{"name": "xhs-mcp-setup"}`
- `skill_view` `{"file_path": "references/xhs-mcp-http-api.md", "name": "hermes-agent-configuration"}`
- `skill_view` `{"file_path": "references/xhs-cli-alternatives.md", "name": "hermes-agent-configuration"}`

## tool · 2026-07-03 06:07:48

{"success": false, "error": "Skill 'xhs-mcp-setup' not found.", "available_skills": ["agent-memory-architecture", "agentic-workflows", "apple-platform", "browser-data-extraction", "collaborative-media-case-map", "comms-platforms", "computer-use", "diagramming", "dogfood", "exa-search", "fact-freshness", "gifs", "github-workflow", "hermes-agent-configuration", "hermes-cron-ops", "media-case-map", "media-tools", "memory-consolidator", "model-ops", "office-suite"], "hint": "Use skills_list to see all available skills"}

## tool · 2026-07-03 06:07:48

{"success": true, "name": "hermes-agent-configuration", "file": "references/xhs-mcp-http-api.md", "content": "# Xiaohongshu MCP HTTP REST API\n\nThe xiaohongshu-mcp server (xpzouying, 14k⭐) exposes **two interfaces**:\n- **Stdio MCP** (via `uvx --from jobson-xhs-mcp xhs-mcp`) — Hermes default transport\n- **HTTP REST API** (via Go binary `xhs-mcp -port :18060`) — standalone server\n\n## When the stdio MCP search returns empty\n\nHermes `mcp_xhs_search_notes` (mapped to MCP tool `search_feeds`) may return `\"未找到相关笔记\"` even when cookies are valid. In that case, try the HTTP REST API directly.\n\n## Key REST Endpoints\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `/api/v1/feeds/search?keyword=xxx` | GET | Search notes by keyword |\n| `/api/v1/feeds/detail` | POST | Get note detail (body: `{\"feed_id\":\"...\",\"xsec_token\":\"...\"}`) |\n| `/api/v1/login/status` | GET | Check login status |\n| `/api/v1/feeds/list` | GET | Get home feed |\n\n## Usage Examples\n\n```bash\n# Search\ncurl -s \"http://localhost:18060/api/v1/feeds/search?keyword=净水器\"\n\n# Get note detail\ncurl -s -X POST http://localhost:18060/api/v1/feeds/detail \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"feed_id\":\"68cfa2a1000000001201c50f\",\"xsec_token\":\"ABz...\"}'\n\n# Check login\ncurl -s http://localhost:18060/api/v1/login/status\n```\n\n## Cookie Loading Priority (Go Binary)\n\nThe Go binary (`/root/.hermes/bin/xhs-mcp`) loads cookies in this order:\n1. `/tmp/cookies.json` — legacy path, checked first\n2. `$COOKIES_PATH` env var — if set, use that file\n3. `./cookies.json` — current working directory fallback\n\n**To pass cookies on restart:**\n```bash\ncp /root/.hermes/xhs-data/cookies.json /tmp/cookies.json  # or\ncd /root/.hermes/xhs-data && COOKIES_PATH=/tmp/cookies.json xhs-mcp ...\n```\n\n## Restart After Crash\n\nFull restart procedure:\n```bash\n# 1. Kill everything\npkill -9 -f \"xhs-mcp\" 2>/dev/null\npkill -9 -f \"chrome.*headless\" 2>/dev/null\npkill -9 -f \"chrome.*crashpad\" 2>/dev/null\nsleep 3\n\n# 2. Verify nothing left\nps aux | grep -E \"chrome|xhs-mcp\" | grep -v grep\n\n# 3. Restart with explicit browser binary and cookies\ncd /root/.hermes/xhs-data && \\\nCOOKIES_PATH=/tmp/cookies.json \\\n/root/.hermes/bin/xhs-mcp \\\n  -port :18060 \\\n  -headless true \\\n  -bin /root/.cache/rod/browser/chromium-1321438/chrome\n```\n\n**Browser binary path** (`-bin` flag): Rod downloads its own Chromium at `/root/.cache/rod/browser/chromium-*/chrome`. Use the full path explicitly on restart. Without it, rod may fail to find the browser or download a fresh copy.\n\n**Timeout behavior**: If the headless browser gets stuck (context canceled), it's usually because the VPS IP is flagged by xiaohongshu anti-bot — the browser can't load the page. Clean all chrome processes and restart.\n\n## Workflow Optimization\n\n**DO NOT call `get_feed_detail` for every note.** The search API already returns enough data:\n- Note title, author nickname\n- Likes, collects, comments, shares counts\n- Cover image URLs\n\nThe detail API is slow (10-30s per call), unreliable (many notes return \"feed not found\" — xsec_token expiry), and often returns empty descriptions. Search results data (`noteCard.interactInfo`) is sufficient for most analysis.\n\n```\n✓ Fast path:    search → use noteCard data from results\n✗ Slow path:    search → detail call per note → time out on most\n```\n\nIf you must read a specific note's content or comments, verify the note URL works in a browser first — expired xsec_tokens are unrecoverable via API.\n\n## Related Tools\n\n- **xiaohongshu-cli** (jackwener, 2.2k⭐, Python, Apache 2.0): `uv tool install xiaohongshu-cli` — signed API calls, fast but IP-dependent\n- **redbook** (lucasygu, 328⭐, TypeScript, MIT): `npm install -g @lucasygu/redbook` — same approach, smaller community\n\nSee `references/xhs-cli-alternatives.md` for detailed comparison.\n\n## Implementation Note\n\nTwo versions of xhs-mcp exist:\n1. **Go binary** (`/root/.hermes/bin/xhs-mcp`) — Supports `-port`, `-headless`, HTTP server. Reads cookies from `cookies.json` file.\n2. **Python (uvx)** (`jobson-xhs-mcp` package) — Stdio-only Hermes transport. Uses `XHS_COOKIE` env var for auth.\n\nThe Go binary's HTTP REST API is more reliable for data extraction when the stdio MCP transport has issues. Both use the same headless browser under the hood (go-rod / playwright).\n", "file_type": ".md"}

## tool · 2026-07-03 06:07:48

{"success": true, "name": "hermes-agent-configuration", "file": "references/xhs-cli-alternatives.md", "content": "# Xiaohongshu CLI Alternatives\n\nTwo alternative CLI tools exist that bypass the headless browser approach used by xhs-mcp. Both use **reverse-engineered API signatures** to make direct HTTP calls to xiaohongshu's internal API (`edith.xiaohongshu.com`).\n\n## Comparison\n\n| Feature | xhs-mcp (xpzouying) | xiaohongshu-cli (jackwener) | redbook (lucasygu) |\n|---------|---------------------|------------------------------|--------------------|\n| ⭐ Stars | 14k | 2.2k | 328 |\n| Language | Go (headless browser) | Python (signed API) | TypeScript (signed API) |\n| Speed | 5-30s (browser launch) | 0.5-2s (direct API) | 0.5-2s (direct API) |\n| Auth | Cookie file / env var | Browser cookie extraction + QR | Browser cookie extraction |\n| Anti-bot | Browser fingerprint | Gaussian jitter, captcha cooldown | — |\n| Comments | Via feed detail API | `xhs comments --all` | `redbook comments --all` |\n| HTTP API | Built-in (port 18060) | No | No |\n| License | — | Apache 2.0 | MIT |\n| Install | Binary download | `uv tool install xiaohongshu-cli` | `npm install -g @lucasygu/redbook` |\n\n## xiaohongshu-cli (Recommended)\n\n### Installation\n```bash\nuv tool install xiaohongshu-cli\n```\n\n### Cookie Setup\nCookies file: `~/.xiaohongshu-cli/cookies.json` — **dict format** (not array):\n```json\n{\n \"a1\": \"19eab...\",\n \"web_session\": \"0400...\"\n}\n```\n\nAuto-extract from browser: `xhs login` (detects Chrome/Firefox/Safari etc.)\nOr copy from existing xhs-mpc cookies.json → convert to dict.\n\n### Key Commands\n```bash\nxhs search \"关键词\" --sort popular # Search notes\nxhs read <url> # Read note\nxhs comments <url> --all # Get all comments\nxhs feed # Home feed\nxhs whoami # Check login\n```\n\n### Captcha Issues\n- IP triggers captcha (type=216) if flagged by xiaohongshu\n- Auto-retries with cooling (5s → 10s → 20s → 30s)\n- Solution: clean IP (different VPS / proxy / home network)\n- Captcha UUID given — can solve in browser manually for testing\n\n### Anti-Detection Features\n- Gaussian jitter delays (~1-1.5s) mimicking human browsing\n- Chrome fingerprint consistency (UA, sec-ch-ua, platform)\n- Exponential backoff on HTTP 429/5xx\n- Captcha cooldown doubles delay permanently after trigger\n- x-s / x-s-common / x-t request signatures\n\n## redbook\n\n### Installation\n```bash\nnpm install -g @lucasygu/redbook\n```\n\n### Cookie Setup\nAuto-extracts from Chrome. Manual: `--cookie-string \"a1=值; web_session=值\"`\n\n### Key Commands\n```bash\nredbook search \"关键词\" --sort popular\nredbook read <url>\nredbook comments <url> --all\nredbook analyze-viral <url> # Viral note analysis\nredbook feed\n```\n\n### Bonus Features\n- 13 analysis modules (keyword matrix, cross-topic heatmap, opportunity scoring)\n- Markdown → XHS-style image card rendering (7 themes)\n- Rate-limit detection via creator API hidden level field\n\n## When to Use Which\n\n- **From this VPS** (IP flagged): xhs-mcp HTTP API via headless browser (slow but works)\n- **From clean IP / local**: xiaohongshu-cli (fastest, most features)\n- **Need analysis** (viral patterns, keyword research): redbook\n- **MCP-integrated**: xhs-mcp stdio (via Hermes)\n\n## Cookie Format Conversion\n\nBetween xhs-mcp (Rod JSON array) and xiaohongshu-cli (dict):\n```python\n# Rod format → dict\nrod_cookies = json.load(open('cookies.json'))\ncli_cookies = {c['name']: c['value'] for c in rod_cookies}\njson.dump(cli_cookies, open('~/.xiaohongshu-cli/cookies.json', 'w'))\n```\n", "file_type": ".md"}

## assistant · 2026-07-03 06:07:48

### tool calls

- `read_file` `{"path": "/root/browser-data-extraction/references/xiaohongshu-extraction.md"}`
- `terminal` `{"command": "ls -la /root/.xiaohongshu-cli /root/.hermes/xhs-data /root/social-media/xiaohongshu-mcp 2>/dev/null && printf '\\n--- check_xhs_config.py ---\\n' && sed -n '1,220p' /root/check_xhs_config.py 2>/dev/null"}`

## tool · 2026-07-03 06:07:48

{"content": "1|# 小红书 (Xiaohongshu) Data Extraction\n2|\n3|## Overview\n4|Heavily bot-protected platform. Browser blocked (\"IP at risk\"), web_extract empty. Two working approaches:\n5|\n6|## A: xhs-mcp HTTP API (slow, reliable)\n7|**Pre-installed** via Hermes MCP config. Two transport layers:\n8|- **MCP stdio tools** (`mcp_xhs_search_notes`): **Unreliable** — search often returns empty.\n9|- **Go HTTP server** at `localhost:18060`: **More reliable** — bypasses stdio issues.\n10|\n11|### Key endpoints\n12|```\n13|GET  /health                              # Health check\n14|GET  /api/v1/feeds/search?keyword=X       # Search → feed cards w/ id + xsecToken\n15|POST /api/v1/feeds/detail                 # Content + comments in data.data.comments.list\n16|     {\"feed_id\":\"...\",\"xsec_token\":\"...\"}\n17|```\n18|\n19|### Speed: 5-30s per search. Detail: 10-15s each.\n20|\n21|### Pitfalls\n22|- **xsec_token expires** — many search-result notes unreachable via detail API\n23|- **Browser resource leak** — restart after repeated searches: kill + background restart\n24|- **Browser download fragile** — cache at `~/.cache/rod/browser/`. Delete if broken.\n25|- **MCP stdio tools often empty** for queries that work via HTTP API\n26|\n27|## B: xiaohongshu-cli (fast, IP-sensitive)\n28|`uv tool install xiaohongshu-cli`. Uses **reverse-engineered API signatures** (x-s/x-s-common).\n29|### Speed: ~1-3s per call. No headless browser.\n30|\n31|### Captcha issue\n32|VPS/flagged IPs trigger `type=216` captcha. Auto-retry w/ cooldown fails eventually.\n33|**Solution**: residential IP or proxy.\n34|\n35|### Cookie format\n36|`~/.xiaohongshu-cli/cookies.json` expects dict: `{\"a1\":\"...\",\"web_session\":\"...\"}`\n37|\n38|## Decision tree\n39|- VPS, 1-2 searches → HTTP API :18060 (restart if timeout)\n40|- VPS, bulk → try xiaohongshu-cli + proxy, or run locally\n41|- Home network → xiaohongshu-cli (much faster)\n42|\n43|## Restart xhs-mcp\n44|```bash\n45|kill $(lsof -t -i:18060) 2>/dev/null\n46|terminal(command=\"/root/.hermes/bin/xhs-mcp -port :18060 -headless true\", background=true)\n47|sleep 3 && curl -s http://localhost:18060/health\n48|```\n49|", "total_lines": 48, "file_size": 1972, "truncated": false, "is_binary": false, "is_image": false}

## tool · 2026-07-03 06:07:48

{"output": "/root/.hermes/xhs-data:\ntotal 12\ndrwxr-xr-x  2 root root 4096 Jun  9 16:43 .\ndrwx------ 36 root root 4096 Jul  3 06:07 ..\n-rw-r--r--  1 root root 3079 Jun  9 16:43 cookies.json\n\n/root/social-media/xiaohongshu-mcp:\ntotal 32\ndrwxr-xr-x 5 root root 4096 Jun  9 17:23 .\ndrwxr-xr-x 4 root root 4096 Jun  9 16:51 ..\ndrwxr-xr-x 2 root root 4096 Jun  9 17:24 references\ndrwxr-xr-x 2 root root 4096 Jun  9 17:00 scripts\n-rw------- 1 root root 8964 Jun  9 17:23 SKILL.md\ndrwxr-xr-x 2 root root 4096 Jun  9 16:59 templates\n\n/root/.xiaohongshu-cli:\ntotal 36\ndrwxr-xr-x  2 root root  4096 Jun  9 17:31 .\ndrwx------ 97 root root 20480 Jul  2 00:29 ..\n-rw-r--r--  1 root root   119 Jun  9 17:31 cookies.json\n-rw-r--r--  1 root root     0 Jun  9 17:31 cookies.txt\n-rw-------  1 root root   165 Jun  9 17:48 search_sessions.json\n\n--- check_xhs_config.py ---\n#!/usr/bin/env python3\n\"\"\"Verify xhs-mcp configuration\"\"\"\nimport yaml\nwith open('/root/.hermes/config.yaml') as f:\n    cfg = yaml.safe_load(f)\nxhs = cfg.get('mcp_servers', {}).get('xhs', {})\ncookie = xhs.get('env', {}).get('XHS_COOKIE', '')\nprint(f'Args: {xhs.get(\"args\")}')\nprint(f'Cookie length: {len(cookie)}')\nchecks = ['web_session=', 'a1=', 'x-rednote-']\nfor c in checks:\n    print(f'  Has {c}: {c in cookie}')\nprint(f'Cookie: {cookie[:40]}...{cookie[-20:]}')", "exit_code": 0, "error": null}

## assistant · 2026-07-03 06:07:48

Boss，先给结论：**从这台 VPS 硬搜小红书，目前最稳的做法不是继续加代理“硬冲”，而是把现有三个工具分层用起来，并把真正的“口碑挖掘”放到评论层做过滤。**

你已经装了完整栈，不需要重新找项目，关键是怎么编排：

- **VPS 兜底层**：用现有的 `xhs-mcp` HTTP 接口（`localhost:18060`）做低频搜索和高互动笔记的评论抓取。它走 headless browser，慢，但比直连 API 更难被当场拦掉。
- **主力搜索层**：用 `xiaohongshu-cli` 或 `redbook` 做关键词搜索。它们走内部签名 API，速度快，但 VPS  flagged IP 很容易出 `type=216` 验证码。所以这层最好跑在干净 IP / 住宅代理 / 你自己本地机器上。
- **评论层分析**：你的需求核心本来就不在帖子正文，而在评论区“真实经历 + 避雷 + 推荐”。所以流程应该是：
  1. 搜索：`汕头 医院 拔智齿 医生推荐`
  2. 从搜索结果里挑互动高、看起来像真实经历的笔记
  3. 抓评论，按“账号是否是广告号”过滤
  4. 汇总成：**推荐医生 / 避雷医生 / 信号强度**

**广告号过滤策略**，可以按你 observed 特征落地：
- 账号主页帖子高度同质化（全是同一科室测评、同一医院推荐、或全是带商品链接）
- 同一账号在多个笔记下发近似文案
- 新号 / 粉丝少但评论高频
- 评论内容含明显外链、加微、预约话术

这些可以直接做规则打分，不需要上大模型。

**VPS 侧稳定性建议**：
- `xhs-mcp` 的 detail API 很慢且 `xsec_token` 容易过期，别逐篇打详情；搜索结果里的 `noteCard.interactInfo` 通常足够初筛
- 遇到 `IP at risk` / timeout，直接 kill 掉 chrome 和 `xhs-mcp`，换 cookies 或等一会再起，不要空转重试
- 批量任务别在单 IP 高频跑，容易把 cookies 也带崩

**可借鉴的最新开源策略**：
- `jackwener/xiaohongshu-cli`：Gaussian jitter、验证码冷却、指数退避
- `lucasygu/redbook`：分析模块里已经有“病毒式传播分析、关键词矩阵”，适合做“哪些医生被高频正面/负面提及”的量化
- `xpzouying/xhs-mcp`：HTTP 模式比 stdio 可靠，适合脚本化

如果你愿意，我下一步可以直接把这套流程做成一套脚本/工作流，接你现在这套 Hermes 工具链，输入“地区 + 医院 + 项目”就能输出一份“避雷/推荐医生 + 证据来源”的短报告。要做吗？

## session_meta · 2026-07-03 06:07:48

## user · 2026-07-03 06:19:35

你查一下hindsight有关小红书的记录，我们之前的小红书mcp，跑了一会汕头哪个牛肉店好吃就被截断了，账号也收到了警告

对此，下次你会怎么调整

## assistant · 2026-07-03 06:21:04

### tool calls

- `skill_view` `{"name":"browser-data-extraction"}`
- `search_files` `{"path":"/root","pattern":"小红书|xhs|xiaohongshu|牛肉|汕头|警告|截断|风控","target":"content","limit":50}`
- `terminal` `{"command":"python3 - <<'PY'\nimport os, json\npaths=['/root/.xiaohongshu-cli/search_sessions.json','/root/.xiaohongshu-cli/cookies.json','/root/.hermes/xhs-data/cookies.json','/root/social-media/xiaohongshu-mcp/SKILL.md']\nfor p in paths:\n print('\\n---',p,'---')\n try:\n st=os.stat(p); print('exists size',st.st_size,'mtime',st.st_mtime)\n if p.endswith('search_sessions.json'):\n print(open(p,encoding='utf-8').read()[:2000])\n elif p.endswith('SKILL.md'):\n s=open(p,encoding='utf-8').read()\n for kw in ['警告','风控','截断','牛肉','汕头','captcha','type=216','rate']:\n if kw.lower() in s.lower(): print('has',kw)\n except Exception as e: print('ERR',e)\nPY"}`

## tool · 2026-07-03 06:21:04

{"success": true, "name": "browser-data-extraction", "description": "Extract data from web pages that resist standard tools — terminal-emulator pages, JavaScript-rendered tables, interactive dashboards, and sites where web_extract or browser_snapshot return garbled or empty content.", "tags": [], "related_skills": [], "content": "---\nname: browser-data-extraction\ncategory: research\ndescription: Extract data from web pages that resist standard tools — terminal-emulator pages, JavaScript-rendered tables, interactive dashboards, and sites where web_extract or browser_snapshot return garbled or empty content.\ntrigger: User asks to read/extract data from a URL where standard tools fail\n---\n\n# Browser Data Extraction\n\n## Cost Model\n\n**Completely free.** Uses Hermes's bundled local Chromium browser — no external API, no per-request fees. Costs only:\n- ~200-500 MB RAM during extraction (released when browser closes)\n- LLM token cost when reading and summarizing extracted content\n\n## Relationship with Firecrawl\n\nFirecrawl (`web_extract`) is the FIRST choice for all pages — fast (~0.5s), clean, cheap. Use THIS skill only as fallback when Firecrawl fails.\n\n**Cloudscraper is the SECOND choice** (lighter than browser, handles Cloudflare). Only fall back to full browser when cloudscraper also fails.\n\nThe order is always: Firecrawl → cloudscraper → browser (if both lighter options fail).\n\n## When to use\n\nStandard `web_extract` or `browser_snapshot` comes back empty, garbled, or truncated, but the page visibly has data when viewed in a real browser. Common culprits:\n\n## Lighter Alternative: cloudscraper (CF bypass, no browser)\n\nBefore spinning up a full headless browser, try **cloudscraper** — a Python library that mimics browser TLS fingerprints to bypass Cloudflare. It is:\n- **~20x faster** than browser (1-2s vs 5-10s per page)\n- **~0 MB RAM** (no Chromium process)\n- **Already installed** in the Hermes venv (`/usr/local/lib/hermes-agent/venv/bin/python3`)\n\n### When to use cloudscraper vs browser cloudscraper\n\n| Scenario | Use |\n|----------|-----|\n| Page behind Cloudflare challenge | cloudscraper |\n| SPA that renders after JS execution | browser (unless there's an SSR API too) |\n| Terminal emulator (xterm.js) | **browser** — cloudscraper gets the terminal widget, not the data inside |\n| Tabbed dashboard (data behind clickable tabs) | **browser** — cloudscraper can't click tabs |\n| Static HTML page | web_extract (no need for either) |\n| RSS/Atom feed | web_extract or cloudscraper (either works) |\n\n### Basic usage\n\n```python\nimport cloudscraper\nscraper = cloudscraper.create_scraper()\nresp = scraper.get('https://example.com/', timeout=15)\nprint(resp.text) # Fully rendered HTML, CF bypassed\n```\n\n### Pitfalls\n\n- cloudscraper **cannot click tabs, run JS, or interact with SPAs** — it's an HTTP client with CF bypass, not a browser\n- Some aggressive WAFs still block it (rare); fall back to browser when it fails with a 403\n- The `timeout` param is critical — CF challenges can take 10+ seconds even when they succeed\n- Always use the Hermes venv's python, not the system python: `/usr/local/lib/hermes-agent/venv/bin/python3`\n\n### Confirmed working sites (tested 2026-06-18)\n\n| Site | Status |\n|------|--------|\n| producthunt.com | ✅ 200 (homepage, product pages, feed) |\n| hunted.space | ✅ 200 |\n| news.ycombinator.com | ✅ 200 |\n| mcp.so | ✅ 200 |\n\n- **Terminal-emulator pages** (NodeQuality, bench.sh web results, monitoring dashboards rendered via xterm.js)\n- **SPA pages** where content loads after JS execution\n- **Tabbed dashboards** where data is sectioned behind clickable tabs\n- **Pages with CSS-encoded text** (unicode characters that render as ASCII art in visual mode)\n\n## The technique\n\n### Step 1: Try web_extract first (fast, cheap)\n```python\nweb_extract(urls=[\"https://example.com/report\"])\n```\nIf it returns useful markdown text — done. If garbled or empty, proceed.\n\n### Step 2: Load with browser_navigate\n```\nbrowser_navigate(url=\"...\")\n```\nReturns a compact snapshot showing interactive elements (buttons, tabs, links with `ref` IDs).\n\n### Step 3: Identify the data-access pattern\nLook for tabs, accordions, or \"copy text\" buttons in the snapshot. Terminal pages often hide data in sections behind clickable tab labels.\n\n### Step 4: Click tabs and extract via browser_console\n```\n# Click each tab\nbrowser_click(ref=\"@e12\") # Tab 1 (e.g. 基本信息)\nbrowser_click(ref=\"@e13\") # Tab 2 (e.g. IP质量)\n\n# After each click, extract via console\nbrowser_console(expression=\"document.body.innerText\")\n```\n\n`document.body.innerText` is the most reliable — the accessibility tree (snapshot) may show garbled characters, but the DOM text node still has clean data.\n\n### Step 5: Parse the data\nThe console output returns as a block of raw text with ANSI-style characters and formatting. Extract the structured information relevant to your task.\n\n## Pitfalls\n\n- **browser_vision is NOT a fallback for data extraction** — it is for visual inspection (layout, screenshots). It cannot reliably read terminal tables or dense text. Use browser_console instead.\n- **Do not call browser_snapshot(full=true) repeatedly** on terminal pages — the accessibility tree represents the terminal widget, not the rendered data inside it. You get the same garbled output every time.\n- **Tab ref IDs reset on navigate** — when going to a new URL, the ref IDs (@e12 etc.) will be different. Re-identify them from the fresh snapshot.\n- **Click one tab at a time** — clicking a tab replaces the content section, it does not accumulate. Click -> extract -> move to next tab.\n- **Some pages limit data visible per section** — NodeQuality.com requires clicking each tab separately (全部, 基本信息, IP质量, 网络质量, 回程路由) to get full data.\n\n## Reference: 小红书 (Xiaohongshu) data extraction\n\nSee `references/xiaohongshu-extraction.md` for the complete guide. TL;DR:\n- Browser blocked, web_extract empty → use the pre-installed xhs-mcp HTTP API at `localhost:18060`\n- MCP stdio tools (`mcp_xhs_search_notes`) often return empty; the HTTP API is more reliable\n- Alternative: `xiaohongshu-cli` (signed API, fast but IP-sensitive, triggers captcha on VPS IPs)\n\n## Reference: Product Hunt data extraction\n\nSee `references/producthunt-extraction.md` for the full technique. TL;DR:\n- No working REST API — use **Atom feed** (`/feed`) for product list + **homepage Apollo SSR data** for trending slugs\n- Apollo SSR data contains JavaScript `undefined` literal — **must replace `:undefined` with `:null`** before JSON parsing\n- Vote counts require individual product page scraping (too heavy for daily cron); skip or estimate from feed position\n- Deduplication via JSON tracking file to avoid pushing the same product twice\n- See `/root/.hermes/scripts/product-hunt-daily.py` for the complete daily digest implementation\n\n## Reference: CSTD.cn (cstd.cn) authenticated media extraction\n\nChinese e-learning platform that requires login for video retrieval. Content is not in static HTML — video URLs come from an AJAX endpoint after authentication.\n\n### Login & authentication\n- **Cookie pitfall**: `document.cookie` in browser console often misses `JSESSIONID` and other HttpOnly cookies. Always get cookies from **Application tab → Cookies → <domain>** in DevTools, not the Console.\n- **Login endpoints**:\n - Mobile/password: `POST /web/sysUserLogen` with `mobile` and `pwd`\n - WeChat OAuth: WxLogin component with `appid: wxe8259dcb19beeb2d`, redirect_uri: `/web/getUser`\n- **WeChat OAuth cannot be replicated server-side** — it requires a real WeChat client to confirm authorization. Do not attempt to build a static QR code for it.\n\n### Video URL retrieval pattern\n1. Course page: `/web/courseplanDetails?id=<courseId>` (publicly accessible)\n2. AJAX call: `GET /web/getOnlinCourseUrl?id=<courseId>` (requires login)\n3. Response JSON:\n ```json\n {\"cod

...[truncated 2766 chars]

## tool · 2026-07-03 06:21:04

{"total_count": 50, "matches_format": "path-grouped: each file path on its own line, followed by indented '<line>: <content>' rows for matches in that file", "matches_text": "/root/marketing_management_public_case_pool.md\n 194: #### A. 汽车销售：懂车帝/小红书/抖音种草 + 4S 店成交\n 205: #### B. 新消费品牌：小红书种草 + 天猫/抖音成交\n 239: | 渠道归因 | BBVA Compass | 汽车购买链路 / 小红书种草链路 | 不能只看最后成交渠道 |\n 304: ### 汽车购买链路 / 小红书种草\n 307: - `小红书 种草 天猫 成交 归因`\n 326: - 用汽车购买链路或小红书种草链路替换 BBVA Compass。\n/root/import_sessions_to_hindsight.py\n 87: \"20260609_102612_fb038291\": \"小红书/高德MCP接入 + 汕头牛肉火锅 (6月9日10:26)\",\n 88: \"20260609_160201_994b7a01\": \"小红书MCP安装测试 (6月9日16:02)\",\n 89: \"20260609_170052_8b7c4456\": \"净水器搜索 + xhs-mcp故障 (6月9日17:00)\",\n/root/devops/proxy-client-configuration/SKILL.md\n 99: **AnyTLS 配置警告：** 客户端 outbound **绝对不能**设置 `tcp_fast_open: true`。sing-box 源码明确阻止（nil pointer dereference），启动即报错。服务端 inbound 设置 TFO 是安全的。\n/root/devops/sing-box-ops/SKILL.md\n 560: | **\"Origin CA Key 已弃用\" 混淆** | 用户看到弃用警告后不知用什么凭证 | 告知用户：Origin CA Key（已弃用）≠ Global API Key。同一页面往下翻或旁边就是 **Global API Key**。更推荐创建 **API Token**（`cfut_...` 格式，无需邮箱） |\n/root/marketing_management_public_case_deepened.md\n 197: - 再看懂车帝、小红书、B站测评\n/root/test_xhs_simple.py\n 2: \"\"\"Test xhs-mcp via direct HTTP API (simpler)\"\"\"\n 5: COOKIE = \"acw_tc=0a00d54217809917546276171e41e7739a273920efbe4747857c8a587b02c9; abRequestId=a2b31348-f3b8-5164-9b8a-8541c33352a3; ets=1780991755879; webBuild=6.16.0; xsecappid=xhs-pc-web; loadts=1780991755969; a1=19eab6186c8t7wlugnls5d4fy2jqe63n93tk984ot50000409837; webId=f39c423b76810d2ea5d9eca6bd0b12ae; websectiga=10f9a40ba454a07755a08f27ef8194c53637eba4551cf9751c009d9afb564467; sec_poison_id=842b0990-ea7f-40cf-94fc-7707280c1e4e; gid=yjd0DKyj4Sjdyjd0DKyYKqY4SY9WE17kI1M29l9f4ivJVT28AdKqIj88848\n 12: [uvx, \"--from\", \"jobson-xhs-mcp\", \"xhs-mcp\", \"--type\", \"stdio\"],\n/root/book_knowledge/source/自媒体与内容生产/马督工自媒体教程/08-第六讲：不急于反驳.md\n 294: 却对德国用马肉爆出牛肉是耳不见的\n/root/book_knowledge/source/自媒体与内容生产/马督工自媒体教程/04-第二讲：关注人民内部矛盾.md\n 707: 第一个线纹是装门警告的\n 709: 护主报警说附近的村民抢了33吨警告\n 713: 河南人道歉偷警告警察放纵\n 715: 完美抓上了河南人偷警告的东式传说\n 738: 工厢警告的故事线\n 759: 留在河南也不会更喜欢警告\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/6.新质供应链_来自大湾区制造业的启示.md\n 3990: 由于本身具备的产业属性，数字化步伐比中化国际风控总监虞立戎博土与信息技术业遍布中国各地，必须制定整体数字化战\n 4122: | 年建立起一套完整高效的风险与信用管理 | 风控和业务人员对交易风险进行清晰的认 | | 数字化转型已获成果 |\n 4131: | “精细化工行业风控大数据平台”应运而 | 对内对外传播中化国际数字化转型的信息字化转型还在进行中，目前比较显著的成 | | |\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/12.领导力.md\n 1336: 以行政手段开具“警告书”，收到两张“警告书”的员工将会被辞退。面对这一系列\n 1368: 严厉的训斥，甚至“威胁”要开具“警告书”。有时，林经理会亲自督查并处理在她\n 1455: 训骂，重则开具“警告单”。同时还要求主管抽查、部门经理抽查，一旦发现不符合\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/7.战略模拟与行为反思.md\n 6406: 警告，您唯一的选择将是削减支出。如果收到了这则警告，则说明您的公司资本严重不足。\n 6660: 注意：银行将在第六季度初收回总额为1,500,000的现金。并且不会有任何的提醒或警告。\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/13.产业互联网平台化战略-产业重做一遍的方法论.md\n 2538: 入加盟后，贝壳同样以门店组织规则和价值观的输出。德佑有“小红书”，贝壳有“小蓝\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/3.供应链战略与决策分析.md\n 1573: 推荐及社交媒体获取时尚资讯，网络小视频及网红直播带货、小红书等时尚软件\n 1719: 组会受到警告，连续两季度排在最后10%的产品小组会被拆散重组。\n 2542: 是必须做，不做要么死，要么起不来。为什么抖音、小红书会出现，种草行为对\n 4303: 与画像、智能配载、调度、路径优化等应用相比，易流对数据授信和风控的探索意味\n 4313: 迹和单据来进行数据风控。物流企业可以通过运单贷筹集资金，向司机及时支付运费，\n 4575: ### 风控完成\n 9467: ### “风险与风控措施”，或者给出一堆明\n 11086: 如信用机构及信息汇总公司律商联讯（LexisNexis）汤森警告系统。该公司主要向汽车制造商出售其辅助系统，制造\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/9.创新与创业.md\n 3912: 博、小红书、快手、抖音和淘宝直播等新电商涌现，日益分散的渠道格局令品牌商十\n 3920: 小红书等的崛起，及其带来的线上渠道碎片化，大量品牌亦显得“手足无措”，整合\n 3945: 不断加强客服、物流仓储等服务能力。每当新渠道兴起时，如抖音、小红书等，璞康\n 4131: 够根据节气、促销日历等信息推荐电商用图，也可以输出淘宝、抖音、小红书等平台\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/11.财务报告.md\n 5331: 对IPO被中途取消感到意外，并发出警告称，百威英博还需要再过一年时间，才\n/root/book_knowledge/source/商学院案例库/中欧商学院案例合集/1.产业发展与竞争分析.md\n 176: ### 《小红书闷声赚麻了》（媒体报道）\n 181: 1.B站是否应该从小众走向主流？小红书是否应该\"用户泛化”（亦即从小众走向\n 3906: ### 小红书闷声赚麻了\n 3926: 当传统电商平台愈发对双十一战报祛魅时，小红书是为数不多仍在\"秀肌肉\"的平台之一。\n 3928: 11月中旬，小红书发布\"1年1度购物狂欢\"收官战报，三个关键指标似乎宣告着一场\"大捷\"一一单\n 3932: 交出亮眼的电商数据之前，小红书上半年已经跑出不俗的营收势能一一10月中旬，外媒报道称，"}

## tool · 2026-07-03 06:21:04

{"output": "--- /root/.xiaohongshu-cli/search_sessions.json ---\nexists size 165 mtime 1780998482.0998466\n{\n  \"[\\\"净水器\\\", \\\"general\\\", 0]\": {\n    \"search_id\": \"2GH7LXWVY46QR26P8JQDR\",\n    \"created_at\": 1780998482.1059418,\n    \"last_used_at\": 1780998482.1059418\n  }\n}\n\n--- /root/.xiaohongshu-cli/cookies.json ---\nexists size 119 mtime 1780997497.5255554\n\n--- /root/.hermes/xhs-data/cookies.json ---\nexists size 3079 mtime 1780994617.679648\n\n--- /root/social-media/xiaohongshu-mcp/SKILL.md ---\nexists size 8964 mtime 1780997039.6546454\nhas 牛肉\nhas 汕头\nhas rate", "exit_code": 0, "error": null}

## assistant · 2026-07-03 06:21:04

### tool calls

- `tool_slimmer_request_full_tools` `{"reason":"User explicitly asked to check Hindsight records about Xiaohongshu; hindsight_recall is not available in the trimmed tool list."}`
- `session_search` `{"query":"小红书 汕头 牛肉 警告 截断 MCP OR xhs OR xiaohongshu","limit":5,"sort":"newest"}`
- `read_file` `{"path":"/root/social-media/xiaohongshu-mcp/SKILL.md","limit":500}`

## tool · 2026-07-03 06:21:04

{"message": "Full tool schemas requested for the next model call. Retry the original task after the tool list reloads; do not build substitute tools.", "ok": true, "reason": "User explicitly asked to check Hindsight records about Xiaohongshu; hindsight_recall is not available in the trimmed tool list.", "tool_slimmer_full_tools_requested": true}

## tool · 2026-07-03 06:21:04

{"success": true, "mode": "discover", "query": "小红书 汕头 牛肉 警告 截断 MCP OR xhs OR xiaohongshu", "results": [{"session_id": "20260702_001900_7b8d28", "when": "July 01, 2026 at 07:41 PM", "source": "weixin", "model": "gpt-5.5", "title": "Google Drive 授权与文件处理", "matched_role": "assistant", "match_message_id": 45768, "snippet": "展开。\n\n- **BBVA Compass** → 换成 **汽车购买链路 / 小红书种草链路**  \n  证明：不能只看最后成交渠道。\n\ndone:\n- 已验证这个新 skill 能用于其他中欧成稿。\n- 已产出第二个公开案例池文件。\n-", "bookend_start": [{"id": 45760, "role": "user", "content": "还是不太符合预期，从两方面入手，一方面你去找找一些公开的案例库，另一方面你看看马督工skill唯独，如果没有一手资料，退而求其次应该去找哪些，不用卡死在这几个案例\n\n案例虽然是一手信息，但是案例公司不够有知名度，同时深度不够，感觉有点蜻蜓点水。没什么记忆点，作为观众案例作为观点的佐证有点没什么说服力\n\n我在Google drive上传了新的东西：马督工自媒体教程文件夹，你去拉去然后用book to skill处理一下", "timestamp": 1782922811.1123788}], "messages": [{"id": 45765, "role": "user", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Respond ONLY to the latest user message that appears AFTER this summary — that message is the single source of truth for what to do right now. Topic overlap with the summary does NOT mean you should resume its task: even on similar topics, the latest user message WINS. Treat ONLY the latest message as the active task and discard stale items from '## Historical Task Snapshot' / '## Historical In-Progress State' / '## Historical Pending User Asks' / '## Historical Remaining Work' entirely — do not 'wrap up' or 'finish' work described there unless the latest message explicitly asks for it. Reverse signals in the latest message (e.g. 'stop', 'undo', 'roll back', 'just verify', 'don't do that anymore', 'never mind', a new topic) must immediately end any in-flight work described in the summary; do not re-surface it in later turns. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Historical Task Snapshot\n用户最近未完成请求，原话：\n“你去深化一下，我看看你改完之后是什么样子”\n\n## Goal\n用户要把“中欧商学院自媒体稿”的案例替换流程做成一条稳定、可复用的公开信息源头链路：以后遇到上游稿件，优先从公开案例池中替换弱/内/版权风险案例，并希望我继续保持这个方向；同时还要对另一篇已处理过的中欧成稿再做一次“深化”。\n\n## Constraints & Preferences\n- 对外公开版必须低版权风险，优先公开一手来源、公司公告、SEC/年报/公开访谈、可检索公开报道\n- 不再回读中欧案例原文；后续测试要求只读取“已经由自媒体 skill 处理过的成品稿件”\n- 用户明确要求：要记住案例池位置、以及“怎么找到上游这个案例库”的方法，未来可包装成 skill\n- 用户偏好：案例要有记忆点、能复述、能形成“proof job”\n- 先做公开可查性审计，再决定是否保留原稿案例或替换\n- 绝不保留任何凭证、密钥等敏感信息\n\n## Completed Actions\n1. READ `madugong-self-media` skill chapters 11/12 — 确认搜索应优先公开资料、抓源头/时间线/特殊名词，避免复制粘贴式二手信息 [tool: skill_view]\n2. READ `ceibs_ceo_succession_two_versions_comparison.md` — 评估 A/B 两版 CEO 继任稿的结构差异与可发性 [tool: read_file]\n3. SEARCHED local files for CEO/继任/接班相关成品稿 — 找到已有成品稿路径，包括 `/root/ceibs_ceo_succession_v3_selfmedia.md`、`/root/ceibs_ceo_succession_selfmedia.md`、`/root/ceibs_ceo_succession_two_versions_comparison.md` [tool: search_files]\n4. READ `/root/ceibs_ceo_succession_selfmedia.md` — 识别原稿中使用的案例：耐克、拉卡德皮革、瑞安、思科等 [tool: read_file]\n5. BUILT公开案例版 CEO 继任稿 `/root/ceo_succession_public_cases_madugong_version.md` — 以 Apple、Microsoft、Disney、Starbucks 等公开公司公告/SEC 披露替换内部案例，并反复改写增强记忆点 [tool: write_file]\n6. VERIFIED公开案例版文件状态 — 文件存在，大小约 14–16 KB、约 267–315 行；已确认内部案例关键词（拉卡德/耐克/瑞安/中欧商学院案例）不再出现在正文中 [tool: terminal]\n7. READ `/root/.hermes/skills/book-derived/decode-od/chapters/ch19.md`、`ch18.md`、`ch20.md`、`ch21.md` — 提取阿里巴巴、华为、京东、腾讯的 OD/治理机制摘要，作为可对照的公开机制材料 [tool: read_file]\n8. CREATED公开版 CEO 继任稿的“机制对照”增强版 — 将公开公司案例与公开管理知识对照，强化“交出头衔不等于交出权力”“空降高管不是能力迁移，而是合法性重建”等判断 [tool: write_file]\n9. VERIFIED增强版文件存在 — `/root/ceo_succession_public_cases_madugong_version.md`，约 16.6 KB、7280 字、315 行；确认包含 Apple/Microsoft/Disney/Starbucks/SEC/8-K/Technology Advisor/双汇报线/barista 等关键公开元素，且不含内部案例关键词 [tool: terminal]\n10. READ并输出公开案例可查性审计文档 `/root/ceo_succession_original_case_public_audit.md` — 对原稿案例逐项判断公开可查性与风险等级 [tool: write_file]\n11. READ并核验原稿 PDF 文本（已转成 txt）`/root/ceo_succession_case_public_audit/original_a_version.txt` — 抽取原稿 A 版的案例片段与框架（含拉卡德、耐克、美的、四种考验、18 个月窗口等） [tool: terminal]\n12. FOUND Nike 原始公开来源 — SEC 8-K 明确记载 2004-11-18 任命 William D. Perez 为 President and CEO，Philip Knight 保留 Chairman and active role；2006-01-23 8-K/新闻稿写明 Perez resign、Parker 接任，且公开解释与 Knight 的 leadership differences [tool: terminal]\n13. FOUND Apple 原始公开来源 — SEC 8-K/Apple 8-K 2011-08-24 记载 Steve Jobs 辞任 CEO、Tim Cook 接任 CEO、Jobs 转任董事会主席、Cook 加入董事会 [tool: terminal]\n14. FOUND Microsoft 原始公开来源 — SEC 8-K/Exhibit 99.1 2014-02-04 记载 Satya Nadella 任 CEO、Bill Gates 转为 Technology Advisor、John Thompson 任董事长 [tool: terminal]\n15. FOUND Disney 原始公开来源 — SEC 8-K/董事会新闻稿 2020-02-24/2022-11-21 记载 Bob Iger→Executive Chairman、Bob Chapek→CEO，2022 Iger 回任 CEO [tool: terminal]\n16. FOUND Starbucks 原始公开来源 — SEC 8-K/press release 2022-09-01 与 2023-03-20 记载 Laxman Narasimhan 先作为 incoming CEO 过渡、后正式担任 CEO，并在过渡期学习门店/工厂/咖啡农场/支援中心并考取 barista 认证 [tool: terminal]\n17. FOUND公开搜索线索证明“美的/方太/李锦记”可作为中文公开案例池核心 — 通过百度搜索摘要见到美的“外姓人”方洪波接班、方太“茅理翔/茅忠群”、李锦记“家族宪法/家族会议/双重治理结构”等公开线索 [tool: terminal]\n18. SUMMARIZED公开案例池文件 `/root/ceo_succession_source_chain_case_pool.md` — 明确把案例池分成“职业经理人接班线”和“家族企业传承线”，并建议主案例优先用美的、方太、李锦记、耐克 2004 [tool: write_file]\n19. CREATED skill `public-case-source-replacement` — 新 skill 用于后续把弱/内/版权风险案例替换为公开低风险高认知案例，且在 skill 中记住了案例池路径 `/root/ceo_succession_source_chain_case_pool.md` [tool: skill_manage]\n20. CREATED《营销管理》成品稿公开案例替换池 `/root/marketing_public_case_replacement_pool.md` — 只读取已由自媒体 skill 处理过的成品稿，不回读案例原文；识别原稿案例：极橙儿童齿科、Xiameter/道康宁、厚朴方舟、合生元、BBVA Compass，并给出替换方向 [tool: write_file]\n\n## Active State\n- 工作目录：`/root`\n- 当前重点文件：\n  - `/root/ceo_succession_public_cases_madugong_version.md`（公开案例版 CEO 继任稿，已强化过）\n  - `/root/ceo_succession_original_case_public_audit.md`（原稿案例公开可查性审计）\n  - `/root/ceo_succession_source_chain_case_pool.md`（公开案例池/上游源头链路）\n  - `/root/ceo_succession_public_case_source_expansion.md`（从美的/方太顺藤摸瓜扩展出来的案例池说明）\n  - `/root/marketing_public_case_replacement_pool.md`（营销管理成品稿的公开替换池）\n  - `/root/.hermes/skills/writing-skills/public-case-source-replacement/SKILL.md`（新建 skill）\n- 状态：已完成“公开案例池 + 可查性审计 + skill 化”闭环，准备继续处理下一篇已由自媒体 skill 产出的成品稿\n- 当前未在跑的后台任务/服务：无明确新增后台进程记录\n- 尚未进行的下一步：对用户要求“随机选一篇已处理过的中欧成稿（不读原案例）”继续用新 skill 深化改写\n\n## Historical In-Progress State\n正在把“公开案例替换”从单篇改稿升级为可复用工作流：\n1. 先审计原稿案例是否公开可查；\n2. 再建立上游公开案例池；\n3. 最后把该池包装成 skill，供未来所有类似稿件复用；\n4. 同时用户要求对另一篇已处理过的成稿继续“深化”，不回读原案例原文。\n\n## Blocked\n- 无硬阻塞\n- 主要限制：没有 web_search/browser 这类原生工具时，只能借助 SEC/公开网页/百度搜索摘要/本地检索来做公开可查性判断；但已足以完成当前任务\n- 个别更细的学术/媒体来源需后续再补强，但不影响当前公开替换方向\n\n## Key Decisions\n1. **不再碰内部中欧案例作为对外证据主体**：对外发布时只保留公开可查素材，降低版权风险\n2. **案例要承担单一 proof job**：每个案例只讲一个核心判断，避免“案例堆砌”\n3. **公开案例优先级**：SEC/公司公告 > 公司新闻稿 > 权威媒体/公开访谈 > 百科线索\n4. **把“案例池”当作长期资产**：从美的、方太这类中文公开案例向上游扩展，比每次临时找几个知名公司更稳定\n5. **skill 化长期记忆**：将“公开案例替换”写成 skill `public-case-source-replacement`，并记住案例池路径，方便后续上游稿件直接处理\n6. **原稿审计结论**：耐克、美的、方太可公开核查；拉卡德皮革无法查到可靠公开来源，不建议实名对外使用\n7. **成品稿处理原则**：只读取已由自媒体 skill 处理过的成品稿件，不回读中欧案例原文，符合用户要求\n\n## Resolved Questions\n1. “把md发给我看看你怎么调整的” — 已通过生成并核验 `/root/ceo_succession_public_cases_madugong_version.md` 回复；文件已用公开案例重写\n2. “案例再深度一下，感觉没啥记忆点” — 已通过增强版公开案例稿回应；加入更强的冲突切片、记忆点语句和机制对照\n3. “你这样，换种思路，关于这个话题，你检查一下已经转化的book skill，你看看哪本书里面有没有相对应的案例可以抄” — 已检查 book-derived skills，结论是 `ceibs-case-collection` 有原始 CEO 继任内部案例，但不适合对外；`decode-od` 只有机制层材料可做参照\n4. “我要的是可对外公开版，内部的不用考虑版权问题了。主要是对外就会有版权风险。你帮我想想方法” — 已回答：用公开一手材料 + 公开机制材料对照，避免内部案例\n5. “好了吗？发md文件给我” — 已生成更新版 `/root/ceo_succession_public_cases_madugong_version.md`\n6. “你这样吧，你查一下原稿那些案例，是否是公开信息能查到的，如果能查到那就比较低风险了” — 已完成原稿案例公开可查性审计，输出 `/root/ceo_succession_original_case_public_audit.md`\n7. “像美的，方太这种公开资料，能不能顺藤摸瓜找上去，看看信息方有没有更多案例” — 已找到并整理公开案例池 `/root/ceo_succession_source_chain_case_pool.md`，并进一步扩成 `public-case-source-replacement` skill\n8. “非常好，这个案例池在哪里，是公开信息吗

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## tool · 2026-07-03 06:21:04

{"content": "1|---\n2|name: xiaohongshu-mcp\n3|description: \"Connect Xiaohongshu (小红书) to Hermes via MCP or direct REST API — search notes, get content, publish, read comments. Covers the hybrid auth pattern: Playwright login → cookie extraction → HTTP API calls.\"\n4|version: \"1.0\"\n5|---\n6|\n7|## Xiaohongshu MCP — Hybrid Auth Pattern\n8|\n9|Connect your Hermes Agent to Xiaohongshu data. The recommended server is `xiaohongshu-mcp` by xpzouying (14k⭐, Go-based, HTTP Streamable MCP).\n10|\n11|### Recommended MCP Server\n12|\n13|- **Project**: `xpzouying/xiaohongshu-mcp` (GitHub 14k⭐)\n14|- **Language**: Go (single binary, no Python/Node dependency)\n15|- **Protocol**: Streamable HTTP MCP on port 18060\n16|- **Browser**: Bundles Chromium via Rod (Go's Playwright)\n17|\n18|### Installation\n19|\n20|```bash\n21|# 1. Download latest Linux amd64 binary\n22|curl -sL \"https://api.github.com/repos/xpzouying/xiaohongshu-mcp/releases/latest\" | \\\n23| python3 -c \"import sys,json; [print(a['browser_download_url']) for a in json.load(sys.stdin)['assets'] if 'linux-amd64' in a['name']]\"\n24|\n25|# 2. Extract and install\n26|mkdir -p ~/.hermes/xhs-data\n27|cd /tmp\n28|curl -sL <url> -o xhs-mcp.tar.gz\n29|tar xzf xhs-mcp.tar.gz\n30|mv xiaohongshu-mcp-linux-amd64 ~/.hermes/bin/xhs-mcp\n31|chmod +x ~/.hermes/bin/xhs-mcp\n32|\n33|# 3. Create data directory for cookies\n34|mkdir -p ~/.hermes/xhs-data\n35|```\n36|\n37|### Authentication\n38|\n39|#### Option A: Login via QR (requires display/Xvfb)\n40|Run with `-headless=false` and a virtual display:\n41|```bash\n42|Xvfb :99 -screen 0 1024x768x24 &\n43|export DISPLAY=:99\n44|cd ~/.hermes/xhs-data && ./xhs-mcp -port :18060 -headless=false\n45|```\n46|Then call `get_login_qrcode` MCP tool and scan with Xiaohongshu app.\n47|\n48|#### Option B: Pre-populate cookies (recommended for VPS)\n49|Convert an existing cookie string to Rod/Playwright JSON format:\n50|```python\n51|import json\n52|cookie_str = \"key1=value1; key2=value2\" # from env or config\n53|cookies = []\n54|for pair in cookie_str.split(\"; \"):\n55| if \"=\" in pair:\n56| name, value = pair.split(\"=\", 1)\n57| cookies.append({\n58| \"name\": name.strip(),\n59| \"value\": value.strip(),\n60| \"domain\": \".xiaohongshu.com\",\n61| \"path\": \"/\",\n62| \"httpOnly\": False,\n63| \"secure\": True,\n64| \"sameSite\": \"Lax\"\n65| })\n66|with open(\"cookies.json\", \"w\") as f:\n67| json.dump(cookies, f, indent=2)\n68|```\n69|\n70|### ⚠️ Key Pitfalls\n71|\n72|1. **Headless login fails on VPS**: QR code callback doesn't reach the headless browser. The Xiaohongshu server needs to push a message back to the browser via WebSocket, which doesn't work through headless Chrome on a remote VPS (no display, potential GFW issues). **Fix**: Use Option B (pre-populate cookies) instead.\n73|\n74|2. **Use existing cookies**: The `jobson-xhs-mcp` cookie from Hermes config or env vars works fine — just convert and save to `cookies.json`. The cookie string must be parsed from HTTP header format into Rod's JSON array format. See `references/cookie-format.md`.\n75|\n76|3. **Search keyword limitation**: Use short keywords without spaces (e.g. `\"汕头牛肉火锅\"` works, `\"汕头牛肉火锅 推荐\"` returns empty). The `filters` parameter in `search_feeds` supports `sort_by`, `note_type`, `publish_time` — use these instead of long query strings.\n77|\n78|4. **Session management**: Streamable HTTP MCP requires the `Mcp-Session-Id` header from the initialize response in ALL subsequent requests. Without it, tools return `\"method tools/call is invalid during session initialization\"`.\n79|\n80|5. **Content extraction — unreliable**: `get_feed_detail` often returns empty note body text or \"feed not found\" because xsec_tokens expire quickly after search results are returned. The search results themselves (note cards with title, likes, comments count, collected count, xsecToken) are the most reliable data source.\n81|\n82|6. **First startup slow**: First run downloads Chromium (~150MB) to `~/.cache/rod/browser/`. Takes 30-60s on first launch. Subsequent launches are instant.\n83|\n84|7. **WeChat media CDN failures**: When sending QR code images via MEDIA: tag fails with CDN upload HTTP 500, serve the image through an existing Caddy static file site instead (e.g. `daily.889977551.xyz/qr.png`).\n85|\n86|8. **Two separate xhs-mcp instances**: The Hermes config spawns a **stdio** instance (Python via `uvx --from jobson-xhs-mcp xhs-mcp`), while the Go binary (`~/.hermes/bin/xhs-mcp -port :18060`) is a separate **HTTP** server. They use different cookie sources and don't share state. If the stdio MCP dies, the HTTP server keeps working.\n87|\n88|9. **Performance**: Searching via the HTTP REST API (`GET /api/v1/feeds/search`) takes ~1s. Batch-calling `POST /api/v1/feeds/detail` is slow (10-15s per note) and most notes return empty. Prefer reading note card data from search results instead.\n89|\n90|10. **Browser tool blocked**: Navigating to `xiaohongshu.com` via Hermes browser tools is blocked (\"IP at risk\"), even with headless Chromium. The xhs-mcp Go binary's internal rod-based browser is the only working path. Do NOT attempt browser_navigate to xiaohongshu.com.\n91|\n92|### Starting the Server\n93|\n94|```bash\n95|cd ~/.hermes/xhs-data && ~/.hermes/bin/xhs-mcp -port :18060 -headless true\n96|```\n97|First run downloads Chromium (~150MB) to `~/.cache/rod/browser/`.\n98|\n99|### MCP Protocol Usage\n100|\n101|```bash\n102|# 1. Initialize (capture session ID)\n103|curl -s http://localhost:18060/mcp -X POST \\\n104| -H \"Content-Type: application/json\" \\\n105| -d '{\"jsonrpc\":\"2.0\",\"method\":\"initialize\",\"params\":{\"protocolVersion\":\"2024-11-05\",\"capabilities\":{},\"clientInfo\":{\"name\":\"hermes\",\"version\":\"1.0\"}},\"id\":1}'\n106|\n107|# 2. Call tools with session ID\n108|SESSION=\"<from-initialize-response-header>\"\n109|curl -s --max-time 30 http://localhost:18060/mcp -X POST \\\n110| -H \"Content-Type: application/json\" \\\n111| -H \"Mcp-Session-Id: $SESSION\" \\\n112| -d '{\"jsonrpc\":\"2.0\",\"method\":\"tools/call\",\"id\":2,\"params\":{\"name\":\"check_login_status\",\"arguments\":{}}}'\n113|```\n114|\n115|### Available MCP Tools\n116|\n117|| Tool | Purpose |\n118||------|---------|\n119|| `check_login_status` | Verify cookies/login state |\n120|| `get_login_qrcode` | Get QR code image (base64 PNG) |\n121|| `search_feeds` | Search notes by keyword |\n122|| `list_feeds` | Homepage feed |\n123|| `get_feed_detail` | Note content + comments |\n124|\n125|### Quick Cookie Injection\n126|\n127|```bash\n128|python3 /root/.hermes/skills/social-media/xiaohongshu-mcp/scripts/inject-cookie.py\n129|```\n130|\n131|This reads `XHS_COOKIE` from your Hermes config and writes `cookies.json` in the correct Rod format. See `references/cookie-format.md` for manual conversion.\n132|\n133|### Systemd Service\n134|\n135|A template is at `templates/xhs-mcp.service`. Install with:\n136|\n137|```bash\n138|cp /root/.hermes/skills/social-media/xiaohongshu-mcp/templates/xhs-mcp.service /etc/systemd/system/\n139|systemctl daemon-reload && systemctl enable xhs-mcp && systemctl start xhs-mcp\n140|```\n141|\n142|Also add a @reboot cron as fallback:\n143|```bash\n144|(crontab -l 2>/dev/null; echo '@reboot /root/.hermes/scripts/start-xhs-mcp.sh') | crontab -\n145|```\n146|\n147|### REST API Usage (Faster Alternative)\n148|\n149|The Go HTTP server on :18060 also exposes REST API endpoints under `/api/v1/`. These are **faster and more reliable** than the MCP protocol for common read operations.\n150|\n151|```bash\n152|# Check health (always works)\n153|curl -s http://localhost:18060/health\n154|\n155|# Search notes (fast, ~1s)\n156|curl -s \"http://localhost:18060/api/v1/feeds/search?keyword=净水器\"\n157|\n158|# Search with filters\n159|curl -s \"http://localhost:18060/api/v1/feeds/search?keyword=美的&sort_by=最多点赞

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## assistant · 2026-07-03 06:21:04

### tool calls

- `patch` `{"mode":"replace","path":"/root/social-media/xiaohongshu-mcp/SKILL.md","old_string":"10. **Browser tool blocked**: Navigating to `xiaohongshu.com` via Hermes browser tools is blocked (\"IP at risk\"), even with headless Chromium. The xhs-mcp Go binary's internal rod-based browser is the only working path. Do NOT attempt browser_navigate to xiaohongshu.com.\n","new_string":"10. **Browser tool blocked**: Navigating to `xiaohongshu.com` via Hermes browser tools is blocked (\"IP at risk\"), even with headless Chromium. The xhs-mcp Go binary's internal rod-based browser is the only working path. Do NOT attempt browser_navigate to xiaohongshu.com.\n\n11. **Account-risk lesson from prior run**: A prior exploratory run around `汕头牛肉火锅` was cut off and the Xiaohongshu account received a platform warning. Treat Xiaohongshu as account-risky, not just IP-risky. Do not run bulk searches/comment scraping from the user's main account on the VPS. Default to low-frequency, human-in-the-loop sampling, se

...[truncated 1340 chars]`

## tool · 2026-07-03 06:21:04

{"success": true, "diff": "--- a//root/social-media/xiaohongshu-mcp/SKILL.md\n+++ b//root/social-media/xiaohongshu-mcp/SKILL.md\n@@ -89,6 +89,21 @@\n \n 10. **Browser tool blocked**: Navigating to `xiaohongshu.com` via Hermes browser tools is blocked (\"IP at risk\"), even with headless Chromium. The xhs-mcp Go binary's internal rod-based browser is the only working path. Do NOT attempt browser_navigate to xiaohongshu.com.\n \n+11. **Account-risk lesson from prior run**: A prior exploratory run around `汕头牛肉火锅` was cut off and the Xiaohongshu account received a platform warning. Treat Xiaohongshu as account-risky, not just IP-risky. Do not run bulk searches/comment scraping from the user's main account on the VPS. Default to low-frequency, human-in-the-loop sampling, search-result-only triage, and local/residential execution for any substantial research.\n+\n+### Account-Safe Operating Policy\n+\n+For future Xiaohongshu research tasks:\n+\n+1. **No bulk crawling on the VPS by default** — VPS IPs are high-risk and can transfer risk to cookies/account.\n+2. **Do not use the user's main account for automation-heavy collection** — use manual sampling, a dedicated low-value research account, or local device flow.\n+3. **Keep queries short and sparse** — avoid long keyword chains and rapid variations. Use one compact query, inspect results, then decide the next query.\n+4. **Prefer search-card triage before details** — first collect titles, author names, likes/comments counts, and URLs; only open details/comments for a small handpicked set.\n+5. **Throttle aggressively** — minutes between detail/comment fetches, random delay, hard cap per session, and stop immediately on captcha/warning/abnormal login提示.\n+6. **Cache every result** — never repeat the same search/detail call if a local cache exists.\n+7. **If comments are the target** — sample 3–5 high-signal notes first; do not batch all note comments.\n+8. **If the task is important** — run from a local browser/residential network and let Hermes analyze exported/cached HTML/JSON, rather than making Hermes perform the risky collection from the VPS.\n+\n ### Starting the Server\n \n ```bash\n", "files_modified": ["/root/social-media/xiaohongshu-mcp/SKILL.md"], "lint": {"status": "skipped", "message": "No linter for .md files"}, "resolved_path": "/root/social-media/xiaohongshu-mcp/SKILL.md"}

## assistant · 2026-07-03 06:21:04

Boss，我查了本地记录和 session 线索：确实有 **6月9日“小红书/高德 MCP 接入 + 汕头牛肉火锅”** 这条历史；本地 skill 里也已经写着几个关键教训：VPS 浏览器访问小红书会触发 **IP at risk**，`get_feed_detail` 不稳定，搜索词太长容易空结果。

但这次你补充的核心信息更关键：**账号收到警告**。这说明问题不是“技术上能不能抓”，而是**账号风控风险已经被触发**。所以下次我会改策略：

1. **不再用主账号 + VPS 做批量搜索/评论抓取**
   - VPS IP 风险会传导到 cookie/账号。
   - 小红书这类平台不能按普通网页爬虫处理。

2. **先做低频搜索卡片，不直接批量进评论**
   - 只取标题、作者、互动数、链接。
   - 人工/半自动挑 3–5 篇高价值笔记，再看评论。
   - 不再一轮跑很多关键词变体。

3. **评论区改为“抽样研究”，不是全量抓取**
   - 每个需求先抓少量高信号评论。
   - 发现验证码、异常登录、警告提示，立即停，不重试。

4. **采集端尽量放在本地/住宅网络**
   - VPS 负责分析，不负责冒风险采集。
   - 最稳方案是：本地浏览器或本地 CLI 导出搜索结果/评论缓存，Hermes 读取 JSON/HTML 后做医生/店铺口碑分析。

5. **主账号只做人工验证，不做自动化**
   - 如果要长期做，应该用低价值研究号或专门采集号。
   - 医院/医生这种高价值决策场景，宁可少量高质量采样，也不要把账号跑废。

我已经把这条教训补进本地 `xiaohongshu-mcp` skill 了：以后默认按“账号安全策略”执行，不再把它当纯 IP 反爬问题。

我的建议：**下次小红书需求，Hermes 只做分析中枢；采集走本地/住宅网络、低频、缓存优先。** 这样最稳。

## user · 2026-07-03 06:25:56

成功经验你才去写skill，失败经验不用了skill了，不过话说应该有说小红书，hindsight应该会自动召回关于小红书的历史记录，自动注入上下文，不应该我再问你才对

## assistant · 2026-07-03 06:29:20

### tool calls

- `skill_view` `{"name":"hindsight-ops"}`
- `read_file` `{"path":"/root/.hermes/CONFIG_OWNERSHIP.md","limit":300}`
- `read_file` `{"path":"/root/.hermes/hindsight/config.json","limit":300}`
- `terminal` `{"command":"/root/.hermes/scripts/hermes_config_doctor.sh 2>&1 | sed -n '1,220p'","timeout":60}`
- `session_search` `{"query":"小红书 汕头 牛肉 MCP 警告 风控 截断","limit":10,"sort":"newest","role_filter":"user,assistant,tool"}`

## tool · 2026-07-03 06:29:20

{"success": true, "name": "hindsight-ops", "description": "Use when Hermes/Hindsight memory feels stale, not getting smarter, observations stay at 0, recall quality is poor, Hindsight backlog/operations are stuck, or the user asks to tune/fix Hindsight memory. Diagnose live stats/config/logs, apply safe observation/consolidation/recall tuning, and verify before claiming success.", "tags": [], "related_skills": [], "content": "---\nname: hindsight-ops\ndescription: \"Use when Hermes/Hindsight memory feels stale, not getting smarter, observations stay at 0, recall quality is poor, Hindsight backlog/operations are stuck, or the user asks to tune/fix Hindsight memory. Diagnose live stats/config/logs, apply safe observation/consolidation/recall tuning, and verify before claiming success.\"\n---\n\n# Hindsight Ops\n\n## Operating Rules\n\n- Do not guess from old memory. Always inspect live files, API stats, Docker state, and recent logs first.\n- Never print API keys. Check env variable names and whether values are present, not the values.\n- Back up before writing `/root/.hermes/hindsight/config.json`, `/root/.hermes/config.yaml`, `/root/docker-compose.yml`, `/root/hindsight-docker.env`, or `/root/.hermes/CONFIG_OWNERSHIP.md`.\n- Use the live Hindsight HTTP API on `http://127.0.0.1:8888`. The correct operations path is `/v1/default/banks/default/operations`; the shorter `/v1/default/operations` returns 404.\n- Report results as `done`, `blocked`, `not verified`, and `next step`. Do not claim Hindsight is fixed until checks pass.\n\n## Fast Diagnosis\n\nRun the local helper first:\n\n```bash\npython3 /root/.hermes/skills/hermes-agent/hindsight-ops/scripts/hindsight_ops.py status --pretty\n```\n\nThen cross-check the runtime:\n\n```bash\nhermes memory status\nhermes fallback list\ndocker compose -f /root/docker-compose.yml ps hindsight\ncurl -fsS http://127.0.0.1:8888/health\ncurl -fsS http://127.0.0.1:8888/v1/default/banks/default/stats | python3 -m json.tool\ncurl -fsS http://127.0.0.1:8888/v1/default/banks/default/config | python3 -m json.tool\ncurl -fsS 'http://127.0.0.1:8888/v1/default/banks/default/operations?limit=10' | python3 -m json.tool\ndocker compose -f /root/docker-compose.yml logs --tail=160 hindsight 2>&1 | grep -E 'CONSOLIDATION|llm_batch|slow llm call|WORKER_TASK|failed|payload_null|PENDING_BREAKDOWN' || true\n```\n\nCheck Hindsight LLM routing without exposing secrets:\n\n```bash\ndocker exec hindsight env 2>/dev/null \\\n | grep -E 'HINDSIGHT_API_(LLM|RETAIN_LLM|REFLECT_LLM|CONSOLIDATION)_(PROVIDER|MODEL|BASE_URL|TIMEOUT|MAX_CONCURRENT)|HINDSIGHT_API_CONSOLIDATION_' \\\n | grep -v 'API_KEY' \\\n | sort\n```\n\nThis machine's current standard is:\n\n- Hermes main route: `custom:sub2api / gpt-5.5 / http://127.0.0.1:8080/v1`.\n- Automatic fallback chain: `custom:sub2api / gpt-5.4-mini`, then `stepfun / step-3.7-flash`, then `custom:mistral / mistral-small-latest`.\n- Hindsight Docker LLM route: OpenAI-compatible StepFun `step-3.7-flash` at `https://api.stepfun.com/step_plan/v1`, keys in `/root/hindsight-docker.env`.\n- Hermes-side Hindsight recall mode after successful backfill: `recall_types=[\"observation\"]`.\n\n## Interpretation\n\n`total_observations=0` after meaningful use is not a \"wait longer\" problem. Check bank config:\n\n- If `enable_observations=false`, observations can never grow.\n- If `enable_auto_consolidation=false`, new experiences will not be turned into observations automatically.\n- Fix both, set observation-focused missions, then trigger or wait for consolidation.\n\n`pending_consolidation>0` and `failed_consolidation=0` means backfill is still running. Estimate remaining time from logs:\n\n- Hindsight usually processes `consolidation_max_memories_per_round` memories per operation.\n- On this VPS with StepFun, observed rounds ranged from about 6 to 13 minutes per 30 memories, with occasional slow LLM calls.\n- Keep transition recall `[\"observation\",\"experience\"]` until `pending_consolidation=0`.\n\n`failed_consolidation>0` or `failed_operations` rising means inspect recent operations and logs before changing recall policy.\n\n`processing` operations whose `task_type` is `refresh_mental_model` do not block switching to observation-only if `pending_consolidation=0`, `failed_consolidation=0`, and observations exist.\n\nA Hermes smoke reply saying Tool Slimmer hid a Hindsight recall tool is not proof that memory is broken. Check recall/tool logs before treating it as a Hindsight failure.\n\n## Backup and Recovery Safety\n\nFor routine VPS backups, do not stop or restart Hindsight. Hindsight's embedded `pg0` storage is PostgreSQL; prefer an online dump using the container's own PG tools, then include the raw bind mount as fallback in encrypted full-state backup.\n\nCurrent safe dump pattern:\n\n```bash\ndocker exec hindsight sh -lc '\n set -Eeuo pipefail\n PW=$(python3 - <<\"PY\"\nimport json\nprint(json.load(open(\"/home/hindsight/.pg0/instances/hindsight/instance.json\"))[\"password\"])\nPY\n)\n export PGPASSWORD=\"$PW\"\n /home/hindsight/.pg0/installation/18.1.0/bin/pg_dump \\\n -h 127.0.0.1 -p 5432 -U hindsight -d hindsight -F c \\\n -f /tmp/hindsight.dump\n /home/hindsight/.pg0/installation/18.1.0/bin/pg_restore --list \\\n /tmp/hindsight.dump >/tmp/hindsight.dump.list\n'\n```\n\nThen `docker cp` `/tmp/hindsight.dump` and `/tmp/hindsight.dump.list` out, verify they are non-empty, and back up `/root/hindsight-data`, `/root/docker-compose.yml`, `/root/hindsight-docker.env`, and `/root/.hermes/hindsight/config.json` as fallback/config state. Do not make raw `/root/hindsight-data` the only backup while the database is live.\n\n## Safe Automatic Fixes\n\nDry run:\n\n```bash\npython3 /root/.hermes/skills/hermes-agent/hindsight-ops/scripts/hindsight_ops.py fix-basic\npython3 /root/.hermes/skills/hermes-agent/hindsight-ops/scripts/hindsight_ops.py switch-observation-only\n```\n\nApply the standard observation/consolidation tuning:\n\n```bash\npython3 /root/.hermes/skills/hermes-agent/hindsight-ops/scripts/hindsight_ops.py fix-basic --apply\n```\n\nThis backs up current files, patches bank config via `PATCH /v1/default/banks/default/config` using the required `{\"updates\": {...}}` wrapper, and sets Hermes-side recall to transition mode while backfill is incomplete.\n\nTrigger consolidation after fixing config:\n\n```bash\npython3 /root/.hermes/skills/hermes-agent/hindsight-ops/scripts/hindsight_ops.py trigger-consolidation --apply\n```\n\nSwitch to official observation-only recall only after backfill is complete:\n\n```bash\npython3 /root/.hermes/skills/hermes-agent/hindsight-ops/scripts/hindsight_ops.py switch-observation-only --apply --min-observations 5\n```\n\nThe switch command must refuse to apply unless:\n\n- `pending_consolidation == 0`\n- `failed_consolidation == 0`\n- `total_observations >= --min-observations`\n\n## Manual Bank Config Patch\n\nIf the helper is unavailable, patch the bank config directly:\n\n```bash\ncurl -fsS -X PATCH 'http://127.0.0.1:8888/v1/default/banks/default/config' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"updates\": {\n \"enable_observations\": true,\n \"enable_auto_consolidation\": true,\n \"retain_extraction_mode\": \"concise\",\n \"retain_mission\": \"Extract durable task state first: current goal, stage boundary, completed steps, blocked steps, pending steps, exact files/services/accounts/commands, decisions, user corrections, and whether an issue was resolved. Also extract stable preferences and personal context. Preserve before/after status so future sessions do not reopen completed work.\",\n \"observations_mission\": \"Synthesize long-lived Hermes project/task state into concise observations: current goals, completed/blocked/pending work, decisions, user corrections, canonical paths/configs/services, stable preferences, and latest verified evidence. Prefer newer concrete evidence and avoid reopening completed work.\

...[truncated 3409 chars]

## tool · 2026-07-03 06:29:20

{"content": "1|# Hermes VPS Configuration Ownership\n2|\n3|Last verified: 2026-06-24 CST\n4|\n5|This file is the operational source map for Hermes on this VPS. Current facts must still be checked against the live files and commands below before changing services.\n6|\n7|## Canonical Roots\n8|\n9|| Area | Canonical source | Notes |\n10|| --- | --- | --- |\n11|| Hermes code | `/usr/local/lib/hermes-agent` | Upstream checkout with local carry patches. Back up before `hermes update`. |\n12|| Hermes home | `/root/.hermes` | Runtime config, memories, logs, scripts, auth state. |\n13|| Hermes config | `/root/.hermes/config.yaml` | Main model, gateway, memory provider, tools, platform config. |\n14|| Hermes env/secrets | `/root/.hermes/.env` | Env-var backed keys only. Do not send secrets through WeChat when SSH is available. |\n15|| Hermes auth pool | `/root/.hermes/auth.json` via `hermes auth list` | Manual credentials and env-backed credentials. Check presence before asking the user for a key. |\n16|| WeChat inbound file archive | `/root/weixin_file_archive` | Long-term archive for files/media the user sends through WeChat. Do not rely on `/root/.hermes/cache/documents`; it is short-lived cache only. Index: `/root/weixin_file_archive/index.jsonl`. |\n17|| Built-in memory | `/root/.hermes/memories/MEMORY.md`, `/root/.hermes/memories/USER.md`, `/root/.hermes/SOUL.md` | Small hard rules, user preferences, current operating contract. |\n18|| Hindsight client config | `/root/.hermes/hindsight/config.json` | Hermes-side client/provider config: API URL, recall/retain behavior, memory mode. |\n19|| Hindsight service deployment | `/root/docker-compose.yml` | Docker service image, ports, memory limits, LLM model/base URL/runtime env. |\n20|| Hindsight service secrets | `/root/hindsight-docker.env` | Hindsight LLM API keys only. Values are secret and must not be printed. |\n21|| Hindsight database | `/root/hindsight-data` bind-mounted to `/home/hindsight/.pg0` | Durable PostgreSQL data. Do not delete to fix config issues. |\n22|\n23|## Retired Or Non-Canonical Paths\n24|\n25|| Path | Status |\n26|| --- | --- |\n27|| `/root/.hindsight/profiles/hermes.env` | Legacy local-daemon profile. Not used by the active Docker deployment. |\n28|| `/home/hindsight/.hindsight/profiles/*.env` inside container | Not the active config source for this deployment. Do not edit it. |\n29|| `custom:newapi` | Retired provider. It should not appear in config or active auth pool. |\n30|\n31|## Hindsight Edit Rules\n32|\n33|1. Hermes recall/retain behavior: edit `/root/.hermes/hindsight/config.json`, then restart Hermes gateway if needed.\n34|2. Hindsight Docker LLM routing/model/base URL: edit `/root/docker-compose.yml`.\n35|3. Hindsight Docker LLM keys: edit `/root/hindsight-docker.env` from SSH/terminal, not WeChat.\n36|4. After Docker env/config changes, use `cd /root && docker compose up -d --force-recreate hindsight`.\n37|5. A plain `docker compose restart hindsight` does not reload changed env values reliably enough for this setup.\n38|6. Never treat container-local profile files or `/root/.hindsight/profiles/hermes.env` as authoritative.\n39|\n40|## Current Model Routing\n41|\n42|Last routing update: 2026-06-24 CST\n43|\n44|- Canonical main Hermes model: `gpt-5.5` via `custom:sub2api`.\n45|- Canonical main endpoint: `http://127.0.0.1:8080/v1`, configured under `providers.sub2api`.\n46|- Main API key env name: `SUB2API_API_KEY` in `/root/.hermes/.env`; do not store the value in config, handoffs, logs, or chat.\n47|- API mode: `chat_completions`.\n48|- First automatic fallback: `custom:sub2api / gpt-5.4-mini`.\n49|- Later automatic fallbacks: `stepfun / step-3.7-flash` at `https://api.stepfun.com/step_plan/v1`, then `custom:mistral / mistral-small-latest`. `cliproxyapi` was removed from Hermes active config on 2026-06-24 because it is no longer used.\n50|- Do not put free OpenRouter/NVIDIA/Nous models back into the automatic critical fallback chain unless the user explicitly accepts instability; keep them as manual aliases only.\n51|- Fast/cost-saving auxiliary route: lightweight text auxiliary tasks, compression, curator, and delegation use `custom:sub2api / gpt-5.4-mini` first, with StepFun as a stable fallback.\n52|- Hindsight Docker LLM route: `custom:sub2api / gpt-5.4-mini` via OpenAI-compatible base URL `http://172.17.0.1:8080/v1`; Hindsight keys are stored in `/root/hindsight-docker.env` and sourced from the local Sub2API key.\n53|- Hindsight observation root cause fixed on 2026-06-24: the bank had `enable_observations=false` and `enable_auto_consolidation=false`, so observations stayed at 0 no matter how long Hermes used Hindsight. Both are now true.\n54|- Hindsight backfill reached `pending_consolidation=0`, `failed_consolidation=0`, and `total_observations=42` on 2026-06-24. Hermes Hindsight recall was then switched from transition mode `observation+experience` to official observation-only recall.\n55|\n56|## Memory Division\n57|\n58|Keep both built-in memory and Hindsight active.\n59|\n60|- Built-in memory is the hard layer: durable user preferences, strict output contracts, configuration ownership, safety rules, and facts that must always be in the system prompt.\n61|- Hindsight is the recall layer: long-tail task state, past decisions, project context, and searchable experience. It is a clue source, not proof of live configuration.\n62|- Live config, service state, credentials, ports, domains, billing, queues, and logs must be verified from commands/files/API before acting.\n63|- Avoid enabling multiple external memory providers at once. Current external provider is `hindsight`; other providers are available but not active.\n64|\n65|## Secret Handling\n66|\n67|- Before asking the user for an API key, check `hermes config check`, `hermes auth list`, and env key names in `/root/.hermes/.env` or `/root/hindsight-docker.env`.\n68|- Report only present/missing status or key names, never values.\n69|- Prefer SSH/terminal secret entry with `/root/.hermes/scripts/set-hermes-secret.sh`.\n70|- If a key was sent through WeChat and may be truncated, do not keep retrying decryption in chat. Ask for SSH/terminal entry instead.\n71|\n72|## Doctor Command\n73|\n74|Run:\n75|\n76|```bash\n77|/root/.hermes/scripts/hermes_config_doctor.sh\n78|```\n79|\n80|The doctor prints canonical paths, active provider summaries, Hindsight Docker state, memory status, and retired-path warnings without printing secret values.\n81|", "total_lines": 80, "file_size": 6168, "truncated": false, "is_binary": false, "is_image": false}

## tool · 2026-07-03 06:29:20

{"content": "1|{\n2|  \"_note\": \"Hermes-side Hindsight client config. Docker service/runtime config lives in /root/docker-compose.yml and /root/hindsight-docker.env. Do not edit container-local ~/.hindsight profiles for this deployment.\",\n3|  \"mode\": \"local_external\",\n4|  \"api_url\": \"http://localhost:8888\",\n5|  \"bank_id\": \"default\",\n6|  \"recall_budget\": \"mid\",\n7|  \"auto_recall\": true,\n8|  \"auto_retain\": true,\n9|  \"retain_every_n_turns\": 1,\n10|  \"recall_types\": [\n11|    \"observation\"\n12|  ],\n13|  \"timeout\": 60,\n14|  \"idle_timeout\": 300,\n15|  \"recall_max_tokens\": 2200,\n16|  \"recall_max_input_chars\": 1600,\n17|  \"memory_mode\": \"hybrid\",\n18|  \"recall_prefetch_method\": \"recall\",\n19|  \"retain_context\": \"Long-running Hermes Agent conversations with the user. Prioritize current goal, stage boundary, completed steps, blocked steps, pending steps, files, services, accounts, commands, user corrections, and next action.\",\n20|  \"bank_retain_mission\": \"Extract durable task state first: current goal, stage boundary, completed steps, blocked steps, pending steps, exact files/services/accounts/commands, decisions, user corrections, and whether an issue was resolved. Also extract stable preferences and personal context. Preserve before/after status so future sessions do not reopen completed work.\",\n21|  \"bank_mission\": \"Recall authoritative task/status memory for Hermes. Prioritize current project state, completed/blocked/pending items, decisions, exact technical facts, and user constraints before general preferences.\",\n22|  \"recall_prompt_preamble\": \"# Hindsight Memory (persistent task/status context)\\nObservation entries are consolidated, deduplicated task memory and should be read first. Experience entries are raw supporting evidence while observations backfill. If memory conflicts with the current user message or live files/logs/config, verify live evidence before acting.\",\n23|  \"retain_tags\": [\n24|    \"source:hermes\",\n25|    \"channel:gateway\",\n26|    \"kind:task-state\"\n27|  ],\n28|  \"retain_async\": true,\n29|  \"recall_preamble\": \"Prioritize consolidated Hindsight observations: deduplicated, evidence-grounded lessons and stable user/project preferences. Use raw memories only through Hindsight consolidation, not direct recall.\"\n30|}\n31|", "total_lines": 30, "file_size": 2158, "truncated": false, "is_binary": false, "is_image": false}

## tool · 2026-07-03 06:29:20

{"output": "===== Hermes =====\nnow=2026-07-03 06:26:16 CST\nbin=/root/.local/bin/hermes\nHermes Agent v0.17.0 (2026.6.19) · upstream 4d5d9fff · local 2ba1cfeb (+1 carried commit)\nProject: /usr/local/lib/hermes-agent\nPython: 3.11.2\nOpenAI SDK: 2.24.0\n📋 Configuration Status\n Config version: 30 ✓\n Required:\n\n\n===== Canonical paths =====\ndirectory root:root 4096 bytes 2026-06-23 03:25:19.748171900 +0800 /usr/local/lib/hermes-agent\nregular file root:root 19199 bytes 2026-07-03 06:17:52.171674061 +0800 /root/.hermes/config.yaml\nregular file root:root 24681 bytes 2026-06-27 15:33:12.829083600 +0800 /root/.hermes/.env\nregular file root:root 13513 bytes 2026-07-03 06:22:33.621484660 +0800 /root/.hermes/auth.json\nregular file root:root 15953 bytes 2026-06-24 18:46:45.936193455 +0800 /root/.hermes/SOUL.md\nregular file root:root 3074 bytes 2026-06-24 18:46:48.140207824 +0800 /root/.hermes/memories/MEMORY.md\nregular file root:root 6135 bytes 2026-06-24 18:54:06.995068916 +0800 /root/.hermes/memories/USER.md\nregular file root:root 2158 bytes 2026-06-24 22:48:07.539797641 +0800 /root/.hermes/hindsight/config.json\nregular file root:root 1775 bytes 2026-06-25 00:46:00.846712686 +0800 /root/docker-compose.yml\nregular file root:root 297 bytes 2026-06-25 00:46:00.846712686 +0800 /root/hindsight-docker.env\ndirectory UNKNOWN:UNKNOWN 4096 bytes 2026-06-06 22:33:29.754132340 +0800 /root/hindsight-data\nregular file root:root 6168 bytes 2026-06-29 12:18:28.161107058 +0800 /root/.hermes/CONFIG_OWNERSHIP.md\n\n===== Hermes config summary =====\n{\n \"config_version\": 30,\n \"model\": {\n \"default\": \"gpt-5.5\",\n \"provider\": \"custom:sub2api\",\n \"base_url\": \"http://127.0.0.1:8080/v1\",\n \"aliases\": {\n \"pro\": \"deepseek/deepseek-v4-pro\",\n \"flash\": \"deepseek/deepseek-v4-flash\",\n \"free_gptoss\": \"openrouter/openai/gpt-oss-120b:free\",\n \"free_gemma\": \"openrouter/google/gemma-4-31b-it:free\",\n \"paid_flash\": \"deepseek/deepseek-v4-flash\",\n \"paid_pro\": \"deepseek/deepseek-v4-pro\",\n \"free_step\": \"nous/stepfun/step-3.7-flash:free\",\n \"company\": \"custom:sub2api/gpt-5.5\",\n \"company_fast\": \"custom:sub2api/gpt-5.4-mini\",\n \"sub2api\": \"custom:sub2api/gpt-5.5\",\n \"sub2api_fast\": \"custom:sub2api/gpt-5.4-mini\",\n \"free_fast\": \"openrouter/liquid/lfm-2.5-1.2b-instruct:free\",\n \"free_balanced\": \"openrouter/google/gemma-4-31b-it:free\",\n \"free_reasoning\": \"openrouter/openai/gpt-oss-20b:free\",\n \"free_strong\": \"openrouter/openai/gpt-oss-120b:free\",\n \"free_tiny\": \"custom:mistral/ministral-3b-latest\",\n \"free_nvidia\": \"nvidia/nvidia/llama-3.3-nemotron-super-49b-v1.5\",\n \"free_openrouter\": \"openrouter/google/gemma-4-31b-it:free\",\n \"free_mistral\": \"custom:mistral/ministral-8b-latest\",\n \"free_ocr\": \"openrouter/nvidia/nemotron-nano-12b-v2-vl:free\"\n },\n \"api_key\": \"${SUB2...KEY}\",\n \"api_mode\": \"chat_completions\"\n },\n \"memory\": {\n \"memory_enabled\": true,\n \"user_profile_enabled\": true,\n \"write_approval\": false,\n \"memory_char_limit\": 1600,\n \"user_char_limit\": 1100,\n \"provider\": \"hindsight\"\n },\n \"gateway_platforms\": [\n \"weixin\"\n ],\n \"fallback_providers\": [\n {\n \"provider\": \"custom:sub2api\",\n \"model\": \"gpt-5.4-mini\"\n },\n {\n \"provider\": \"stepfun\",\n \"model\": \"step-3.7-flash\",\n \"base_url\": \"https://api.stepfun.com/step_plan/v1\"\n },\n {\n \"provider\": \"custom:mistral\",\n \"model\": \"mistral-small-latest\"\n }\n ]\n}\n\n===== Env key names =====\nfile=/root/.hermes/.env\n AGNES_API_KEY=***\n AMAP_MAPS_API_KEY=***\n BROWSERBASE_ADVANCED_STEALTH=<present>\n BROWSERBASE_PROXIES=<present>\n BROWSER_INACTIVITY_TIMEOUT=<present>\n BROWSER_SESSION_TIMEOUT=<present>\n CLOUDFLARE_ACCOUNT_ID=<present>\n CLOUDFLARE_API_TOKEN=***\n CLOUDFLARE_EMAIL=<present>\n CLOUDFLARE_GLOBAL_API_KEY=***\n CLOUDFLARE_ZONE_ID=<present>\n DEEPSEEK_API_KEY=***\n EXA_API_KEY=***\n FIRECRAWL_API_KEY=***\n GITHUB_TOKEN=***\n GITHUB_TOKEN=***\n GITHUB_TOKEN=***\n GOG_KEYRING_PASSWORD=***\n GOOGLE_API_KEY=***\n GROQ_API_KEY=***\n HERMES_DASHBOARD_BASIC_AUTH_PASSWORD=***\n HERMES_DASHBOARD_BASIC_AUTH_PASSWORD=***\n HERMES_DASHBOARD_BASIC_AUTH_SECRET=***\n HERMES_DASHBOARD_BASIC_AUTH_USERNAME=***\n HERMES_DASHBOARD_BASIC_AUTH_USERNAME=***\n HINDSIGHT_API_KEY=***\n HINDSIGHT_API_LLM_BASE_URL=<present>\n HINDSIGHT_API_URL=<present>\n HINDSIGHT_IDLE_TIMEOUT=<present>\n HINDSIGHT_LLM_API_KEY=***\n HINDSIGHT_LLM_MODEL=<present>\n HINDSIGHT_TIMEOUT=<present>\n IMAGE_TOOLS_DEBUG=<present>\n MEMORY_RETRIEVAL_DEPTH=<present>\n MEMORY_RETRIEVAL_STRATEGY=<present>\n MISTRAL_API_KEY=***\n MOA_TOOLS_DEBUG=<present>\n NVIDIA_API_KEY=***\n OPENCODE_GO_API_KEY=***\n OPENROUTER_API_KEY=***\n SKILL_RETRY_DELAY=<present>\n SKILL_RETRY_ENABLED=<present>\n SKILL_RETRY_MAX=<present>\n SKILL_RETRY_NOTIFICATION=<present>\n SKILLS_AUTO_RELOAD=<present>\n SKILLS_WATCH=<present>\n STEPFUN_API_KEY=***\n STEPFUN_BASE_URL=<present>\n SUB2API_API_KEY=***\n TERMINAL_LIFETIME_SECONDS=<present>\n TERMINAL_MODAL_IMAGE=<present>\n TERMINAL_TIMEOUT=<present>\n TZ=<present>\n VISION_TOOLS_DEBUG=<present>\n WEB_TOOLS_DEBUG=<present>\n WEIXIN_ACCOUNT_ID=<present>\n WEIXIN_ALLOW_ALL_USERS=<present>\n WEIXIN_ALLOWED_USERS=<present>\n WEIXIN_BASE_URL=<present>\n WEIXIN_CDN_BASE_URL=<present>\n WEIXIN_DM_POLICY=<present>\n WEIXIN_GROUP_ALLOWED_USERS=<present>\n WEIXIN_GROUP_POLICY=<present>\n WEIXIN_HOME_CHANNEL=<present>\n WEIXIN_HOME_CHANNEL_THREAD_ID=<present>\n WEIXIN_TOKEN=***\n WXARTICLE_PROXY_AUTHORIZATION_SECRET=***\n WXARTICLE_PROXY_WORKDIR=<present>\nfile=/root/hindsight-docker.env\n HINDSIGHT_API_LLM_API_KEY=***\n HINDSIGHT_API_REFLECT_LLM_API_KEY=***\n HINDSIGHT_API_RETAIN_LLM_API_KEY=***\nRun 'hermes config check' directly for the full optional-key matrix.\n\n===== Auth pool =====\ncopilot (1 credentials):\n #1 GITHUB_TOKEN api_key env:GITHUB_TOKEN ←\n\ncustom:agnes (1 credentials):\n #1 api-key-1 api_key manual ←\n\ndeepseek (1 credentials):\n #1 DEEPSEEK_API_KEY api_key env:DEEPSEEK_API_KEY ←\n\ngemini (1 credentials):\n #1 GOOGLE_API_KEY api_key env:GOOGLE_API_KEY ←\n\nnous (1 credentials):\n #1 device_code oauth device_code ←\n\nnvidia (1 credentials):\n #1 NVIDIA_API_KEY api_key env:NVIDIA_API_KEY ←\n\nopencode-go (1 credentials):\n #1 OPENCODE_GO_API_KEY api_key env:OPENCODE_GO_API_KEY rate-limited GoUsageLimitError (429) (5d 9h left)\n\nopenrouter (1 credentials):\n #1 OPENROUTER_API_KEY api_key env:OPENROUTER_API_KEY ←\n\nstepfun (1 credentials):\n #1 STEPFUN_API_KEY api_key env:STEPFUN_API_KEY ←\n\n\n===== Memory =====\n\nMemory status\n────────────────────────────────────────\n Built-in: always active\n Provider: hindsight\n\n Plugin: installed ✓\n Status: available ✓\n\n Installed plugins:\n • byterover (requires API key)\n • hindsight (API key / local) ← active\n • holographic (local)\n • honcho (API key / local)\n • mem0 (API key / local)\n • openviking (API key / local)\n • retaindb (API key / local)\n • supermemory (requires API key)\n\nhindsight_client={\"mode\": \"local_external\", \"api_url\": \"http://localhost:8888\", \"bank_id\": \"default\", \"recall_budget\": \"mid\", \"auto_recall\": true, \"auto_retain\": true, \"retain_every_n_turns\": 1, \"recall_types\": [\"observation\"], \"memory_mode\": \"hybrid\", \"recall_max_tokens\": 2200, \"recall_max_input_chars\": 1600}\n\n===== Hindsight Docker =====\nNAMES IMAGE STATUS PORTS\nhindsight ghcr.io/vectorize-io/hindsight:latest Up 8 da

...[truncated 346 chars]

## tool · 2026-07-03 06:29:20

{"success": true, "mode": "discover", "query": "小红书 汕头 牛肉 MCP 警告 风控 截断", "results": [{"session_id": "20260703_054723_8a6e2f57", "when": "July 03, 2026 at 05:47 AM", "source": "weixin", "model": "gpt-5.5", "title": "HERMES_AGENT_TIMEOUT功能说明", "matched_role": "tool", "match_message_id": 45934, "snippet": "{\"output\": \"gateway status:\\n● hermes-gateway.service - Hermes Agent Gateway - Messaging Platform Integration\\n Load", "bookend_start": [{"id": 45911, "role": "user", "content": " 提示：HERMES_AGENT_TIMEOUT=0 disables the gateway inactivity kill for a running agent — use for long research run\n\n\n这是啥功能，要启用吗？去社区了解一下", "timestamp": 1783028941.574578}], "messages": [{"id": 45930, "role": "assistant", "content": "Boss，查过了。这不是新功能，更像一个**网关防挂死保护的开关**。\n\n结论：**我不建议现在全局启用 `HERMES_AGENT_TIMEOUT=0`。**\n\n当前你的网关实际是：\n\n```text\nHERMES_AGENT_TIMEOUT=1800\n```\n\n也就是 **30 分钟“无活动”才杀掉运行中的 agent**。注意它不是 30 分钟总时长限制：只要 agent 还在调用工具、收流式响应、推进任务，就可以跑很久；只有卡死、API 挂住、工具无输出时才会触发。\n\n社区/代码里对应说明：\n\n- PR #8298：给 gateway `/background` 任务加 inactivity timeout，避免后台任务静默挂死。\n- 默认 1800 秒，900 秒会先 warning。\n- `HERMES_AGENT_TIMEOUT=0` 表示**无限等待**，适合特别长的研究任务，但风险是：如果模型/API/工具真的卡死，网关不会自动清理这个运行中的 agent。\n- 另有 PR #11772 加了 stream heartbeat，避免长流式响应被误判为无活动。\n\n我的建议：\n\n1. **日常保持现在的 1800 秒。** 这是稳妥值。\n2. 真要跑“长研究任务”，优先用：\n - `/background`\n - cron\n - `terminal(background=True, notify_on_complete=True)`\n 而不是把全局 gateway timeout 关掉。\n3. 如果以后确实经常被误杀，可以把 `agent.gateway_timeout` 提到 3600 或 7200，而不是直接设 0。\n\nTool Slimmer 那句是当前环境提示：工具 schema 可能被瘦身隐藏，缺工具时要显式请求完整工具集。它是省 token 的机制，不是你需要手动启用的业务功能。\n\n当前状态：**无需启用。保持现状即可，Boss。**", "timestamp": 1783029082.0938158}, {"id": 45932, "role": "user", "content": " background目前是可以正常启动的对吗", "timestamp": 1783029275.464202}, {"id": 45933, "role": "assistant", "content": "", "timestamp": 1783029310.741888, "tool_calls": [{"id": "call_CSXBeHu3SFfzW7nj7hLbkFxl", "call_id": "call_CSXBeHu3SFfzW7nj7hLbkFxl", "response_item_id": "fc_CSXBeHu3SFfzW7nj7hLbkFxl", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"set -e\\nprintf 'gateway status:\\\\n'\\nhermes gateway status 2>&1 || true\\nprintf '\\\\nrunning processes:\\\\n'\\npgrep -af 'hermes.*gateway|gateway.*run' || true\\nprintf '\\\\nrecent background/gateway log refs:\\\\n'\\nfor f in /root/.hermes/logs/gateway.log /root/.hermes/logs/agent.log; do\\n [ -f \\\"$f\\\" ] || continue\\n echo \\\"--- $f\\\"\\n grep -iE '/background|background task|HERMES_AGENT_TIMEOUT|inactivity timeout|CancelledError|timeout' \\\"$f\\\" 2>/dev/null | tail -40 || true\\ndone\",\"timeout\":60}"}}, {"id": "call_IQJ3gAIA3GCchYXZt5vS03eT", "call_id": "call_IQJ3gAIA3GCchYXZt5vS03eT", "response_item_id": "fc_IQJ3gAIA3GCchYXZt5vS03eT", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python3 - <<'PY'\\nimport yaml, pathlib, os, json\\np=pathlib.Path('/root/.hermes/config.yaml')\\ndata=yaml.safe_load(p.read_text()) if p.exists() else {}\\nprint(json.dumps({\\n 'config_agent_gateway_timeout': (data or {}).get('agent',{}).get('gateway_timeout'),\\n 'config_agent_gateway_timeout_warning': (data or {}).get('agent',{}).get('gateway_timeout_warning'),\\n 'config_agent_max_turns': (data or {}).get('agent',{}).get('max_turns'),\\n 'env_HERMES_AGENT_TIMEOUT_current_shell': os.environ.get('HERMES_AGENT_TIMEOUT'),\\n}, ensure_ascii=False, indent=2))\\nPY\",\"timeout\":20}"}}]}, {"id": 45934, "role": "tool", "content": "{\"output\": \"gateway status:\\n● hermes-gateway.service - Hermes Agent Gateway - Messaging Platform Integration\\n Loaded: loaded (/root/.config/systemd/user/hermes-gateway.service; enabled; preset: enabled)\\n Active: active (running) since Mon 2026-06-29 12:21:10 CST; 3 days ago\\n Main PID: 2425470 (hermes)\\n Tasks: 66 (limit: 7047)\\n Memory: 3.1G\\n CPU: 1h 9min 42.348s\\n CGroup: /user.slice/user-0.slice/user@0.service/app.slice/hermes-gateway.service\\n ├─ 192012 /usr/bin/bash -c \\\"source /tmp/hermes-snap-66b0228d8388.sh >/dev/null 2>&1 || true\\\\nbuiltin cd -- /root || exit 126\\\\neval 'set -e\\\\nprintf '\\\\\\\\''gateway status:\\\\\\\\n'\\\\\\\\''\\\\nhermes gateway status 2>&1 || true\\\\nprintf '\\\\\\\\''\\\\\\\\nrunning processes:\\\\\\\\n'\\\\\\\\''\\\\npgrep -af '\\\\\\\\''hermes.*gateway|gateway.*run'\\\\\\\\'' || true\\\\nprintf '\\\\\\\\''\\\\\\\\nrecent background/gateway log refs:\\\\\\\\n'\\\\\\\\''\\\\nfor f in /root/.hermes/logs/gateway.log /root/.hermes/logs/agent.log; do\\\\n [ -f \\\\\\\"\\\\$f\\\\\\\" ] || continue\\\\n echo \\\\\\\"--- \\\\$f\\\\\\\"\\\\n grep -iE '\\\\\\\\''/background|background task|HERMES_AGENT_TIMEOUT|inactivity timeout|CancelledError|timeout'\\\\\\\\'' \\\\\\\"\\\\$f\\\\\\\" 2>/dev/null | tail -40 || true\\\\ndone'\\\\n__hermes_ec=\\\\$?\\\\nexport -p > /tmp/hermes-snap-66b0228d8388.sh 2>/dev/null || true\\\\npwd -P > /tmp/hermes-cwd-66b0228d8388.txt 2>/dev/null || true\\\\nprintf '\\\\\\\\n__HERMES_CWD_66b0228d8388__%s__HERMES_CWD_66b0228d8388__\\\\\\\\n' \\\\\\\"\\\\$(pwd -P)\\\\\\\"\\\\nexit \\\\$__hermes_ec\\\"\\n ├─ 192014 /usr/local/lib/hermes-agent/venv/bin/python3 /root/.local/bin/hermes gateway status\\n ├─ 192019 systemctl --user status hermes-gateway --no-pager\\n ├─2425470 /usr/local/lib/hermes-agent/venv/bin/python -m hermes_cli.main gateway run\\n ├─2425476 /root/.local/bin/uv tool uvx amap-mcp-server\\n ├─2425478 /root/.local/bin/uv tool uvx --from jobson-xhs-mcp xhs-mcp\\n ├─2425519 /root/.cache/uv/archive-v0/LPWA6EWrRIreH1NY/bin/python /root/.cache/uv/archive-v0/LPWA6EWrRIreH1NY/bin/amap-mcp-server\\n ├─2425522 /root/.cache/uv/archive-v0/EatXorTLuHpK-0iT/bin/python /root/.cache/uv/archive-v0/EatXorTLuHpK-0iT/bin/xhs-mcp\\n └─3435192 python3 /root/.hermes/temp-oauth-proxy/proxy.py\\n\\nJul 03 05:43:31 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: client_session: <aiohttp.client.ClientSession object at 0x7f0c65770490>\\nJul 03 05:43:31 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: ERROR asyncio: Unclosed connector\\nJul 03 05:43:31 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x7f0c0c7202f0>, 2308090.112860622)])']\\nJul 03 05:43:31 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: connector: <aiohttp.connector.TCPConnector object at 0x7f0c2eeae850>\\nJul 03 05:50:25 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: WARNING agent.tool_executor: Tool terminal returned error (30.11s): {\\\"output\\\": \\\"service env refs:\\\\n\\\\nprocess env refs:\\\\nHERMES_AGENT_TIMEOUT=1800\\\\n\\\\nconfig/env refs:\\\\n\\\\n[Command timed out after 30s]\\\", \\\"exit_code\\\": 124, \\\"error\\\": null}\\nJul 03 05:51:22 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: ERROR asyncio: Unclosed client session\\nJul 03 05:51:22 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: client_session: <aiohttp.client.ClientSession object at 0x7f0c441b9e50>\\nJul 03 05:51:22 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: ERROR asyncio: Unclosed connector\\nJul 03 05:51:22 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x7f0bedc78bb0>, 2308561.350712005)])']\\nJul 03 05:51:22 VM-0562c027-d4bb-4aa7-97ab-d6b12d3013d1 python[2425470]: connector: <aiohttp.connector.TCPConnector object at 0x7f0c0c34ce90>\\n✓ User gateway service is running\\n✓ Systemd linger is enabled (service survives logout)\\n\\nrunning processes:\\n192012 /usr/bin/bash -c source /tmp/hermes-snap-66b0228d8388.sh >/dev/null 2>&1 || true builtin cd -- /root || exit 126 eval 'set -e printf '\\\\''gateway status:\\\\n'\\\\'' hermes gateway status 2>&1 || true printf '\\\\''\\\\nrunning processes:\\\\n'\\\\'' pgrep -af '\\\\''hermes.*gateway|gateway.*run'\\\\'' || true printf '\\\\''\\\\nrecent backgr

...[truncated 84551 chars]

## assistant · 2026-07-03 06:29:20

### tool calls

- `patch` `{"mode":"replace","path":"/root/social-media/xiaohongshu-mcp/SKILL.md","old_string":"\n11. **Account-risk lesson from prior run**: A prior exploratory run around `汕头牛肉火锅` was cut off and the Xiaohongshu account received a platform warning. Treat Xiaohongshu as account-risky, not just IP-risky. Do not run bulk searches/comment scraping from the user's main account on the VPS. Default to low-frequency, human-in-the-loop sampling, search-result-only triage, and local/residential execution for any substantial research.\n\n### Account-Safe Operating Policy\n\nFor future Xiaohongshu research tasks:\n\n1. **No bulk crawling on the VPS by default** — VPS IPs are high-risk and can transfer risk to cookies/account.\n2. **Do not use the user's main account for automation-heavy collection** — use manual sampling, a dedicated low-value research account, or local device flow.\n3. **Keep queries short and sparse** — avoid long keyword chains and rapid variations. Use one compact query, inspect result

...[truncated 790 chars]`
- `terminal` `{"command":"python3 - <<'PY'\nimport json, urllib.request\nbase='http://127.0.0.1:8888'\nqueries=['小红书 汕头 牛肉 MCP 警告 风控 截断','小红书 MCP 汕头牛肉火锅','xiaohongshu xhs 汕头 牛肉']\nfor q in queries:\n print('\\n=== QUERY',q,'===')\n for path, payload in [\n ('/v1/default/banks/default/recall', {'query': q, 'max_tokens': 1200}),\n ('/v1/default/banks/default/reflect', {'query': q, 'max_tokens': 1200}),\n ]:\n req=urllib.request.Request(base+path, data=json.dumps(payload).encode(), headers={'Content-Type':'application/json'})\n try:\n with urllib.request.urlopen(req, timeout=60) as r:\n txt=r.read().decode('utf-8','replace')\n print('---',path,'status ok len',len(txt))\n print(txt[:1500])\n except Exception as e:\n print('---',path,'ERR',repr(e))\nPY","timeout":180}`

## tool · 2026-07-03 06:29:20

{"success": true, "diff": "--- a//root/social-media/xiaohongshu-mcp/SKILL.md\n+++ b//root/social-media/xiaohongshu-mcp/SKILL.md\n@@ -89,21 +89,6 @@\n \n 10. **Browser tool blocked**: Navigating to `xiaohongshu.com` via Hermes browser tools is blocked (\"IP at risk\"), even with headless Chromium. The xhs-mcp Go binary's internal rod-based browser is the only working path. Do NOT attempt browser_navigate to xiaohongshu.com.\n \n-11. **Account-risk lesson from prior run**: A prior exploratory run around `汕头牛肉火锅` was cut off and the Xiaohongshu account received a platform warning. Treat Xiaohongshu as account-risky, not just IP-risky. Do not run bulk searches/comment scraping from the user's main account on the VPS. Default to low-frequency, human-in-the-loop sampling, search-result-only triage, and local/residential execution for any substantial research.\n-\n-### Account-Safe Operating Policy\n-\n-For future Xiaohongshu research tasks:\n-\n-1. **No bulk crawling on the VPS by default** — VPS IPs are high-risk and can transfer risk to cookies/account.\n-2. **Do not use the user's main account for automation-heavy collection** — use manual sampling, a dedicated low-value research account, or local device flow.\n-3. **Keep queries short and sparse** — avoid long keyword chains and rapid variations. Use one compact query, inspect results, then decide the next query.\n-4. **Prefer search-card triage before details** — first collect titles, author names, likes/comments counts, and URLs; only open details/comments for a small handpicked set.\n-5. **Throttle aggressively** — minutes between detail/comment fetches, random delay, hard cap per session, and stop immediately on captcha/warning/abnormal login提示.\n-6. **Cache every result** — never repeat the same search/detail call if a local cache exists.\n-7. **If comments are the target** — sample 3–5 high-signal notes first; do not batch all note comments.\n-8. **If the task is important** — run from a local browser/residential network and let Hermes analyze exported/cached HTML/JSON, rather than making Hermes perform the risky collection from the VPS.\n-\n ### Starting the Server\n \n ```bash\n", "files_modified": ["/root/social-media/xiaohongshu-mcp/SKILL.md"], "lint": {"status": "skipped", "message": "No linter for .md files"}, "resolved_path": "/root/social-media/xiaohongshu-mcp/SKILL.md"}

## tool · 2026-07-03 06:29:20

{"output": "=== QUERY 小红书 汕头 牛肉 MCP 警告 风控 截断 ===\n--- /v1/default/banks/default/recall ERR <HTTPError 404: 'Not Found'>\n--- /v1/default/banks/default/reflect status ok len 623\n{\"text\":\"## 结论\\n\\n这条记录里已经明确了：**小红书 MCP 在搜索“汕头哪个牛肉店好吃”时被中断，且小红书账号收到了警告**。这意味着问题不只是普通的搜索不稳定，而是**账号风控风险已经被触发**。\\n\\n## 我下次会怎么调整\\n\\n- **不再用主账号 + VPS 做批量搜索/评论抓取**\\n- **把小红书采集当成账号安全问题处理，不只当作 IP 反爬**\\n- **先做低频、少量搜索，只拿标题/作者/互动数/链接**\\n- **评论区只做抽样研究，不做全量抓取**\\n- **一旦出现验证码、异常登录、警告提示，立刻停止，不重试**\\n- **采集尽量放在本地或住宅网络，VPS 只做分析**\\n- **主账号只做人工验证，不做自动化**\\n\\n## 依据的已知事实\\n\\n- 之前的小红书 MCP 任务在搜“汕头哪个牛肉店好吃”时被截断。\\n- 同时，小红书账号收到了警告。\\n- 记录里还提到，后续策略已经更新为**“账号安全策略”优先**，不再把问题仅视为 IP 反爬。\\n\\n## 简短建议\\n\\n**以后这类需求：少抓、慢抓、先验证、见警告就停。**\",\"based_on\":null,\"structured_output\":null,\"usage\":{\"input_tokens\":90119,\"output_tokens\":521,\"total_tokens\":90640},\"trace\":null}\n\n=== QUERY 小红书 MCP 汕头牛肉火锅 ===\n--- /v1/default/banks/default/recall ERR <HTTPError 404: 'Not Found'>\n--- /v1/default/banks/default/reflect status ok len 750\n{\"text\":\"## 结论\\n\\n我查到的相关记录里，**“小红书 MCP”** 和 **“汕头牛肉火锅”** 确实有关联，而且后续已经确认过一个更重要的风险点：**账号收到了警告**。\\n\\n### 相关记录要点\\n\\n- 6 月 9 日有一条历史记录：做过 **小红书 / 高德 MCP 接入**，并且演示过 **“汕头杏花吴记牛肉火锅”** 的美食推荐。\\n- 同一批记录里还提到：小红书在 VPS 上可访问但**需要 cookie**，并且可能触发**安全验证**。\\n- 另有记录显示：小红书任务在搜索 **“汕头哪个牛肉店好吃”** 时被中断，且**小红书账号收到了警告**。\\n- 相关技能也已经更新为：**默认按“账号安全策略”执行**，不再只把问题理解为普通的 IP 反爬。\\n\\n### 这意味着什么\\n\\n如果你现在是想继续做 **“小红书 MCP + 汕头牛肉火锅”** 这类任务，当前的核心约束不是“怎么搜得更快”，而是：\\n\\n- **先避免继续触发账号风控**\\n- **不要再把主账号用于高频搜索/互动**\\n- **后续流程要优先考虑账号安全**\\n\\n### 如果你要我继续处理\\n\\n我可以基于这条历史记录，直接帮你整理成一版：\\n\\n- **当前可用策略**\\n- **风险点**\\n- **下一步调整方案**\\n\\n如果你只是想确认“有没有这条记录”，答案是：**有，而且已经确认触发过账号警告。**\",\"based_on\":null,\"structured_output\":null,\"usage\":{\"input_tokens\":82054,\"output_tokens\":606,\"total_tokens\":82660},\"trace\":null}\n\n=== QUERY xiaohongshu xhs 汕头 牛肉 ===\n--- /v1/default/banks/default/recall ERR <HTTPError 404: 'Not Found'>\n--- /v1/default/banks/default/reflect status ok len 772\n{\"text\":\"## 你这句我理解成：想查“小红书 / XHS 里，汕头牛肉”相关的历史记录\\n\\n我查到的相关内容主要有这几条：\\n\\n- **有一条明确的历史记录**：你之前做过一个“小红书/高德 MCP 接入 + 汕头牛肉火锅”的演示，内容涉及美食推荐、评分、旅客实拍照和路线规划。\\n- **还有一条更具体的记录**：你当时在小红书相关流程里，跑到“**汕头哪个牛肉店好吃**”这个方向后，账号收到了警告，后来我们把这归因为 **账号风控风险**，不是单纯技术抓取问题。\\n- **系统里已经形成的后续结论**：以后小红书相关需求要按**账号安全策略**处理，不再把它当成普通反爬问题。\\n\\n## 关键结论\\n\\n如果你现在是想继续做 **XHS / 汕头牛肉** 这类检索或推荐：\\n\\n- **不要再用主账号 + VPS 做批量搜索/评论抓取**\\n- **低频、小范围、先看标题/互动/链接，再人工挑选**\\n- **评论区只做抽样，不做全量**\\n- **采集尽量放在本地/住宅网络，Hermes 只做分析**\\n\\n## 相关历史信号\\n\\n- 你做过“汕头杏花吴记牛肉火锅”的美食推荐演示。\\n- 你后来问过“小红书 mcp 跑了一会汕头哪个牛肉店好吃就被截断了，账号也收到了警告”，这被记录为一个**已知风险案例**。\\n\\n如果你要的是**汕头牛肉店的内容方向/选题词**，或者要我按 **XHS 起号** 的角度整理关键词，我可以直接继续按这个方向给你整理。\",\"based_on\":null,\"structured_output\":null,\"usage\":{\"input_tokens\":79441,\"output_tokens\":608,\"total_tokens\":80049},\"trace\":null}", "exit_code": 0, "error": null}

## assistant · 2026-07-03 06:29:20

对，Boss。你这次纠正是对的。

我刚才不该把失败经验写进 skill。**skill 应该沉淀成功流程、稳定方法、可复用操作；失败教训更适合进 Hindsight / memory，不该污染操作型 skill。**  
我已经把刚才加进 `xiaohongshu-mcp` skill 的那段“账号风险策略”撤掉了。

我也查了 live 配置和 Hindsight：

- Hermes 当前确实启用了 Hindsight：`provider=hindsight`
- `auto_recall=true`
- `recall_types=["observation"]`
- Hindsight 容器 healthy
- 我直接调 Hindsight `reflect` 查“小红书 汕头 牛肉 MCP 警告 风控 截断”，确实能召回这条：
  - 小红书 MCP 搜“汕头哪个牛肉店好吃”被中断
  - 账号收到警告
  - 后续应按账号风控风险处理，而不是普通 IP 反爬

所以问题不是 Hindsight 没记住，而是：**这一轮自动注入没有把这条小红书历史打进当前上下文，或者我没有在第一时间主动查 Hindsight。**

还有一个细节：当前 Hermes 侧是 `recall_types=["observation"]`，召回只走 consolidated observation。它更省 token，但对这种“具体失败现场”可能会丢细节或召回不稳定。要解决有两个方向：

1. **保持 observation-only**  
   省 token，稳定，但我必须在遇到“小红书 / xhs / MCP / 风控”这类关键词时主动查 Hindsight。

2. **改成 observation + experience**  
   更容易召回具体历史细节，但会更耗 token，也可能带入更多噪声。

我的建议：**先不改全局配置。**  
更稳的做法是把“小红书/xhs 属于高风险历史主题，必须主动 Hindsight reflect/recall”作为我的执行习惯，而不是为了一个主题放大所有会话的召回成本。

这次我会记住两条：

- 失败经验不写 skill，除非它已经转化成稳定可复用流程。
- 小红书相关任务，不能等你提醒；一看到“小红书 / xhs / MCP / 风控 / 评论区采集”，我先查 Hindsight 历史。

## user · 2026-07-03 06:33:43

那算了保持 observation-only吧，有些需要的我再主动提醒你去找细节即可

## assistant · 2026-07-03 06:33:56

好的，Boss。保持 `observation-only`。

这个选择稳妥：省 token、噪声低，适合日常微信对话。需要具体历史细节时，你提醒一句“去 Hindsight 找细节”，我再主动查。

我这边按这个规则执行：

- 默认不改 Hindsight 配置。
- 普通任务只用 consolidated observation。
- 涉及具体旧事故、历史操作细节、某次失败原因时，再查更细记录。
- skill 只沉淀成功流程；失败经验优先留在 Hindsight，不随便写进 skill。

当前无需操作，配置保持现状。

## user · 2026-07-03 06:35:01

那小红书这个有没有本地方案呢？比如本地浏览器模拟真人点击浏览？这种会不会稳妥点，虽然慢了点

## assistant · 2026-07-03 06:35:42

### tool calls

- `read_file` `{"path":"/root/browser-data-extraction/references/xiaohongshu-extraction.md","limit":120}`
- `read_file` `{"path":"/root/hermes-agent-configuration/references/xhs-cli-alternatives.md","limit":220}`

## tool · 2026-07-03 06:35:42

{"content": "1|# Xiaohongshu CLI Alternatives\n2|\n3|Two alternative CLI tools exist that bypass the headless browser approach used by xhs-mcp. Both use **reverse-engineered API signatures** to make direct HTTP calls to xiaohongshu's internal API (`edith.xiaohongshu.com`).\n4|\n5|## Comparison\n6|\n7|| Feature | xhs-mcp (xpzouying) | xiaohongshu-cli (jackwener) | redbook (lucasygu) |\n8||---------|---------------------|------------------------------|--------------------|\n9|| ⭐ Stars | 14k | 2.2k | 328 |\n10|| Language | Go (headless browser) | Python (signed API) | TypeScript (signed API) |\n11|| Speed | 5-30s (browser launch) | 0.5-2s (direct API) | 0.5-2s (direct API) |\n12|| Auth | Cookie file / env var | Browser cookie extraction + QR | Browser cookie extraction |\n13|| Anti-bot | Browser fingerprint | Gaussian jitter, captcha cooldown | — |\n14|| Comments | Via feed detail API | `xhs comments --all` | `redbook comments --all` |\n15|| HTTP API | Built-in (port 18060) | No | No |\n16|| License | — | Apache 2.0 | MIT |\n17|| Install | Binary download | `uv tool install xiaohongshu-cli` | `npm install -g @lucasygu/redbook` |\n18|\n19|## xiaohongshu-cli (Recommended)\n20|\n21|### Installation\n22|```bash\n23|uv tool install xiaohongshu-cli\n24|```\n25|\n26|### Cookie Setup\n27|Cookies file: `~/.xiaohongshu-cli/cookies.json` — **dict format** (not array):\n28|```json\n29|{\n30| \"a1\": \"19eab...\",\n31| \"web_session\": \"0400...\"\n32|}\n33|```\n34|\n35|Auto-extract from browser: `xhs login` (detects Chrome/Firefox/Safari etc.)\n36|Or copy from existing xhs-mpc cookies.json → convert to dict.\n37|\n38|### Key Commands\n39|```bash\n40|xhs search \"关键词\" --sort popular # Search notes\n41|xhs read <url> # Read note\n42|xhs comments <url> --all # Get all comments\n43|xhs feed # Home feed\n44|xhs whoami # Check login\n45|```\n46|\n47|### Captcha Issues\n48|- IP triggers captcha (type=216) if flagged by xiaohongshu\n49|- Auto-retries with cooling (5s → 10s → 20s → 30s)\n50|- Solution: clean IP (different VPS / proxy / home network)\n51|- Captcha UUID given — can solve in browser manually for testing\n52|\n53|### Anti-Detection Features\n54|- Gaussian jitter delays (~1-1.5s) mimicking human browsing\n55|- Chrome fingerprint consistency (UA, sec-ch-ua, platform)\n56|- Exponential backoff on HTTP 429/5xx\n57|- Captcha cooldown doubles delay permanently after trigger\n58|- x-s / x-s-common / x-t request signatures\n59|\n60|## redbook\n61|\n62|### Installation\n63|```bash\n64|npm install -g @lucasygu/redbook\n65|```\n66|\n67|### Cookie Setup\n68|Auto-extracts from Chrome. Manual: `--cookie-string \"a1=值; web_session=值\"`\n69|\n70|### Key Commands\n71|```bash\n72|redbook search \"关键词\" --sort popular\n73|redbook read <url>\n74|redbook comments <url> --all\n75|redbook analyze-viral <url> # Viral note analysis\n76|redbook feed\n77|```\n78|\n79|### Bonus Features\n80|- 13 analysis modules (keyword matrix, cross-topic heatmap, opportunity scoring)\n81|- Markdown → XHS-style image card rendering (7 themes)\n82|- Rate-limit detection via creator API hidden level field\n83|\n84|## When to Use Which\n85|\n86|- **From this VPS** (IP flagged): xhs-mcp HTTP API via headless browser (slow but works)\n87|- **From clean IP / local**: xiaohongshu-cli (fastest, most features)\n88|- **Need analysis** (viral patterns, keyword research): redbook\n89|- **MCP-integrated**: xhs-mcp stdio (via Hermes)\n90|\n91|## Cookie Format Conversion\n92|\n93|Between xhs-mcp (Rod JSON array) and xiaohongshu-cli (dict):\n94|```python\n95|# Rod format → dict\n96|rod_cookies = json.load(open('cookies.json'))\n97|cli_cookies = {c['name']: c['value'] for c in rod_cookies}\n98|json.dump(cli_cookies, open('~/.xiaohongshu-cli/cookies.json', 'w'))\n99|```\n100|", "total_lines": 99, "file_size": 3502, "truncated": false, "is_binary": false, "is_image": false}

## assistant · 2026-07-03 06:35:42

有，本地方案反而是我更建议的方向，Boss。

结论：**“本地浏览器模拟真人点击浏览”会比 VPS 稳妥，但不要理解成万能。它降低的是 IP / 设备异常风险，不消除账号行为风险。**

我会把方案分成三层：

## 1. 最稳：本地真人浏览 + Hermes 分析

你在本地小红书正常搜索、打开帖子、看评论；然后把链接、截图、复制出的评论文本或导出的 HTML/JSON 给我。

适合：医院、医生、拔智齿这类高价值决策。  
优点：最像真人，账号风险最低。  
缺点：慢，需要你参与采样。

这其实很适合你的需求，因为你不需要全网爬取，只需要找到“高信号评论”。

## 2. 次稳：本地 Playwright/Chrome 自动慢速浏览

在你本地电脑跑脚本，用真实 Chrome 用户资料：

- 使用真实浏览器 profile
- 不 headless
- 鼠标滚动、停留、随机等待
- 每次只搜 1–2 个关键词
- 每篇只看少量评论
- 结果保存为本地 JSON/Markdown
- Hermes 只读取结果做分析

这比 VPS 好很多，因为网络、设备、登录环境都更接近你本人。

但我会设置很保守：

```text
每轮搜索：1 个关键词
每轮打开：3–5 篇笔记
每篇停留：30–90 秒
评论采样：前 20–50 条
每轮结束：至少间隔数小时
遇到验证码/异常提示：立即停止
```

## 3. 不建议作为主路：VPS + 住宅代理 + 自动化

理论可行，但不如本地稳。  
原因是即使代理像住宅，VPS 的浏览器指纹、行为节奏、cookie 环境仍然容易不自然。你之前已经收到过账号警告，我不建议再拿主账号试。

## 我推荐的实际工作流

```text
本地浏览器采集
→ 保存搜索结果/评论文本
→ 发给 Hermes
→ Hermes 做：
   医生/医院/项目提及统计
   正负面口碑归类
   广告号/水军特征过滤
   推荐/避雷清单
```

也就是说：**本地负责“像人一样看”，VPS 负责“像机器一样整理”。**

如果要做成半自动，我建议先做一个“本地采样助手”，不是爬虫：

- 输入：地区、医院、项目、医生名
- 打开小红书搜索页
- 你人工扫码/登录
- 它慢速辅助打开候选帖子
- 自动把页面文本、评论、作者主页摘要存成本地文件
- 最后上传给我分析

这条路最符合你的场景：慢一点，但风险小，得到的是可决策信息。

## user · 2026-07-03 06:38:34

天下文章一大抄，你去看看有没有现成的成熟方案，开源

## assistant · 2026-07-03 06:40:44

### tool calls

- `tool_slimmer_request_full_tools` `{"reason":"需要联网检索当前开源的小红书本地浏览器/采集成熟方案与社区策略；当前工具集中没有 web/search 工具。"}`

## tool · 2026-07-03 06:40:44

{"message": "Full tool schemas requested for the next model call. Retry the original task after the tool list reloads; do not build substitute tools.", "ok": true, "reason": "\u9700\u8981\u8054\u7f51\u68c0\u7d22\u5f53\u524d\u5f00\u6e90\u7684\u5c0f\u7ea2\u4e66\u672c\u5730\u6d4f\u89c8\u5668/\u91c7\u96c6\u6210\u719f\u65b9\u6848\u4e0e\u793e\u533a\u7b56\u7565\uff1b\u5f53\u524d\u5de5\u5177\u96c6\u4e2d\u6ca1\u6709 web/search \u5de5\u5177\u3002", "tool_slimmer_full_tools_requested": true}

## assistant · 2026-07-03 06:40:44

### tool calls

- `terminal` `{"command":"python3 - <<'PY'\nimport urllib.request, urllib.parse, json, time\nqueries = [\n 'xiaohongshu crawler browser playwright',\n '小红书 爬虫 playwright',\n 'xiaohongshu mcp',\n 'xhs crawler comments',\n '小红书 评论 采集 开源',\n]\nfor q in queries:\n url='https://api.github.com/search/repositories?'+urllib.parse.urlencode({'q':q,'sort':'stars','order':'desc','per_page':8})\n print('\\n===',q,'===')\n req=urllib.request.Request(url, headers={'Accept':'application/vnd.github+json','User-Agent':'Hermes-Agent'})\n try:\n with urllib.request.urlopen(req, timeout=20) as r:\n data=json.load(r)\n for it in data.get('items',[])[:8]:\n print(f\"{it['stargazers_count']:>6}★ {it['full_name']} | {it.get('language')} | {it['html_url']}\")\n print(' ', (it.get('description') or '')[:180])\n except Exception as e:\n print('ERR',repr(e))\n time.sleep(1)\nPY","timeout":180}`
- `read_file` `{"path":"/root/hermes-agent-configuration/references/xhs-cli-alternatives.md","limit":140}`
- `read_file` `{"path":"/root/browser-data-extraction/references/xiaohongshu-extraction.md","limit":100}`

## tool · 2026-07-03 06:40:44

{"output": "=== xiaohongshu crawler browser playwright ===\n\n=== 小红书 爬虫 playwright ===\n    64★ persist-1/Visual_MediaCrawler | Python | https://github.com/persist-1/Visual_MediaCrawler\n    可视化爬虫（支持：哔哩哔哩 | 抖音 | 小红书 | 贴吧 | 微博 | 知乎 | 快手），异步、高效、直观地采集国内主流平台的媒体数据的前后端一体项目（Based on \"MediaCrawler\"）。\n     8★ yangsijie666/xiaohongshu-crawler | Python | https://github.com/yangsijie666/xiaohongshu-crawler\n    小红书数据采集工具 — 基于 Playwright 的隐匿爬虫，支持搜索、笔记详情、评论采集，双层反检测，导出 JSON/Excel，内置 MCP 协议服务 | Xiaohongshu (RedNote) stealth crawler with anti-detection\n     2★ yxwxl/login-state-reader | JavaScript | https://github.com/yxwxl/login-state-reader\n     登录态读取器是一款面向爬虫调试、自动化测试和 Playwright 开发的浏览器插件 + Python 桌面桥接工具。它可以在真实 Chrome/Edge 登录环境中读取当前网页 Cookie、Cookie Header 和 Playwright storage_state，帮助你快速复用 B站、腾讯视频、小红书、知乎、微博等平台的登录状态。支持插件手动复\n     0★ manayim/xhs_blueprint_picker | Python | https://github.com/manayim/xhs_blueprint_picker\n    🔴 小红书选品爬虫工具 - 基于 Playwright 的笔记抓取与爆款分析\n     0★ jiaxuan-yue/XHS_folder_analysis | Python | https://github.com/jiaxuan-yue/XHS_folder_analysis\n    XHS Interview Cards 是一个全自动化的小红书面试知识卡片提取系统。通过 Playwright 爬虫采集小红书收藏集合中的面试笔记，使用 LLM 进行问答分解与生成，最终转换为结构化卡片，最终帮助用户高效积累面试知识\n\n=== xiaohongshu mcp ===\n 49089★ Panniantong/Agent-Reach | Python | https://github.com/Panniantong/Agent-Reach\n    Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.\n 14483★ xpzouying/xiaohongshu-mcp | Go | https://github.com/xpzouying/xiaohongshu-mcp\n    MCP for xiaohongshu.com\n  1067★ iFurySt/RedNote-MCP | TypeScript | https://github.com/iFurySt/RedNote-MCP\n    🚀MCP server for accessing RedNote(XiaoHongShu, xhs).\n   499★ chubbyguan/chubbyskills | Python | https://github.com/chubbyguan/chubbyskills\n    把中文全渠道内容（抖音 / B站 / 小红书 / 公众号 / X / 播客）采集进个人知识库的 13 个 AI Skill：图文存图、视频转文字稿、字幕优先免 GPU，附带知识库 MCP server。 ｜ Ingest Chinese content into your personal knowledge base — image/video routi\n   229★ autoclaw-cc/xiaohongshu-mcp-skills | None | https://github.com/autoclaw-cc/xiaohongshu-mcp-skills\n    xiaohongshu-mcp skills\n   193★ xixihhhh/clipforge | TypeScript | https://github.com/xixihhhh/clipforge\n    ClipForge（原『带货剪手』/ daihuo-jianshou）：开源 AI 带货短视频神器——上传一张商品图，AI 自动提炼卖点 + 写种草脚本 + 锁定商品原图不变形 + 配画面/配音/字幕，一键产出抖音小店 / 快手 / 小红书 / TikTok Shop 卖货短视频。0 成本批量出片、开源无水印、本地自部署。也支持一句话主题成片。Open-so\n   176★ aicu-icu/xhs-mcp-server | JavaScript | https://github.com/aicu-icu/xhs-mcp-server\n    小红书MCP服务器 | 秒级工具调用执行，笔记用户搜索、通知消息监控等；打造2026最简单、最快速、最精准、最好用的小红书MCPServer！\n   161★ YYH211/xiaohongshu | Python | https://github.com/YYH211/xiaohongshu\n    这是一个基于人工智能的网页应用程序，用于为小红书生成和自动发布内容。它通过模型上下文协议（MCP）集成了多种工具和服务，实现了从主题输入到内容发布的全自动化流程。\n\n=== xhs crawler comments ===\n    22★ panda-lsy/XHS-HotCommentsCrawler | Jupyter Notebook | https://github.com/panda-lsy/XHS-HotCommentsCrawler\n    本项目是一个综合性的小红书热门评论数据挖掘与分析工具。通过爬取小红书热门笔记的评论数据，结合NLP技术和数据可视化，深入分析小红书用户的行为特征、评论模式和互动习惯，最终生成高赞用户画像报告。\n     1★ KemenMax/taobao-xhs-crawler | Python | https://github.com/KemenMax/taobao-xhs-crawler\n    A powerful crawler for Taobao comments and Xiaohongshu posts (including covers, content, and images). | 淘宝评论与小红书帖子（含封面图/内容）全自动采集工具。\n     0★ maoxiancat/xhs_discution | Python | https://github.com/maoxiancat/xhs_discution\n      小红书评论爬虫 / Xiaohongshu comment crawler\n     0★ neoxie/crawler-xhs-link | Python | https://github.com/neoxie/crawler-xhs-link\n    use BeautifulSoup to visit xiao hong shu article page and extra title, like count, comment count and star count\n\n=== 小红书 评论 采集 开源 ===", "exit_code": 0, "error": null}

## tool · 2026-07-03 06:40:44

## assistant · 2026-07-03 06:40:44

### tool calls

- `terminal` `{"command":"python3 - <<'PY'\nimport urllib.request, json, base64\nrepos=['NanmiCoder/MediaCrawler','persist-1/Visual_MediaCrawler','yangsijie666/xiaohongshu-crawler','xpzouying/xiaohongshu-mcp','iFurySt/RedNote-MCP','Panniantong/Agent-Reach','aicu-icu/xhs-mcp-server','panda-lsy/XHS-HotCommentsCrawler']\nfor repo in repos:\n print('\\n===',repo,'===')\n try:\n meta=json.load(urllib.request.urlopen(urllib.request.Request(f'https://api.github.com/repos/{repo}',headers={'User-Agent':'Hermes-Agent'}),timeout=20))\n print('stars',meta.get('stargazers_count'),'lang',meta.get('language'),'updated',meta.get('updated_at'),'license',(meta.get('license') or {}).get('spdx_id'))\n print('desc',meta.get('description'))\n for name in ['README.md','README_CN.md','readme.md']:\n try:\n req=urllib.request.Request(f'https://api.github.com/repos/{repo}/contents/{name}',headers={'User-Agent':'Hermes-Agent'})\n c=json.load(urllib.req

...[truncated 989 chars]`

## tool · 2026-07-03 06:40:44

{"output": "=== NanmiCoder/MediaCrawler ===\nstars 55053 lang Python updated 2026-07-02T22:25:03Z license NOASSERTION\ndesc 小红书笔记 | 评论爬虫、抖音视频 | 评论爬虫、快手视频 | 评论爬虫、B 站视频 ｜ 评论爬虫、微博帖子 ｜ 评论爬虫、百度贴吧帖子 ｜ 百度贴吧评论回复爬虫  | 知乎问答文章｜评论爬虫\nreadme README.md len 10848 hits ['小红书', 'xhs', 'playwright', 'cookie', 'comment', '评论', '登录', 'browser', '代理']\n52: 一个功能强大的**多平台自媒体数据采集工具**，支持小红书、抖音、快手、B站、微博、贴吧、知乎等主流平台的公开信息抓取。\n56: - **核心技术**：基于 [Playwright](https://playwright.dev/) 浏览器自动化框架登录保存登录态\n57: - **无需JS逆向**：利用保留登录态的浏览器上下文环境，通过 JS 表达式获取签名参数\n62: | 平台   | 关键词搜索 | 指定帖子ID爬取 | 二级评论 | 指定创作者主页 | 登录态缓存 | IP代理池 | 生成评论词云图 |\n64: | 小红书 | ✅          | ✅              | ✅        | ✅              | ✅          | ✅        | ✅              |\n83: - ✅ **多账号 + IP代理池支持**（重点特性）\n84: - ✅ **去除 Playwright 依赖**，使用更简单\n96: - [ ] **基于评论分析AI Agent正在开发中 🚀🚀**\n135: > 如果使用默认的 CDP 模式（连接已有 Chrome 浏览器），**无需安装浏览器驱动**。仅在使用标准 Playwright 模式时需要安装。\n138: # 仅在标准 Playwright 模式下需要安装浏览器驱动\n139: uv run playwright install\n144: 项目默认使用 CDP 模式连接用户已有的 Chrome 浏览器，可以复用浏览器已有的登录状态、Cookie、扩展等，**大幅降低平台风控检测风险**。\n\n=== persist-1/Visual_MediaCrawler ===\nstars 64 lang Python updated 2026-06-17T07:28:54Z license NOASSERTION\ndesc 可视化爬虫（支持：哔哩哔哩 | 抖音 | 小红书 | 贴吧 | 微博 | 知乎 | 快手），异步、高效、直观地采集国内主流平台的媒体数据的前后端一体项目（Based on \"MediaCrawler\"）。\nreadme README.md len 4340 hits ['小红书', 'xhs', 'cookie', '登录']\n58: - **小红书 (xhs)**：支持搜索、详情页、创作者数据采集\n66: ### 登录方式\n68: - **二维码登录 (qrcode)**：通过扫描二维码进行登录\n69: - **手机号登录 (phone)**：使用手机号和验证码登录\n70: - **Cookie登录 (cookie)**：使用已有的Cookie信息登录\n\n=== yangsijie666/xiaohongshu-crawler ===\nstars 8 lang Python updated 2026-05-21T14:11:44Z license None\ndesc 小红书数据采集工具 — 基于 Playwright 的隐匿爬虫，支持搜索、笔记详情、评论采集，双层反检测，导出 JSON/Excel，内置 MCP 协议服务 | Xiaohongshu (RedNote) stealth crawler with anti-detection\nreadme README.md len 8118 hits ['小红书', 'playwright', 'comment', '评论', 'login', '登录', 'browser', 'mcp', 'stealth']\n9: 小红书 (Xiaohongshu / REDnote) 数据采集框架 + MCP 服务。\n11: 基于 Playwright 实现真实浏览器自动化，具备双层反检测能力（playwright-stealth + browserforge）。通过 MCP 协议将采集能力暴露为标准工具，让 AI 助手（Claude Desktop / Code / Cursor）直接调用。\n15: - **MCP 服务**：AI 助手可直接搜索小红书、采集笔记详情和评论\n19: - 评论采集（Top N 评论，含用户信息和 IP 属地）\n20: - 双层反检测（playwright-stealth 环境级 + browserforge 指纹级）\n21: - 登录状态持久化（扫码登录后自动保存，下次启动免登录）\n23: - 生产级稳定性：超时控制、浏览器崩溃自动恢复、登录态失效检测\n40: uv run playwright install chromium\n42: # 4. 首次登录（扫码）\n43: uv run python scripts/verify_login.py\n49: ### MCP 服务使用\n57:   \"mcpServers\": {\n\n=== xpzouying/xiaohongshu-mcp ===\nstars 14483 lang Go updated 2026-07-02T16:00:38Z license None\ndesc MCP for xiaohongshu.com\nreadme README.md len 33434 hits ['小红书', 'xhs', 'cookie', 'comment', '评论', 'login', '登录', 'browser', 'mcp', '代理', 'proxy']\n1: # xiaohongshu-mcp\n9: [![Docker Pulls](https://img.shields.io/docker/pulls/xpzouying/xiaohongshu-mcp?style=flat-square&logo=docker)](https://hub.docker.com/r/xpzouying/xiaohongshu-mc\n11: MCP for 小红书 / xiaohongshu.com。让你的 AI 助手直接访问小红书数据。\n18: > - [xiaohongshu-mcp-skills](https://github.com/autoclaw-cc/xiaohongshu-mcp-skills)（适用于已部署完本项目的用户）\n22: > #### ✨ 方案 B：x-mcp 浏览器插件版 (推荐给非技术同学 / 追求极简的用户)\n23: > - **不想折腾 Docker 或部署环境？试试：[xpzouying/x-mcp](https://github.com/xpzouying/x-mcp)**\n24: > - **零配置**：安装插件即用，无需任何代码、代理或复杂的环境配置。\n29: - **我的博客文章**：[haha.ai/xiaohongshu-mcp](https://www.haha.ai/xiaohongshu-mcp)\n34: 如果您在部署传统 Docker 版本时遇到问题，**务必先查看：[各种疑难杂症 (Issues #56)](https://github.com/xpzouying/xiaohongshu-mcp/issues/56)**。\n36: > *提示：如果环境排查太耗时，切换到 [x-mcp 插件版](https://github.com/xpzouying/x-mcp) 通常是更高效的选择。*\n40: [![Star History Chart](https://api.star-history.com/svg?repos=xpzouying/xiaohongshu-mcp&type=Timeline)](https://www.star-history.com/#xpzouying/xiaohongshu-mcp&\n46: **捐赠时，请备注 MCP 以及名字。**\n\n=== iFurySt/RedNote-MCP ===\nstars 1067 lang TypeScript updated 2026-07-02T06:54:13Z license MIT\ndesc 🚀MCP server for accessing RedNote(XiaoHongShu, xhs).\nreadme README.md len 2378 hits ['小红书', 'playwright', 'cookie', '评论', 'login', '登录', 'mcp']\n1: # RedNote MCP\n5: [![npm](https://img.shields.io/npm/v/rednote-mcp)](https://www.npmjs.com/package/rednote-mcp)\n7: 小红书内容访问的MCP服务\n13: 开始前确保安装了 [playwright](https://github.com/microsoft/playwright) 环境：\n16: npx playwright install\n23: npm install -g rednote-mcp\n25: # 初始化登录，会自动记录cookie到 ~/.mcp/rednote/cookies.json\n26: rednote-mcp init\n33: git clone https://github.com/ifuryst/rednote-mcp.git\n34: cd rednote-mcp\n42: # 或者直接运行，如初始化登录\n48: - 认证管理（支持 Cookie 持久化）\n\n=== Panniantong/Agent-Reach ===\nstars 49089 lang Python updated 2026-07-02T22:38:49Z license MIT\ndesc Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.\nreadme README.md len 12078 hits ['小红书', 'xhs', 'cookie', '评论', '登录', 'browser', 'mcp', '代理']\n31: - 📕 \"帮我看看小红书上这个品的口碑\" → **打不开**，必须登录才能看\n40: 每个平台都有自己的门槛——要付费的 API、要绕过的封锁、要登录的账号、要清洗的数据。你要一个一个去踩坑、装工具、调配置，光是让 Agent 能读个推特就得折腾半天。\n48: 复制给你的 Agent，几分钟后它就能读推特、搜 Reddit、看 YouTube、刷小红书了。\n62: | 💰 **完全免费** | 所有工具开源、所有 API 免费。唯一可能花钱的是服务器代理（$1/月），本地电脑不需要 |\n63: | 🔒 **隐私安全** | Cookie 只存在你本地，不上传不外传。代码完全开源，随时可审查 |\n77: | 🔍 **全网搜索** | — | 全网语义搜索 | 自动配置（MCP 接入，免费无需 Key） |\n78: | 📦 **GitHub** | 读公开仓库 + 搜索 | 私有仓库、提 Issue/PR、Fork | 告诉 Agent「帮我登录 GitHub」 |\n80: | 📺 **B站** | 搜索 + 视频详情（bili-cli，无需登录） | 字幕（OpenCLI） | 告诉 Agent「帮我配 B站」 |\n81: | 📖 **Reddit** | —（没有零配置路径：匿名接口已被封） | 搜索 + 读帖子和评论 | 桌面装 OpenCLI 用浏览器登录态；或 rdt-cli + Cookie |\n82: | 📘 **Facebook** | — | 搜索、主页、Feed、群组列表 | 桌面装 OpenCLI（复用 Chrome 登录态） |\n83: | 📷 **Instagram** | — | 用户搜索、Profile、用户最近帖子、Explore | 桌面装 OpenCLI（复用 Chrome 登录态） |\n84: | 📕 **小红书** | — | 搜索、阅读、评论 | 桌面装 OpenCLI（刷过小红书即可用）；服务器用 xiaohongshu-mcp 扫码 |\n\n=== aicu-icu/xhs-mcp-server ===\nstars 176 lang JavaScript updated 2026-06-30T07:12:06Z license None\ndesc 小红书MCP服务器 | 秒级工具调用执行，笔记用户搜索、通知消息监控等；打造2026最简单、最快速、最精准、最好用的小红书MCPServer！\nreadme README.md len 1167 hits ['小红书', 'xhs', 'mcp']\n1: # 小红书 MCP 服务器\n3: > 简单、快速、精准的小红书 MCP 服务，方便接入 AI 助手实现自动化运营。\n15: 内置 10 个 MCP 工具，支持扩展开发。\n17: ![MCP 工具列表](./screenshots/xhsmcp-broxy.png)\n19: AI 客户端使用 `StreamableHTTP` 协议连接 MCP 服务，可自定义认证头增强安全性。\n21: ![连接测试](./screenshots/xhsmcp-tools.png)\n23: ![使用效果](./screenshots/xhsmcp-test.png)\n29: 1. 打开 [小红书官网](https://www.xiaohongshu.com)\n34: fetch('//raw.githubusercontent.com/aicu-icu/xhs-mcp-server/refs/heads/main/loader.user.js').then(r=>r.text()).then(eval)\n46: 2. 安装完毕后，打开小红书网页：https://www.xiaohongshu.com\n49:    https://cdn.jsdelivr.net/gh/aicu-icu/xhs-mcp-server@main/data-json/latest.json\n53: 导入完毕，点击启动，即可获取 MCP 服务地址去使用\n\n=== panda-lsy/XHS-HotCommentsCrawler ===\nstars 22 lang Jupyter Notebook updated 2026-03-06T10:03:03Z license MIT\ndesc 本项目是一个综合性的小红书热门评论数据挖掘与分析工具。通过爬取小红书热门笔记的评论数据，结合NLP技术和数据可视化，深入分析小红书用户的行为特征、评论模式和互动习惯，最终生成高赞用户画像报告。\nreadme README.md len 633 hits ['小红书', 'xhs', 'comment', '评论']\n1: # XHS-HotCommentsCrawler\n3: 本项目是一个综合性的小红书热门评论数据挖掘与分析工具。通过爬取小红书热门笔记的评论数据，结合NLP技术和数据可视化，深入分析小红书用户的行为特征、评论模式和互动习惯，最终生成高赞用户画像报告。\n6: # 小红书热评数据挖掘与用户画像分析\n10: 本项目是一个综合性的小红书热门评论数据挖掘与分析工具。通过爬取小红书热门笔记的评论数据，结合NLP技术和数据可视化，深入分析小红书用户的行为特征、评论模式和互动习惯，最终生成高赞用户画像报告。\n18: -**评论内容**：文字内容、回复对象、评论层级(一级/二级)\n20: -**互动指标**：点赞数量、收藏数、二级评论数\n28: 1.**时间特征分析**：点赞时段分布、评论发布时间与热度关系\n30: 2.**互动关系分析**：一级评论点赞数与二级评论数的关系、评论网络结构\n39: - 帮助理解高赞评论的特征规律\n40: - 揭示小红书社区互动模式和用户行为特点", "exit_code": 0, "error": null}

## assistant · 2026-07-03 06:40:44

Boss，我查了 GitHub 当前开源生态。现成方案是有的，而且已经有比我们自己从零写 Playwright 更成熟的路线。

## 结论

**最值得借鉴的是 `MediaCrawler`，其次是 `xpzouying/xiaohongshu-mcp` 的浏览器插件版 / x-mcp 思路。**

你要的“本地浏览器模拟真人点击浏览”，开源社区成熟方向已经很明确：

> **本地真实 Chrome + CDP 连接 + 复用已有登录态/Cookie + 慢速采样 + 本地保存结果 + Hermes 分析。**

不是传统 headless 爬虫。

---

## 候选项目

### 1. `NanmiCoder/MediaCrawler` — 最成熟

GitHub：`NanmiCoder/MediaCrawler`  
Stars：约 **55k**  
技术：Python + Playwright / CDP  
支持：小红书搜索、笔记、评论、二级评论、创作者主页、登录态缓存、代理池

它 README 里有一个关键点：

> 默认使用 **CDP 模式连接用户已有 Chrome 浏览器**，可以复用浏览器已有登录状态、Cookie、扩展等，降低平台风控检测风险。

这正好对应你的想法。

**我认为它是首选参考方案。**

优点：
- 社区最大，踩坑最多
- 支持小红书评论
- 支持登录态缓存
- 支持 CDP 连接已有浏览器
- 不需要 JS 逆向，靠浏览器上下文拿签名
- 比 VPS headless 更贴近真人环境

缺点：
- 还是采集工具，不是“账号绝对安全”
- 配置和依赖会比小工具重
- 需要在你本地电脑跑更合适

---

### 2. `persist-1/Visual_MediaCrawler` — 可视化版

GitHub：`persist-1/Visual_MediaCrawler`  
Stars：约 64  
定位：基于 MediaCrawler 的前后端可视化项目  
支持：小红书搜索、详情页、创作者数据采集

优点：
- 有 UI，适合非纯命令行使用
- 可能更适合你本地手动控制

缺点：
- 社区小很多
- 本质还是 MediaCrawler 包装版，稳定性看上游

---

### 3. `yangsijie666/xiaohongshu-crawler` — Playwright 隐匿爬虫

GitHub：`yangsijie666/xiaohongshu-crawler`  
Stars：较少，约 8  
技术：Playwright + playwright-stealth + browserforge  
支持：搜索、笔记详情、评论采集、JSON/Excel 导出、MCP 服务

优点：
- 很贴合“小红书 + Playwright + 评论 + MCP”
- 明确写了双层反检测、登录态持久化、崩溃恢复

缺点：
- 星少，项目成熟度不如 MediaCrawler
- 可以参考设计，但不建议直接当主力

---

### 4. `xpzouying/xiaohongshu-mcp` / `x-mcp` — MCP 生态路线

GitHub：`xpzouying/xiaohongshu-mcp`  
Stars：约 **14k**

这个我们已经在 VPS 上试过，VPS 风控不理想。但它 README 里现在强调了一个新方向：

> x-mcp 浏览器插件版，零配置，安装插件即用。

这个思路很适合我们：**浏览器插件在本地拿数据，Hermes/MCP 只接收结果。**

优点：
- MCP 接入天然适合 Hermes
- 插件版比服务器部署更接近本地真人环境

缺点：
- 需要再验证插件版是否成熟
- 你本地需要装插件/本地服务

---

### 5. `Agent-Reach` — 泛平台本地登录态方案

GitHub：`Panniantong/Agent-Reach`  
Stars：约 **49k**

它不是小红书专用，但定位很有意思：

> 让 Agent 读 Twitter、Reddit、YouTube、Bilibili、小红书；Cookie 只存在本地；桌面装 OpenCLI 复用 Chrome 登录态。

它对小红书的说法是：

> 桌面装 OpenCLI，刷过小红书即可用；服务器用 xiaohongshu-mcp 扫码。

这个方向也值得看：**本地 OpenCLI / 浏览器登录态 → Agent 调用。**

---

## 我建议采用的方案

### 第一阶段：不要自己造轮子，先验证 MediaCrawler 本地 CDP

最小可行路径：

```text
本地电脑打开 Chrome
→ 用真实账号正常登录小红书
→ Chrome 开 remote debugging port
→ MediaCrawler 用 CDP 连接这个 Chrome
→ 低频采样搜索结果和评论
→ 导出 JSON/CSV
→ 发给 Hermes 分析
```

本地 Chrome 启动方式大概是：

```bash
chrome --remote-debugging-port=9222 --user-data-dir=/path/to/xhs-profile
```

然后 MediaCrawler 连接这个浏览器。

### 第二阶段：做“半自动采样助手”

不要做全量爬虫，做一个保守采样器：

```text
输入：
地区 + 医院 + 项目 + 医生名

动作：
1. 搜索 1 个关键词
2. 只打开前 5–10 条
3. 每条停留 30–90 秒
4. 只采样前 20–50 条评论
5. 保存作者主页简要特征
6. 生成 JSON/Markdown
```

### 第三阶段：Hermes 做分析

Hermes 不碰小红书账号，只分析本地导出的数据：

```text
医生/医院提及统计
正负面评论归类
避雷信号
推荐信号
疑似广告号过滤
证据链接整理
```

---

## 我不建议的方案

不建议继续走：

```text
VPS + 主账号 + headless + 高频搜索/评论抓取
```

你已经被警告过一次，这条路收益不值得。

也不建议一上来搞：

```text
住宅代理池 + 多账号矩阵
```

对你这个需求太重，且会把问题复杂化。

---

## 最优路线

我的建议很明确：

**用 `MediaCrawler` 的本地 CDP 模式做底座。**

原因：

- 开源成熟度最高
- 支持小红书评论
- 复用真实 Chrome 登录态
- 比 VPS headless 更稳
- 后续可以和 Hermes 分工：本地采集，VPS 分析

如果你要，我下一步可以直接给你整理一份本地部署方案：Windows/Mac 两套启动命令、MediaCrawler 配置、采样参数，以及导出给 Hermes 分析的数据格式。

## user · 2026-07-03 08:04:08

我想这个采集的，本地有没有浏览器插件可以做呢？自动采集完，数据再清洗一下

## assistant · 2026-07-03 08:05:48

### tool calls

- `terminal` `{"command":"python3 - <<'PY'\nimport urllib.request, urllib.parse, json, base64, time\nqueries=['xiaohongshu browser extension crawler','小红书 浏览器插件 采集 github','小红书 插件 导出 评论 github','xhs chrome extension crawler','xiaohongshu userscript export comments']\nfor q in queries:\n print('\\n===',q,'===')\n url='https://api.github.com/search/repositories?'+urllib.parse.urlencode({'q':q,'sort':'stars','order':'desc','per_page':10})\n req=urllib.request.Request(url,headers={'User-Agent':'Hermes-Agent','Accept':'application/vnd.github+json'})\n try:\n data=json.load(urllib.request.urlopen(req,timeout=20))\n for it in data.get('items',[])[:10]:\n print(f\"{it['stargazers_count']:>6}★ {it['full_name']} | {it.get('language')} | {it['html_url']}\")\n print(' ',(it.get('description') or '')[:180])\n except Exception as e: print('ERR',repr(e))\n time.sleep(1)\nPY","timeout":180}`
- `terminal` `{"command":"python3 - <<'PY'\nimport urllib.request,json,base64\nrepos=['xpzouying/x-mcp','aicu-icu/xhs-mcp-server','yxwxl/login-state-reader','Panniantong/Agent-Reach','chubbyguan/chubbyskills']\nfor repo in repos:\n print('\\n===',repo,'===')\n try:\n meta=json.load(urllib.request.urlopen(urllib.request.Request(f'https://api.github.com/repos/{repo}',headers={'User-Agent':'Hermes-Agent'}),timeout=20))\n print('stars',meta.get('stargazers_count'),'lang',meta.get('language'),'updated',meta.get('updated_at'),'license',(meta.get('license') or {}).get('spdx_id'))\n print('desc',meta.get('description'))\n for name in ['README.md','README.zh-CN.md','README_CN.md','readme.md']:\n try:\n c=json.load(urllib.request.urlopen(urllib.request.Request(f'https://api.github.com/repos/{repo}/contents/{name}',headers={'User-Agent':'Hermes-Agent'}),timeout=20))\n txt=base64.b64decode(c['content']).decode('utf-8','replace')\n

...[truncated 610 chars]`

## tool · 2026-07-03 08:05:48

{"output": "=== xiaohongshu browser extension crawler ===\n\n=== 小红书 浏览器插件 采集 github ===\n\n=== 小红书 插件 导出 评论 github ===\n\n=== xhs chrome extension crawler ===\n\n=== xiaohongshu userscript export comments ===", "exit_code": 0, "error": null}

## tool · 2026-07-03 08:05:48

{"output": "=== xpzouying/x-mcp ===\nstars 389 lang None updated 2026-07-02T00:11:05Z license None\ndesc 小红书创作中心\nreadme README.md len 3361\n1: # 🚀 X-MCP: 小红书自动化助手\n3: [![Chrome Web Store](https://img.shields.io/badge/Chrome-Extension-blue?logo=google-chrome)](https://chromewebstore.google.com/detail/%E5%B0%8F%E7%BA%A2%E4%B9%A6mcp%E5%8A%A9%E6%89%\n6: **X-MCP** 是针对 [xiaohongshu-mcp](https://github.com/xpzouying/xiaohongshu-mcp) 的浏览器插件增强版。它利用 **MCP (Model Context Protocol)** 协议，让 AI 客户端（如 Claude, Cursor）能够直接控制你的浏览器，实现更安全、更简单的自动化发\n10: ## 🌟 为什么选择 X-MCP？\n15: - **操作完全可见**：所有的自动化流程都在浏览器中实时展示，随时可干预。\n16: - **账号安全性高**：复用你常用的浏览器登录状态，无异地登录或环境异常风险。\n22: |<img src=\"https://github.com/user-attachments/assets/2cea0dda-e69f-400a-882b-59273fed403b\" alt=\"x-mcp-qr04\" width=\"300\"/>|\n27: | <img src=\"https://github.com/user-attachments/assets/33f55c4b-71a9-426c-b1ca-ffab1d7a7e2b\" alt=\"x-mcp-feishu-01\" width=\"300\"/>|\n31: ### 方式一：Chrome 应用商店（推荐 ✨）\n32: 1. 访问 [小红书MCP助手 - Chrome应用商店](https://chromewebstore.google.com/detail/%E5%B0%8F%E7%BA%A2%E4%B9%A6mcp%E5%8A%A9%E6%89%8B/dklmcgbmpnmkndhapabinagmmdmjhjgl)。\n33: 2. 点击 **“添加到 Chrome”**。\n38: 2. 打开 Chrome 扩展管理界面 (`chrome://extensions/`)。\n48: > ⚠️ **注意**：Token 只会展示一次，请妥善保存，后续需填入插件中。\n50: ### 2. 绑定插件\n51: 点击插件图标，在设置页填入 API Token。绑定成功后，你可以在官网控制台实时查看到插件的在线状态。\n58: 在插件面板中可以一键测试“检查登录状态”，确保小红书账号已登录且 MCP 连接正常。\n68: claude mcp add --transport http x-mcp https://mcp.aredink.com/mcp --header \"X-API-Key: ***\n74: export CODEX_XMCP_TOKEN=***\n\n=== aicu-icu/xhs-mcp-server ===\nstars 176 lang JavaScript updated 2026-06-30T07:12:06Z license None\ndesc 小红书MCP服务器 | 秒级工具调用执行，笔记用户搜索、通知消息监控等；打造2026最简单、最快速、最精准、最好用的小红书MCPServer！\nreadme README.md len 1167\n1: # 小红书 MCP 服务器\n3: > 简单、快速、精准的小红书 MCP 服务，方便接入 AI 助手实现自动化运营。\n7: - **简单** - 无需复杂配置，浏览器 + 插件即可，2 分钟搞定\n15: 内置 10 个 MCP 工具，支持扩展开发。\n17: ![MCP 工具列表](./screenshots/xhsmcp-broxy.png)\n19: AI 客户端使用 `StreamableHTTP` 协议连接 MCP 服务，可自定义认证头增强安全性。\n21: ![连接测试](./screenshots/xhsmcp-tools.png)\n23: ![使用效果](./screenshots/xhsmcp-test.png)\n29: 1. 打开 [小红书官网](https://www.xiaohongshu.com)\n34: fetch('//raw.githubusercontent.com/aicu-icu/xhs-mcp-server/refs/heads/main/loader.user.js').then(r=>r.text()).then(eval)\n46: 2. 安装完毕后，打开小红书网页：https://www.xiaohongshu.com\n49: https://cdn.jsdelivr.net/gh/aicu-icu/xhs-mcp-server@main/data-json/latest.json\n53: 导入完毕，点击启动，即可获取 MCP 服务地址去使用\n59: > 小红书接口请求频繁或 `xsec_token` 参数不匹配会触发风控，导致账号异常退出。\n\n=== yxwxl/login-state-reader ===\nstars 2 lang JavaScript updated 2026-06-30T05:08:18Z license MIT\ndesc 登录态读取器是一款面向爬虫调试、自动化测试和 Playwright 开发的浏览器插件 + Python 桌面桥接工具。它可以在真实 Chrome/Edge 登录环境中读取当前网页 Cookie、Cookie Header 和 Playwright storage_state，帮助你快速复用 B站、腾讯视频、小红书、知乎、微博等平台的登录状态。支持插件手动复制，也支持 Python 脚本传入 URL 自动打开页面、获取登录态并返回结果，适合本地开发、接口调试、数据采集前置认证和复杂网页登录态分析。\nreadme README.md len 5022\n3: > Chrome / Edge 插件 + Python 桌面桥接工具，用于读取网页 Cookie、Cookie Header 和 Playwright 登录态。\n6: ![Chrome Extension](https://img.shields.io/badge/Chrome-Manifest%20V3-2ea44f.svg)\n9: 登录态读取器面向本地开发、自动化测试、爬虫调试和 Playwright 调试场景。它可以在真实浏览器环境中读取当前页面的 Cookie 和 localStorage，并把结果转换为 Cookie JSON、Cookie Header 或 Playwright `storage_state`。\n15: - 小红书 / xiaohongshu\n16: - 知乎、微博、抖音、其他需要真实浏览器登录态的网站\n21: - **Cookie JSON**：读取目标页面及相关域名 Cookie。\n22: - **Cookie Header**：生成可直接用于 HTTP 请求的 `Cookie` 请求头。\n23: - **Playwright 登录态**：生成 `storage_state` 兼容数据，包含 cookies 和 localStorage。\n24: - **真实浏览器环境**：复用用户已登录的 Chrome / Edge profile。\n25: - **桌面应用通信**：插件通过 `ws://127.0.0.1:17891` 与本地 Python 程序通信。\n26: - **脚本控制插件**：Python 传入 URL 和读取模式，插件自动打开页面、读取数据、返回结果。\n27: - **自动启动浏览器**：浏览器未打开时，桌面端可自动启动 Chrome / Edge。\n28: - **自动清理标签页**：默认关闭插件为本次读取新建的目标标签页。\n33: comment_cli/\n34: ├─ extension/ # 浏览器插件源码，加载这个目录\n45: │ ├─ cookie.png\n60: ### 2. 加载浏览器插件\n62: 打开 Chrome 或 Edge：\n\n=== Panniantong/Agent-Reach ===\nstars 49114 lang Python updated 2026-07-02T23:59:40Z license MIT\ndesc Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.\nreadme README.md len 12078\n31: - 📕 \"帮我看看小红书上这个品的口碑\" → **打不开**，必须登录才能看\n48: 复制给你的 Agent，几分钟后它就能读推特、搜 Reddit、看 YouTube、刷小红书了。\n63: | 🔒 **隐私安全** | Cookie 只存在你本地，不上传不外传。代码完全开源，随时可审查 |\n77: | 🔍 **全网搜索** | — | 全网语义搜索 | 自动配置（MCP 接入，免费无需 Key） |\n81: | 📖 **Reddit** | —（没有零配置路径：匿名接口已被封） | 搜索 + 读帖子和评论 | 桌面装 OpenCLI 用浏览器登录态；或 rdt-cli + Cookie |\n82: | 📘 **Facebook** | — | 搜索、主页、Feed、群组列表 | 桌面装 OpenCLI（复用 Chrome 登录态） |\n83: | 📷 **Instagram** | — | 用户搜索、Profile、用户最近帖子、Explore | 桌面装 OpenCLI（复用 Chrome 登录态） |\n84: | 📕 **小红书** | — | 搜索、阅读、评论 | 桌面装 OpenCLI（刷过小红书即可用）；服务器用 xiaohongshu-mcp 扫码 |\n92: > 🍪 需要 Cookie/登录态的平台（Twitter、小红书、Reddit、Facebook、Instagram 等），优先让用户在自己的浏览器里登录。OpenCLI 复用 Chrome 登录态；传统 CLI 才需要 Cookie-Editor 导出 Cookie。\n94: > 🔒 Cookie 只存在你本地，不上传不外传。代码完全开源，随时可审查。\n103: > Agent Reach 依赖 Agent 执行 shell 命令（`pip install`、`mcporter`、`twitter` 等）。如果你的 OpenClaw 使用了默认的 `messaging` 工具配置，Agent 将无法执行命令。**安装前请先开启 exec 权限**：\n134: 2. **安装系统基建** — 自动检测并安装 Node.js、gh CLI、mcporter\n135: 3. **配置搜索引擎** — 通过 MCP 接入 Exa（免费，无需 API Key）\n138: 6. **问你要不要更多** — 默认只激活 6 个零配置渠道；小红书、Twitter、Reddit、Facebook、Instagram 这些需要登录态的，Agent 会列菜单问你要哪些，点名才装\n156: **不需要记命令。** Agent 读了 SKILL.md 之后自己知道该调什么。需要登录的平台（小红书、Twitter、Reddit、Facebook、Instagram），告诉 Agent「帮我配 XXX」即可解锁。\n162: 有些任务超出\"读\"的范围：登录后的网页操作、表单提交、多账号隔离、并行浏览器会话、自动化流程中的登录、验证、风控提示等高摩擦环节的人工接手。这类\"动手\"场景，可以配合 [BrowserAct](https://www.browseract.ai/Agent) 这类浏览器自动化工具——30+ 预制平台技能，支持 Claude Code / OpenClaw /\n172: 你给一个新 Agent 装环境的时候，总要花时间去找工具、装依赖、调配置——Twitter 用什么读？Reddit 怎么登录？小红书的 CLI 停更了换什么？每次都要重新踩一遍。Agent Reach 做的事情很简单：**当下最稳的接入方式，我们替你选好、装好、体检好。接入方式会换代（2026 年 3 月一批单平台 CLI 集体停更，我们换了路由），你不用操\n186: ├── facebook.py → OpenCLI（桌面浏览器登录态）\n\n=== chubbyguan/chubbyskills ===\nstars 499 lang Python updated 2026-07-02T05:32:01Z license MIT\ndesc 把中文全渠道内容（抖音 / B站 / 小红书 / 公众号 / X / 播客）采集进个人知识库的 13 个 AI Skill：图文存图、视频转文字稿、字幕优先免 GPU，附带知识库 MCP server。 ｜ Ingest Chinese content into your personal knowledge base — image/video routing, subtitle-first transcription, and a KB MCP server.\nreadme README.md len 14247\n28: 1. **采集层**：把 B站、YouTube、抖音、小红书、公众号、X、播客等内容转成统一 Markdown。\n30: 3. **Agent 层**：通过 Agent Skills 和 MCP server，让 Claude Code、Codex、OpenCode、OpenClaw、Hermes 等 Agent 真正读取你的知识库完成任务。\n39: - Agent 用户：把 skill、CLI、MCP 和 vault 串成可复用工作流。\n48: | 平台采集 | 视频、图文、公众号、播客、X、小红书等内容转 Markdown | `tools/chubby_ingest.py` / 各 skill 脚本 |\n53: | MCP | Agent 搜索、读取、重建索引、查看统计 | `knowledge-base-management/scripts/mcp_server.py` |\n72: - MCP 依赖是否可用\n80: python3 tools/mcp_workflow_demo.py\n98: 轻量模式可直接使用：X 图文、小红书图文、公众号基础处理、行业情报雷达、知识库健康检查、content-enrich。视频和播客转录才需要 `ffmpeg`、`yt-dlp`、`funasr`、`torch`、`faster-whisper` 等重依赖。\n131: | 小红书 | [`xiaohongshu-ingest`](./xiaohongshu-ingest/SKILL.md) | 图文存图、视频转录、爆款拆解、衍生选题 | 图文零依赖，建议 `XHS_COOKIE` | `--fallback-text` 手动正文 |\n157: export DEEPSEEK_API_KEY=\"***\"\n227: export OPENAI_API_KEY=\"***\"\n264: ### 8. 让 Agent 通过 MCP 使用你的 vault\n266: 启动 MCP server：\n269: pip install mcp\n270: VAULT_DIR=~/Documents/ObsidianVault python3 knowledge-base-management/scripts/mcp_server.py\n273: MCP 工具：\n285: python3 tools/mcp_workflow_demo.py\n288: 这个 demo 会用 `fixtures/mcp-vault` 完成一次“检索 vault → 读取原文 → 带来源回答”的 Agent 任务。详细配置见 [docs/mcp-workflow.md](./docs/mcp-workflow.md)。", "exit_code": 0, "error": null}

## assistant · 2026-07-03 08:05:48

有，Boss。浏览器插件路线是存在的，而且比“本地 Playwright 脚本”更贴近你的想法。

我刚查了几个开源方向，比较值得看的有这几类：

## 1. 首选参考：`xpzouying/x-mcp`

GitHub：`xpzouying/x-mcp`  
定位：**小红书 MCP 浏览器插件版**  
Stars：约 389  
特点：

- Chrome 插件
- 复用你本地浏览器的小红书登录态
- 所有操作在浏览器里可见
- 可以让 AI/MCP 控制浏览器执行搜索、读取、发布等动作
- 官方描述里强调：无异地登录、环境异常风险更低

它是 `xiaohongshu-mcp` 作者做的浏览器插件增强版。这个方向最符合你说的：

> 本地浏览器模拟真人点击浏览，自动采集完，再清洗数据。

缺点是它偏 MCP 自动化助手，不一定天然就是“批量导出评论清洗工具”，但作为底座很合适。

---

## 2. 可参考：`aicu-icu/xhs-mcp-server`

GitHub：`aicu-icu/xhs-mcp-server`  
定位：浏览器 + 插件 / loader 启动的小红书 MCP 服务  
Stars：约 176

它的 README 明确写：

- 浏览器 + 插件即可
- 打开小红书网页
- 通过 loader/userscript 启动
- 获取 MCP 服务地址使用

它自己也提示了关键风险：

> 小红书接口请求频繁或 `xsec_token` 参数不匹配会触发风控，导致账号异常退出。

这个项目可以参考，但我不会把它作为首选，因为它更像“快速接入 MCP”的轻量方案，成熟度和安全边界需要验证。

---

## 3. 辅助工具：`login-state-reader`

GitHub：`yxwxl/login-state-reader`  
定位：浏览器插件 + Python 桌面桥接工具  
用途：读取真实 Chrome/Edge 登录态，导出 Cookie Header / Playwright storage_state

它不是小红书采集器，但很有用：可以把你本地浏览器登录态安全地转给本地采集脚本，不用在 VPS 或微信里传 cookie。

适合做：

```text
本地 Chrome 登录小红书
→ 插件读取登录态
→ 本地采集器使用
→ 数据导出
```

---

## 4. 泛平台方案：`Agent-Reach`

GitHub：`Panniantong/Agent-Reach`  
Stars：约 49k  
定位：让 Agent 读取 Twitter、Reddit、B站、小红书等平台  
它的思路是：

> 需要登录的平台，优先让用户在自己的浏览器里登录；OpenCLI 复用 Chrome 登录态；Cookie 只存在本地。

这个方向很适合作为“本地数据采集网关”，但它不是小红书专用采集器。

---

## 我的判断

如果你想要“浏览器插件 + 自动采集 + 清洗”，我建议路线是：

```text
x-mcp / 浏览器插件
→ 本地 Chrome 真实登录
→ 低频搜索和打开笔记
→ 导出原始 note/comment 数据
→ 本地清洗脚本
→ Hermes 做口碑分析
```

而不是：

```text
VPS headless 爬虫
```

---

## 推荐架构

### 本地插件层

负责：

- 打开小红书
- 搜索关键词
- 打开笔记
- 展开评论
- 采集正文、评论、作者、点赞、时间、链接
- 本地保存 JSON

### 本地清洗层

负责：

- 去重
- 过滤空评论
- 合并同一医生/医院/项目别名
- 标注疑似广告号
- 生成干净 Markdown / JSON

### Hermes 分析层

负责：

- 正负面归类
- 推荐/避雷医生提取
- 证据链整理
- 广告号识别
- 输出最终决策报告

---

## 我建议先做的 MVP

不要一上来做大而全插件。先做一个保守版：

```text
输入：
汕头 + 某医院 + 拔智齿

采集：
搜索结果前 10 条
每条评论前 30 条
作者主页只抓标题摘要，不深挖

输出：
xhs_raw.json
xhs_cleaned.md
xhs_report.md
```

频率限制：

```text
每次只跑一个关键词
每篇停留 30–90 秒
每轮不超过 10 篇
遇到验证码/异常提示立刻停
```

---

## 最值得先验证的两个项目

我建议按这个顺序：

1. **`xpzouying/x-mcp`**
   - 看它插件版是否能稳定搜索、读评论、导出结果。
   - 如果能用，直接接 Hermes/MCP 最省事。

2. **`MediaCrawler` 的 CDP 本地 Chrome 模式**
   - 如果 x-mcp 导出能力不够，就用 MediaCrawler 做采集底座。
   - 插件只负责登录态/人工可视化控制。

一句话：**有现成方向，不需要从零造轮子。优先试 `x-mcp` 插件版；不够用再落到 MediaCrawler + 本地 Chrome CDP。**

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