OpenClaw/clawhub-security-signals huggingface dataset:
Skill: cortexuvula/taildrop (version 1.0.0)

--- name: taildrop description: Download files from Tailscale Taildrop inbox to local storage. Use when user wants to retrieve files sent via Tailscale or mentions Taildrop. --- # Taildrop File Retrieval Download files from Tailscale's Taildrop inbox to your local Downloads folder. ## Quick Usage ```bash ~/clawd/skills/taildrop/scripts/taildrop-get.sh ``` This will: - Check for files in the Taildrop inbox - Download them to `~/Downloads/` - Handle filename conflicts (skip duplicates by default) - Report what was downloaded ## Options ```bash # Download to a custom directory ~/clawd/skills/taildrop/scripts/taildrop-get.sh /path/to/directory # Overwrite existing files ~/clawd/skills/taildrop/scripts/taildrop-get.sh --overwrite # Rename duplicates ~/clawd/skills/taildrop/scripts/taildrop-get.sh --rename ``` ## Prerequisites - Tailscale must be installed and running - User must be set as Tailscale operator (run once): `sudo tailscale set --operator=$USER` - Otherwise, sudo is required for each download ## Notes - Unlike macOS/Windows, Linux Tailscale doesn't auto-save Taildrop files - Files stay in the inbox until manually retrieved - The script can run in loop mode to auto-receive files as they arrive


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: lz0311-zhen/mplus-report-insight (version 1.0.0)

*** ## name: mplus-deep-insight version: 1.0.1 description: 自动解析 Mplus 输出文件，提取拟合指数与标准化参数，生成 APA 风格图表和可直接引用的 PDF 分析报告。专为心理学、管理学、教育学等社科研究者设计。 trigger-keywords: Mplus, SEM, CFA, 结构方程, 验证性因子分析, 模型拟合, 路径系数, 报告生成 author: Your Name license: MIT tags: [Mplus, SEM, CFA, data-analysis, report-generation, psychology, research] # 🔬 Mplus 深度洞察报告生成器 你是否每次打开 Mplus 的 `.out` 文件都要用肉眼找 χ²、CFI、RMSEA？ 是否手动将一堆数字复制到 Excel 里制图，只为给导师看模型拟合好不好？ 这个助手能自动完成这些机械工作，并为你生成**符合 APA 7 标准的完整分析报告**。 ## 🎯 适用场景 * 验证性因子分析（CFA）/ 结构方程模型（SEM）结果解读 * 多模型拟合比较 * 学位论文、期刊论文的结果图表制作 * 组会汇报前的快速数据整理 ## 🧠 它能做什么 上传 Mplus 的 `.out` 文本文件，自动执行： 1. **文件校验**：确认是否为有效 Mplus 输出，检测必要模块是否存在 2. **统计量提取**：无条件适配不同版本格式，提取 χ²/df、CFI、TLI、RMSEA（含 90% CI）、SRMR、AIC、BIC 及 STDYX 标准化参数 3. **可视化图表**：生成 **不少于 3 张** 学术图表（仅使用蓝/橙/青绿三色，色盲友好） * 拟合指数阈值对比图 * 标准化估计森林图（显著性着色） * 参数载荷排序图 4. **PDF 报告**：整合元数据、图表与分级解读，可直接插入论文或发送导师 5. **智能解读**：基于 Hu & Bentler (1999) 阈值给出严谨的分级结论，不做“一刀切”判断 ## ⚙️ 工作流程 1. 用户上传或指定 `.out` 文件路径 2. 运行 `python scripts/validate.py` 进行格式校验 3. 运行 `python scripts/main.py -i 你的文件.out -o 报告.pdf` 4. 工具自动解析 → 制图 → 生成 PDF → 控制台预览关键结果 ## 📂 项目结构 mplus-deep-insight/ ├── SKILL.md ├── demo-input.txt ├── scripts/ │ ├── main.py # 核心：解析、制图、报告 │ ├── validate.py # 文件合法性校验 │ └── utils.sh # 一键环境安装 ├── references/ │ ├── background.md # Mplus 输出背景知识 │ ├── operating-rules.md # 统计阈值与图表规范 │ └── examples.md # 典型使用案例 └── assets/ ├── output-template.md └── schema.json &#x23;# 🚀 快速开始 &#x60;``bash &#x23; 环境准备（仅首次） bash scripts/utils.sh source mplus-env/bin/activate &#x23; 校验文件（确保是 Mplus 输出） python scripts/validate.py my_model.out &#x23; 生成报告（输出为 analysis_report.pdf） python scripts/main.py -i my_model.out -o analysis_report.pdf ## 📊 示例效果 输入一份典型的 CFA 输出，生成的报告包含： * 📋 报告元数据表（文件来源、Mplus 版本、生成时间） * 📈 拟合指数对比图（χ²/df、CFI、TLI、RMSEA、SRMR + 阈值线） * 🌲 标准化路径系数森林图（p<0.05 为蓝色，否则橙色） * 📊 前 15 大载荷/路径系数排序图 * 📝 每张图一段基于数值的分级解释（优秀/可接受/欠佳） * ✅ 综合结论与后续建模建议 &#x23;# 脚本 * `scripts/main.py` — 主分析脚本：解析、制图、生成 PDF * `scripts/validate.py` — 快速校验文件是否为合法 Mplus 输出 * `scripts/utils.sh` — 一键安装 Python 依赖（matplotlib, pandas, reportlab, numpy） &#x23;# 参考资料 * `references/background.md` — Mplus 输出格式与关键模块说明 * `references/operating-rules.md` — 模型拟合阈值与图表设计原则 * `references/examples.md` — 典型使用案例与命令示例 * `assets/schema.json` — 解析后的数据结构描述 ## ⚠️ 注意事项与学术规范 * 前提条件：输入文件必须包含 MODEL FIT INFORMATION 和 STDYX Standardization 模块，否则报告不完整。 * 阈值说明：工具使用 Hu & Bentler (1999) 的推荐标准，但用户需结合自身领域和样本量灵活判断。 * 显著性声明：自动判定 p<0.05 为显著，未进行多重比较校正；若模型参数较多，请自行使用 Bonferroni 或 FDR 方法。 * 后果声明：报告为 AI 辅助生成，仅供初步参考，最终统计审核应由研究者负责。 *


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: ivangdavila/cloud (version 1.0.0)

--- name: Cloud description: "Choose, organize, sync, share, and back up personal files across iCloud, Google Drive, Dropbox, and OneDrive." --- ## Triggers Activate on: iCloud full, cloud storage, backup photos, sync between devices, share folder, Google Drive help, Dropbox issues, "where are my files", storage plan comparison. ## Scope This skill covers **consumer cloud storage** — the services regular people use for photos, documents, and backups. **Not this skill:** AWS, Azure, S3 buckets, VPS, Docker, APIs → use `infrastructure`, `s3`, or `server`. ## Quick Service Picker | Your devices | Best fit | Why | |--------------|----------|-----| | iPhone + Mac | iCloud | Native integration, seamless | | Android + Chrome | Google Drive | Included with Gmail, auto photo backup | | Windows PC | OneDrive | Built into Windows, Office integration | | Mixed devices | Dropbox | Works equally well everywhere | For detailed pricing and features, see `services.md`. ## Common Confusions | What you think | What's actually happening | |----------------|---------------------------| | "I deleted it from my phone and now it's gone from my laptop" | Sync works as designed — one file, everywhere | | "iCloud storage full but my phone has space" | Phone storage ≠ iCloud storage | | "My photos are duplicated everywhere" | Multiple services backing up the same camera roll | | "I pay for 3 cloud services" | Pick one primary, cancel the rest | ## Storage Full — What to Do 1. **Check what's using space** — Photos usually dominate 2. **Empty trash** — Deleted files count until trash is emptied 3. **Disable duplicate backups** — Pick one photo backup service 4. **Offload old files** — Move archives to external drive For service-specific cleanup steps, see `cleanup.md`. ## Backup Strategy - **3-2-1 rule:** 3 copies, 2 different media, 1 offsite - **Cloud counts as offsite** — but also keep a local backup - **Check backup status monthly** — don't assume it's working What to back up and what NOT to store in cloud: see `backup.md`. ## Sharing Files | Need | Method | |------|--------| | Quick share with anyone | Link (set expiration) | | Ongoing family access | Shared folder | | Sensitive documents | Don't use cloud, or encrypt first | Step-by-step per service: see `sharing.md`. ## Security Basics - **Enable 2FA** on all cloud accounts - **Review shared links** quarterly — revoke old ones - **Don't store unencrypted:** passwords, IDs, financial documents


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tanguyvans/bambu-local (version 1.0.1)

--- name: bambu-local description: Control Bambu Lab 3D printers locally via MQTT (no cloud). Supports A1, A1 Mini, P1P, P1S, X1C. homepage: https://github.com/Doridian/OpenBambuAPI metadata: {"clawdbot":{"emoji":"🖨️","requires":{"bins":["python3"]}}} --- # Bambu Local - 3D Printer Control Control Bambu Lab printers locally via MQTT without cloud dependency. ## Setup 1. Create virtual environment: ```bash python3 -m venv ~/bambu-env source ~/bambu-env/bin/activate pip install paho-mqtt ``` 2. Create `config.json` in skill folder: ```json { "printer_ip": "192.168.x.x", "access_code": "xxxxxxxx", "serial": "xxxxxxxxxxxx", "printer_name": "MyPrinter" } ``` Get these from your printer: Settings → LAN Only Mode (access code) and Settings → Device (serial). ## Commands ### Status ```bash run ~/clawd/skills/bambu-local/bambu status ``` ### Light ```bash run ~/clawd/skills/bambu-local/bambu light on run ~/clawd/skills/bambu-local/bambu light off ``` ### Print Control ```bash run ~/clawd/skills/bambu-local/bambu print pause run ~/clawd/skills/bambu-local/bambu print resume run ~/clawd/skills/bambu-local/bambu print stop ``` ### Speed (1=Silent, 2=Standard, 3=Sport, 4=Ludicrous) ```bash run ~/clawd/skills/bambu-local/bambu speed 2 ``` ### Temperature ```bash run ~/clawd/skills/bambu-local/bambu temp --bed 60 run ~/clawd/skills/bambu-local/bambu temp --nozzle 200 ``` ### G-code ```bash run ~/clawd/skills/bambu-local/bambu gcode "G28" ``` ## Supported Printers - Bambu Lab A1 / A1 Mini - Bambu Lab P1P / P1S - Bambu Lab X1 / X1C


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: broedkrummen/fast-unified-memory (version 1.0.1)

# Skill: Fast Unified Memory A high-performance unified memory system that integrates OpenClaw memory with semantic memory storage using Ollama's nomic-embed-text model for ultra-fast embeddings. ## Overview This skill provides a unified memory layer that combines: - **OpenClaw Memory**: Standard file-based memory storage - **Semantic Memory**: Vector-based memory using Ollama embeddings ## Features - ⚡ **Ultra-fast**: ~130ms for combined search (embedding ~40ms + search ~90ms) - 🔒 **Private**: All processing done locally via Ollama - 💰 **Free**: No API costs - uses local Ollama instance - 🧠 **Semantic**: Uses nomic-embed-text for intelligent similarity matching ## Requirements - [Ollama](https://ollama.ai) installed and running - `nomic-embed-text` model pulled: `ollama pull nomic-embed-text` ## Installation ```bash # Install Ollama first curl -fsSL https://ollama.ai/install.sh | sh # Pull the embedding model ollama pull nomic-embed-text # Start Ollama ollama serve ``` ## Usage ### Commands ```bash # Search both memory systems node fast-unified-memory.js search "your query" # Add a memory node fast-unified-memory.js add "User prefers concise responses" # List all memories node fast-unified-memory.js list # Show system stats node fast-unified-memory.js stats ``` ## Architecture ``` ┌─────────────────────────────────────────────┐ │ FAST UNIFIED MEMORY │ │ │ │ ┌─────────────┐ ┌─────────────┐ │ │ │ OpenClaw │ │ Semantic │ │ │ │ Memory │ │ Memory │ │ │ │ (files) │ │ (vectors) │ │ │ └─────────────┘ └─────────────┘ │ │ ↓ ↓ │ │ [Keyword Match] [Cosine Similarity] │ │ │ │ Unified Results (ranked) │ └─────────────────────────────────────────────┘ ``` ## Performance | Metric | Value | |--------|-------| | Embedding generation | ~40ms | | Vector search | ~50ms | | File search | ~40ms | | **Total search** | **~130ms** | ## Configuration The skill uses these defaults: - Ollama URL: `http://localhost:11434` - Embedding model: `nomic-embed-text` - Memory storage: `~/.mem0/fast-store.json` - OpenClaw memory: `~/.openclaw/workspace/memory/` ## Files - `fast-unified-memory.js` - Main CLI tool - `SKILL.md` - This documentation ## Troubleshooting **Ollama not running:** ```bash ollama serve ``` **Model not found:** ```bash ollama pull nomic-embed-text ``` **Port conflict:** The skill assumes Ollama is on port 11434. Update the `OLLAMA_URL` constant if using a different port. ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: scottkiss/pdf2word-skills (version 1.0.0)

--- name: pdf2word-skills description: Convert scanned PDF documents into Word text documents using a free, local OCR engine or remote api. --- # PDF to Word Converter [🇨🇳 简体中文 / Simplified Chinese](README_zh.md) A skill to extract text from scanned PDF documents and convert them into reusable Word (`.docx`) files using the free, local `docr` OCR engine. ## Prerequisites 1. Initialize the OCR engine by downloading the binaries: ```bash bash scripts/install.sh ``` 2. Install the required Python dependencies: ```bash pip install -r scripts/requirements.txt ``` ## Usage Run the Python script passing the input PDF file and the desired output `.docx` file path. You can also append any additional standard `docr` arguments (such as engine preferences). ```bash python scripts/pdf2word.py <input.pdf> <output.docx> [docr_args...] ``` ### Examples Convert a single file with the default local engine: ```bash python scripts/pdf2word.py sample.pdf sample_output.docx ``` ### Using Other API Engines By default, the script uses the local `RapidOCR` engine. The underlying `docr` tool also supports other engines like the Google Gemini API for potentially higher recognition accuracy on complex layouts. To use Gemini, first configure your API key: ```bash mkdir -p ~/.ocr echo "gemini_api_key=your_gemini_key" > ~/.ocr/config ``` Then pass the `-engine gemini` argument to the script: ```bash python scripts/pdf2word.py sample.pdf sample_output.docx -engine gemini ``` If your document has **tables**, you can force Gemini to output them in Markdown format so the script can parse them into native Word tables: ```bash python scripts/pdf2word.py sample.pdf sample_output.docx -engine gemini -prompt "Extract all text and preserve tables in Markdown format using | symbols." ``` ### How it Works 1. The script calls `docr`, which uses the specified OCR model (RapidOCR by default) to read text from the scanned PDF. 2. The extracted text is temporarily stored. 3. The `python-docx` library is used to read the temporary text and construct a formatted Word document. 4. Temporary files are cleaned up automatically.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: blueworldmarketing/gog-bwm (version 1.0.0)

--- name: gog description: Google Workspace CLI for Gmail, Calendar, Drive, Contacts, Sheets, and Docs. homepage: https://gogcli.sh metadata: {"clawdbot":{"emoji":"🎮","requires":{"bins":["gog"]},"install":[{"id":"brew","kind":"brew","formula":"steipete/tap/gogcli","bins":["gog"],"label":"Install gog (brew)"}]}} --- # gog Use `gog` for Gmail/Calendar/Drive/Contacts/Sheets/Docs. Requires OAuth setup. Setup (once) - `gog auth credentials /path/to/client_secret.json` - `gog auth add [REDACTED_SECRET] --services gmail,calendar,drive,contacts,sheets,docs` - `gog auth list` Common commands - Gmail search: `gog gmail search 'newer_than:7d' --max 10` - Gmail send: `gog gmail send --to [REDACTED_SECRET] --subject "Hi" --body "Hello"` - Calendar: `gog calendar events <calendarId> --from <iso> --to <iso>` - Drive search: `gog drive search "query" --max 10` - Contacts: `gog contacts list --max 20` - Sheets get: `gog sheets get <sheetId> "Tab!A1:D10" --json` - Sheets update: `gog sheets update <sheetId> "Tab!A1:B2" --values-json '[["A","B"],["1","2"]]' --input USER_ENTERED` - Sheets append: `gog sheets append <sheetId> "Tab!A:C" --values-json '[["x","y","z"]]' --insert INSERT_ROWS` - Sheets clear: `gog sheets clear <sheetId> "Tab!A2:Z"` - Sheets metadata: `gog sheets metadata <sheetId> --json` - Docs export: `gog docs export <docId> --format txt --out /tmp/doc.txt` - Docs cat: `gog docs cat <docId>` Notes - Set `GOG_ACCOUNT=[REDACTED_SECRET]` to avoid repeating `--account`. - For scripting, prefer `--json` plus `--no-input`. - Sheets values can be passed via `--values-json` (recommended) or as inline rows. - Docs supports export/cat/copy. In-place edits require a Docs API client (not in gog). - Confirm before sending mail or creating events.

path: _meta.json
content: ownerId: kn70w7wx43p52z66v9j5nfgg5580tg1y
slug: gog-bwm
version: 1.0.0
publishedAt: 1771476118427

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nabssku/spongo (version 1.0.0)

--- name: spotify-player description: Terminal Spotify playback/search via spogo (preferred) or spotify_player. homepage: https://www.spotify.com metadata: {"clawdbot":{"emoji":"🎵","requires":{"anyBins":["spogo","spotify_player"]},"install":[{"id":"brew","kind":"brew","formula":"spogo","tap":"steipete/tap","bins":["spogo"],"label":"Install spogo (brew)"},{"id":"brew","kind":"brew","formula":"spotify_player","bins":["spotify_player"],"label":"Install spotify_player (brew)"}]}} --- # spogo / spotify_player Use `spogo` **(preferred)** for Spotify playback/search. Fall back to `spotify_player` if needed. Requirements - Spotify Premium account. - Either `spogo` or `spotify_player` installed. spogo setup - Import cookies: `spogo auth import --browser chrome` Common CLI commands - Search: `spogo search track "query"` - Playback: `spogo play|pause|next|prev` - Devices: `spogo device list`, `spogo device set "<name|id>"` - Status: `spogo status` spotify_player commands (fallback) - Search: `spotify_player search "query"` - Playback: `spotify_player playback play|pause|next|previous` - Connect device: `spotify_player connect` - Like track: `spotify_player like` Notes - Config folder: `~/.config/spotify-player` (e.g., `app.toml`). - For Spotify Connect integration, set a user `client_id` in config. - TUI shortcuts are available via `?` in the app.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minstn/usememos-api (version 1.0.3)

--- name: usememos-api version: "1.0.3" description: Interact with UseMemos instance — a lightweight, self-hosted memo hub. Create, search, list memos and upload attachments. tags: ["memos", "notes", "self-hosted", "knowledge-base", "attachments"] homepage: https://github.com/minstn/usememos source: https://github.com/minstn/usememos metadata: openclaw: requires: env: - USEMEMOS_URL - USEMEMOS_TOKEN bins: - python3 primaryEnv: USEMEMOS_TOKEN --- # UseMemos ## Setup Create a `.env` file in the skill directory (`skills/usememos/.env`): ``` USEMEMOS_URL=http://192.168.0.157:5230 USEMEMOS_TOKEN=your_access_token_here ``` Get your ```USEMEMOS_TOKEN``` from UseMemos instance, login and go to: **Settings > My Account > Access Tokens**, create one there, do not forget to assign expiration (i use **Never** to avoid troubles, but hey there are also arguments against that) **Note:** All scripts automatically load the `.env` file from the skill directory. No need to export variables manually. ## Scripts | Script | Usage | Description | |--------|-------|-------------| | `create_memo.py` | `<content> [visibility]` | Create a memo (visibility: PRIVATE/PROTECTED/PUBLIC) | | `list_memos.py` | `[limit] [tag]` | List recent memos (default: 10) | | `search_memos.py` | `<query> [limit]` | Search memos by content | | `upload_attachment.py` | `<filepath> [filename] [type]` | Upload a file attachment | | `upload_and_link_attachment.py` | `<memo_id> <filepath> [filename] [type]` | Upload and link attachment to a memo | | `memo_comments.py` | `<list\|add\|delete> [args...]` | Manage comments on a memo | All scripts are in `scripts/` and run with `python3 scripts/<script>`. ## Examples ```bash # Create a memo with tags python3 scripts/create_memo.py "Meeting notes from standup #work" # List recent memos python3 scripts/list_memos.py 5 # Search memos python3 scripts/search_memos.py "website redesign" # Upload attachment standalone python3 scripts/upload_attachment.py photo.jpg "sale_photo.jpg" "image/jpeg" # Create memo then attach a file python3 scripts/create_memo.py "Sale: Stromer Charger #eBike #income" # Use the memo ID from output: python3 scripts/upload_and_link_attachment.py <memo_id> charger_photo.jpg # Add a comment to a memo python3 scripts/memo_comments.py add <memo_id> "Looks great!" # List comments on a memo python3 scripts/memo_comments.py list <memo_id> # Delete a comment python3 scripts/memo_comments.py delete <comment_id> ``` ## Notes - Memo IDs are short IDs like `3UZ7uBbHsLEAwdYE5HKhjd` (not `memos/3UZ7uBbHsLEAwdYE5HKhjd`) - Tags use `#tag` syntax inline in memo content - Default MIME type for attachments is `image/jpeg`; pass the type explicitly for other files. Normally this will be done automatically by openclaw, but sometimes will require some tweaking/ ## API Reference See [references/api.md](references/api.md) for endpoint documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wavmson/ctx-doctor (version 1.0.0)

--- name: context-doctor description: "上下文诊断。随时检查当前会话的健康状态——context 使用率、token 消耗趋势、哪些工具调用最占空间、预估还能聊多少轮、给出优化建议。触发词：体检、诊断、context doctor、健康检查、token 用了多少、还能聊多久、会话状态。" --- # Context Doctor — 上下文诊断 给你的对话做一次全面体检。 ## 什么时候用 - 用户说"体检"、"诊断"、"context doctor" - 对话感觉变慢时（可能接近 context 上限） - 长时间工作后想了解消耗情况 - 决定是否需要 compact 之前 - 定期检查（建议每 20-30 轮对话检查一次） ## 核心理念 每次对话都有一个隐形的"油箱"—— context window。 用着用着就满了，但你看不到油量表。满了之后要么报错，要么被强制压缩丢信息。 Context Doctor 就是这个油量表： - 告诉你用了多少、还剩多少 - 找出最占空间的"油耗大户" - 预测还能跑多远 - 建议什么时候该"加油"（compact） ## 执行流程 ### Step 1 — 采集数据（Collect） 通过 `session_status` 获取当前 session 的实时数据： - **总 context window 大小**（如 200,000 tokens） - **当前已使用 tokens**（输入 + 输出） - **本次 session 消耗**（输入 tokens、输出 tokens、费用） - **模型信息**（当前使用的模型和配置） ### Step 2 — 分析对话结构（Analyze） 回顾当前对话，统计： - **总轮次**：用户和 Agent 各说了多少轮 - **工具调用次数**：exec、read、write、web_search 等各多少次 - **工具输出大小排行**：哪些工具调用返回了最多内容 - **最大单次输出**：最占空间的那次工具调用是什么 - **重复内容检测**：是否有多次读取同一文件等冗余操作 ### Step 3 — 健康评估（Diagnose） 根据采集的数据生成健康评分： | 指标 | 健康 🟢 | 注意 🟡 | 危险 🔴 | |------|---------|---------|---------| | Context 使用率 | <50% | 50-80% | >80% | | 单轮平均 tokens | <2000 | 2000-5000 | >5000 | | 工具输出占比 | <60% | 60-80% | >80% | | 预估剩余轮次 | >30 | 10-30 | <10 | 综合评分： - **A（优秀）**：所有指标绿灯，可以放心继续 - **B（良好）**：个别黄灯，留意但不急 - **C（注意）**：多个黄灯或一个红灯，建议准备 compact - **D（警告）**：多个红灯，强烈建议立即 compact - **F（危险）**：随时可能触发强制压缩 ### Step 4 — 生成报告（Report） 输出可视化诊断报告： ``` 🏥 Context Doctor 诊断报告 ━━━━━━━━━━━━━━━━━━━━━ 📊 综合评分：B（良好） 🔋 Context 油量 ████████████░░░░░░░░ 58% (116,000 / 200,000) 📈 消耗统计 ├─ 对话轮次：23 轮 ├─ 输入 tokens：89,000 ├─ 输出 tokens：27,000 ├─ 本次费用：$0.42 └─ 平均每轮：5,043 tokens 🔍 空间占用 TOP 5 1. exec(cat large-file.log) — 12,300 tokens（10.6%） 2. web_fetch(docs.openclaw.ai) — 8,200 tokens（7.1%） 3. read(MEMORY.md) — 4,500 tokens（3.9%） 4. exec(git log --oneline) — 3,800 tokens（3.3%） 5. web_search("OpenClaw skills") — 2,100 tokens（1.8%） ⚠️ 发现问题 - exec 输出占总 context 的 42%，建议对大输出使用 head/tail 截断 - MEMORY.md 被读取 3 次，考虑缓存到对话上下文中 🔮 预测 ├─ 预估剩余：约 17 轮对话 ├─ 建议 compact 时机：再聊 10 轮后 └─ 如果继续当前节奏：约 35 分钟后达到 80% 💡 优化建议 1. exec 命令加 | head -50 限制输出长度 2. 大文件用 read 的 offset/limit 参数分段读取 3. 考虑在下一个自然断点执行 compact ``` ### Step 5 — 趋势追踪（Trend） 如果多次执行诊断，对比历史数据： ``` 📈 趋势（最近 3 次诊断） 诊断1 (14:00): 32% → B 诊断2 (15:30): 58% → B 诊断3 (16:45): 74% → C ⬆️ 消耗速度：约 28% / 小时 加速中 ⚠️（工具调用频率增加） ``` ## 规则 - **数据准确**：所有数字基于 session_status 真实数据，不估算不猜测 - **建议温和**：给建议但不强制，用户决定是否 compact - **不消耗太多**：诊断本身要轻量，不要因为诊断反而加速 context 膨胀 - **可视化优先**：用进度条和 emoji 让报告直观易读 - **隐私安全**：报告不包含对话具体内容，只统计结构和大小 ## 与其他 Skill 配合 - **Smart Compact**：Context Doctor 诊断后建议 compact → Smart Compact 安全执行 - **Hook Guard**：诊断结果触发 Yellow 级别备份（保存当前对话快照） - **Memory-Dream**：诊断数据是 Dream 整合时的参考（哪些 session 消耗最大） - **Session Resume**：诊断中发现即将 compact 时，提醒保存任务状态


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)