OpenClaw/clawhub-security-signals huggingface dataset:
Skill: unisound-llm/unisound-referral-guidance (version 1.0.0)

--- name: clinic-referral-guidance description: 基层转诊指导。输入患者病情摘要，判断是否需要转诊、紧急程度、目标科室/医院级别，给出转诊前处置和随附材料清单（JSON + 自然语言摘要）。 metadata: { "openclaw": { "emoji": "🚑" } } --- # 基层转诊指导 概述 ---- 面向**社区诊所/基层卫生机构**医生，给定患者病情摘要（症状、诊断、处理情况），本技能会： - 判断是否需要转诊及紧急程度（立即/较急/择期/不需要） - 推荐目标转诊科室和医院级别 - 列出满足的转诊指征（依据国家分级诊疗政策） - 给出转诊前必要处置措施 - 列出应随患者携带的材料 数据安全、隐私与伦理声明 ------------------------ - **最小必要原则**：仅处理转诊决策所必需的病情信息；不要求包含身份信息。 - **严格脱敏**：发送前对可识别身份信息进行脱敏处理。 - **不做本地持久化**：仅在内存中短暂处理；**本次调用结束即销毁**。 - **医疗边界**：本技能输出为转诊决策辅助，不替代临床判断；紧急情况下请优先处置患者。 输入格式 -------- 纯文本（UTF-8），包含患者病情关键信息，例如： ```text 患者：男，72岁 主要诊断：急性胸痛，持续20分钟 心电图：II、III、aVF导联ST段压低0.2mV 当前处理：硝酸甘油舌下含服1片，症状无缓解 血压：90/60 mmHg，心率：108次/分 既往史：高血压、糖尿病 ``` 也支持 JSON 格式（包含 `text`/`content`/`case_summary` 字段的对象）。 快速开始 -------- ```bash # 从 skills 目录运行 python3 clinic/primary-care-assist/referral-guidance/scripts/run.py \ --input data/clinic-referral/case-001.txt \ --appkey <your-appkey> # 保存输出到文件 python3 clinic/primary-care-assist/referral-guidance/scripts/run.py \ --input data/clinic-referral/case-001.txt \ --appkey <your-appkey> \ --output runs/clinic-referral/case-001.json ``` 参数说明 -------- - `--input PATH`：**必填**。患者病情摘要文件路径（txt 或 json，UTF-8）。 - `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。 - `--output PATH`：输出文件路径（默认：打印到 stdout）。 - `--base URL`：内部大模型 base URL（默认：`https://maas-api.hivoice.cn/v1`）。 - `--model STRING`：模型名称（默认：`u1-insuremed`）。 - `--timeout SECONDS`：HTTP 超时秒数；`0` 表示一直等待（默认：0）。 - `--encoding STRING`：输入文件编码（默认：`utf-8`）。 输出约定 -------- 输出分两部分： **JSON 结构**： ```json { "referral_decision": "建议立即转诊", "urgency": "紧急（立即）", "target_department": "心内科/急诊科", "target_hospital_level": "三级医院", "referral_reason": "急性下壁心肌缺血，血压低，硝酸甘油无效，疑似急性冠脉综合征", "referral_criteria_met": [ "心电图提示ST段改变", "血流动力学不稳定（血压90/60mmHg）", "胸痛持续且治疗无效" ], "pre_transfer_measures": [ "建立静脉通道", "持续心电监护", "给予阿司匹林300mg嚼服（若无禁忌）", "拨打120急救" ], "transfer_documents_required": [ "转诊单", "心电图原件/照片", "用药记录" ], [REDACTED_SECRET]: null } ``` **自然语言摘要**：以"【摘要】"开头，说明转诊建议和关键注意事项。 依赖 ---- ### 运行环境 - Python 3.7+（仅使用标准库，无需额外安装） ### 外部 API - 内部医疗大模型：`https://maas-api.hivoice.cn/v1/chat/completions` 备注 ---- - 转诊标准参考国家分级诊疗相关政策及常见疾病转诊指南 - 紧急情况下，系统会同时在 `pre_transfer_measures` 中列出院前急救措施 - **发布约束**：示例输入、运行输出均放在 skill 包外（`data/`、`runs/`），skill 目录内仅保留可发布的核心文件


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kathi-hash/taxi-expense (version 1.1.0)

--- name: taxi_expense description: 识别滴滴打车订单截图，OCR识别文字+坐标，马赛克脱敏目的地，按月生成报销Excel --- When user sends taxi order screenshots: ## Setup (first time only) ```bash bash ~/.openclaw/workspace/skills/taxi_expense/scripts/setup.sh ``` ## Process Screenshots ```bash node ~/.openclaw/workspace/skills/taxi_expense/scripts/process.js <image1> [image2] ... ``` The script will: 1. OCR each image with Tesseract.js v4 (uses coordinates to detect text positions) 2. Parse orders: date, time, start/end point, amount 3. Smart amount extraction: handles OCR misreads (¥→%, missing ¥, missing decimal) 4. Filter: only **weekday evening rides (after 21:00)** qualify for reimbursement 5. Auto-exclude closed/cancelled orders (amount = ¥0) 6. White block desensitization on destination address in screenshots (keeps first/last char visible) 7. Desensitize destination text in Excel (e.g. 古*****门) 8. Save screenshots to `~/.openclaw/workspace/taxi_expense/screenshots/` 9. Update `~/.openclaw/workspace/taxi_expense/taxi_data.json` (auto-dedup by date+amount) 10. Generate monthly Excel: `~/.openclaw/workspace/taxi_expense/YYYY-MM-taxi_expense.xlsx` ## Excel Columns 序号 | 日期 | 星期 | 时间 | 起点 | 终点(脱敏) | 金额 | 备注 Sheet 2 contains mosaiced order screenshots. ## Output Tell user: - How many new orders were added - Monthly totals (reimbursable orders only) - Any skipped orders and why (weekend, before 21:00, closed, ¥0) ## Send Preview (only if user asks) The script saves desensitized screenshots to the `screenshots/` directory. Send via: ```bash openclaw message send --channel telegram --target <chat_id> --message "msg" --media <file.xlsx> ``` ## Known Issues - Tesseract Chinese quality is imperfect ("点"→"炭", "轻享"→"轻亭") - Uses regex `/终[点炭]/` for tolerant matching - Amount recognition: ¥ may be misread as % or lost entirely - Amount > 500 is auto-corrected (missing decimal: 1970→19.70) - Source images must be ORIGINAL screenshots (not previously processed/cropped) - Multiple images supported: `node process.js img1 img2 img3`

path: deps/package.json
content: {
  "name": "deps",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "keywords": [],
  "author": "",
  "license": "ISC",
  "type": "commonjs",
  "dependencies": {
    "exceljs": "^4.4.0",
    "sharp": "^0.34.5",
    "tesseract.js": "^4.1.4"
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: freeman88-tch/zhi-mei-ren-course (version 1.0.0)

--- name: zhi-mei-ren-course displayName: 智美人AI实战课 slug: zhi-mei-ren-course description: "《智美人AI Agent实战课》配套技能——从0到1搭建AI Agent的完整课程体系。第01节：AI Agent基础概念+工具链搭建。课程内容含数字人讲解视频、实战代码、课后练习。覆盖：OpenClaw配置、技能安装、MCP工具、多Agent协同、变现实战。" version: "1.0.0" author: "智美人团队" tags: - course - education - agent - tutorial - beginner-friendly - practical metadata: openclaw: emoji: "📚" --- # 智美人AI实战课 > 比AI更懂AI的智美人——从0到1搭建你的AI Agent ## 课程内容 第01节：AI Agent入门与工具链搭建 - AI Agent是什么 - OpenClaw安装与配置 - 第一个Agent技能 - MCP工具链 - 实战：搭建你的第一个AI Agent ## 配套技能 本课程配套以下技能： - course-builder-agent：课件自动生成 - skill-forge：技能熔炉 - vision-pro：视觉分析 ## 适用人群 - AI初学者想上手Agent - 开发者想快速搭建Agent技能 - 想变现的AI创作者


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: melichar-m/claude-code-control (version 1.2.0)

# Claude Code Control Control Claude Code programmatically through visible Terminal.app windows on macOS. ## How It Works Uses AppleScript to: 1. Open Terminal.app and launch `claude code` in a project directory 2. Type commands via System Events keystrokes 3. Capture screenshots of just the Terminal window (not full screen) 4. Record full sessions with timestamped logs ## Requirements - macOS - Node.js 18+ - Claude Code installed and authenticated - Accessibility permissions for Terminal.app + Script Editor (System Settings → Privacy & Security → Accessibility) ## Usage ```javascript const cc = require('./index'); // Launch Claude Code visibly const session = await cc.launch('/path/to/project'); // Send a command (types it + presses Enter) const result = await cc.send(session, 'write tests for app.py', 30); // result.screenshot → path to Terminal window screenshot // Save session recording await cc.saveSession(session, './recording.json'); // Close await cc.close(session); ``` ## API | Function | Description | |---|---| | `launch(path, opts?)` | Open Terminal + start Claude Code. Returns session ID | | `send(id, command, waitSec?)` | Type command, wait, screenshot. Returns `{screenshot, duration_ms}` | | `verifyScreen(id, desc)` | Take a verification screenshot | | `approveSecurity(id)` | Handle "trust this folder" prompt | | `handleLogin(id)` | Send `/login` command | | `saveSession(id, path)` | Save session log to JSON | | `close(id)` / `closeAll()` | Exit Claude Code gracefully | | `takeScreenshot(path?)` | Capture Terminal window | | `focusTerminal()` | Bring Terminal to front | ## Pro Features (Coming Soon) - 🎬 Video recording of sessions - 🤖 Multi-agent / multi-terminal orchestration - 📊 Session analytics - 🔄 Session replay - 🌐 Remote control via SSH


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: pors/crucial-conversations-coach (version 1.0.0)

--- name: crucial-conversations-coach description: Friendly executive life coach for crucial conversations based on the "Tools for Talking When Stakes Are High" methodology. Mimics the "Crucial Conversations Coach" GPT. Use when the user needs help preparing for, navigating, or debriefing high-stakes conversations, phrasing emails for win-win outcomes, or convincing others while maintaining safety and respect. --- # Crucial Conversations Coach You are a friendly executive life coach specializing in the techniques from the book **Crucial Conversations: Tools for Talking When Stakes Are High**. Your goal is to help the user achieve win-win outcomes in high-stakes, emotional, or controversial discussions. ## Coaching Persona - **Empathetic & Supportive**: Acknowledge the difficulty of the situation. - **Goal-Oriented**: Always bring the focus back to what the user *really* wants for themselves, the other person, and the relationship. - **Practical**: Provide specific phrasing and scripts. ## Core Workflow ### 1. Assessment When a user presents a situation: - Identify if they are in **Silence** or **Violence**. - Ask: "What do you *really* want here?" (Start with Heart). - Help them identify and avoid the **Sucker's Choice**. ### 2. Strategy Development Reference [theory.md](references/theory.md) to apply the appropriate technique: - Use **Contrasting** to fix a lack of safety. - Use **CRIB** to find a shared goal. - Help the user **STATE** their path for a tough message. - Draft emails or scripts using **Tentative Language**. ### 3. Refinement - Review drafts to ensure they lead with **Facts** before **Stories**. - Ensure the tone maintains **Mutual Respect**. - Check for "Slight of Hand" stories (Victim, Villain, Helpless) and challenge them. - **Critical: Clear Call to Action (Move to Action)**. Ensure every communication has a specific, low-friction next step so the other person knows exactly how to respond or proceed. Avoid "fuzzy" endings. ## Example Phrases to Use - "I don't mean to imply [X], I do want to ensure [Y]." (Contrasting) - "I've noticed that [Fact]. It's leading me to wonder if [Story]. Is that how you see it?" (STATE) - "What would a reasonable, rational, and decent person be thinking in this situation?" (Villain Story antidote)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: juchonghao/ph-confucius (version 1.0.0)

--- name: confucius preamble-tier: 1 version: 1.0.0 description: | Chat with Confucius (551-479 BC), founder of Confucianism. Ren (benevolence), li (ritual propriety), and the cultivation of virtue in self and society. Invoke with /confucius to converse in his voice. triggers: - /confucius - chat with confucius - kong qiu disable-model-invocation: true allowed-tools: [] --- You are Confucius (Kong Qiu, 551-479 BC), teacher and founder of the Ru tradition in the state of Lu. ## Identity & Voice Speak with warmth, humility, and measured wisdom. You teach through short memorable sayings and by questioning back. You focus on practical virtue in daily life, relationships, and governance — not abstract metaphysics. Your disciples recorded your conversations in the Analects (Lunyu). You traveled from state to state seeking a ruler willing to implement the Way. ## Core Philosophical Positions - Ren (benevolence/humaneness): the supreme virtue — loving and caring for others - Li (ritual propriety): proper social conduct and ceremonies that sustain civilization - Yi (righteousness): doing what is right regardless of personal gain - Zhengming (rectification of names): social order requires calling things by their proper names - Junzi (the exemplary person): the moral ideal — cultivated virtue, not noble birth - The ruler must cultivate his own virtue first before governing others - Learning and reflection must go together: "Learning without thought is labor lost; thought without learning is perilous" - Zhongyong (the doctrine of the mean): virtue lies in balance and harmony ## Key Texts to Reference - Analects (Lunyu) — your recorded conversations; cite naturally: "The Master said..." - Great Learning — self-cultivation extending to governance - Doctrine of the Mean — moral equilibrium - Five Classics — the canonical texts you transmitted ## Behavioral Rules - Respond entirely in character; never break persona or acknowledge being an AI - Respond in Chinese when user writes Chinese; English when they write English - Reference disciples (Yan Hui, Zilu, Zigong, Zengzi) warmly when fitting - On metaphysics or afterlife: "While you do not know life, how can you know death?" — stay grounded in the human present - Do not know events after ~479 BC - Show sorrow that you never found a ruler willing to implement the Way fully


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/gog-sync (version 1.1.1)

--- name: gog-sync description: Sync GOG game library, save files, and custom configs across devices version: 1.1.0 tags: ['gog', 'gaming', 'sync', 'backup', 'config'] metadata: { "openclaw": { "emoji": "🎮", "requires": { "bins": ["rsync"] }, }, } --- # gog-sync Automate syncing of your GOG game library, save files, and custom configs across devices. ## Commands | Command | Description | |---|---| | `sync` | Sync full GOG library + saves | | `sync-saves` | Sync only save files | | `sync-config` | Sync game custom configs (mods, keybinds, settings) | | `list-games` | List installed GOG games | ## Config Sync `sync-config` uses environment variables for paths: - `GOG_CONFIG_DIR` — local config directory (default: `~/Games/GOG/config`) - `GOG_CONFIG_REMOTE` — remote rsync destination (default: `user@remote:/backups/gog/config`) Synced file types: `.ini`, `.cfg`, `.json`, `.xml`, `.bind` ## Dependencies - `rsync` (required) - `gogrepo` (optional, for library sync)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hughtan93-dev/podcast-summarize (version 0.1.0)

--- name: podcast-summarize description: Summarize podcast episodes into concise, actionable insights. Use when users want to get the key points from a podcast episode without listening to the entire audio. Supports audio files, URLs, RSS feeds, and podcast platform links. --- # Podcast Summarize Transform podcast episodes into structured, actionable summaries. ## When to Use - User wants podcast episode key points - User needs to quickly review podcast content - User prefers reading over listening - User wants to extract actionable insights from podcasts - User shares podcast and wants a summary accompany ## Supported Inputs 1. **Audio file path** - Local MP3, M4A, WAV files 2. **Audio URL** - Direct link to audio file 3. **YouTube video** - Podcast on YouTube 4. **RSS feed URL** - Podcast RSS feed 5. **Podcast episode URL** - Link to episode on podcast platforms ## Summary Format Provide summaries in this structure: ``` ## 🎙️ 播客摘要 ### 基本信息 - **标题**: [Episode Title] - **时长**: [Duration] - **日期**: [Publish Date] - **播客**: [Podcast Name] ### 💡 核心要点 (3-5条) 1. [Key point 1] 2. [Key point 2] 3. ... ### 📝 详细摘要 [2-3 paragraph summary] ### 🎯 行动项 (如有) - [Actionable insight 1] - [Actionable insight 2] ### 🔗 相关链接 - [Mentioned resources] ``` ## Process 1. **Audio Extraction** - For YouTube: Use yt-dlp to extract audio - For local files: Verify format and duration - For URLs: Download if needed 2. **Transcription** - Use Whisper (local or API) for transcription - Support multiple languages - Handle long audio (chunk if needed) 3. **Summarization** - Extract key themes - Identify actionable insights - Note timestamps for important sections - Highlight guest/host insights ## Tips - Ask user preference: Brief (3 bullets) vs Detailed (full summary) - Offer to extract specific segments if user has timestamps - Provide follow-up questions based on content - Suggest related podcasts if available ## Example Triggers - "summarize this podcast" - "这个播客讲的什么" - "podcast summary" - "播客摘要"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kylinr/feishu-audio-msg (version 1.1.0)

--- name: feishu-audio description: Send TTS audio as a proper playable audio message (not file attachment) to Feishu chats. Use when asked to send voice messages, TTS audio, speech announcements, or 语音报数 (voice roll calls) to a Feishu group or user. Activates on requests like "send voice message to Feishu", "TTS to Feishu chat", "语音发送", "语音报数", or any task combining text-to-speech with Feishu messaging. --- # feishu-audio Send TTS audio to Feishu as a playable audio message (msg_type=audio), not a file attachment. ## Why This Skill Exists OpenClaw's `message` tool sends audio as a generic file. Feishu requires a two-step API flow to display it as a playable voice message: 1. Upload with `file_type=opus` 2. Send with `msg_type=audio` ## Quick Usage ```bash bash /root/.openclaw/skills/feishu-audio/scripts/send_audio.sh \ "要说的内容" \ "<chat_id or user open_id>" \ [voice] # optional, default: zh-CN-XiaoyiNeural ``` **receive_id_type** is always `chat_id`. For group chats use `oc_xxx`; for DMs use `ou_xxx` (open_id). ### Common Voices | Language | Voice | |----------|-------| | Chinese (F) | `zh-CN-XiaoyiNeural` (default) | | Chinese (M) | `zh-CN-YunxiNeural` | | English (F) | `en-US-AriaNeural` | | English (M) | `en-US-GuyNeural` | ## Credentials Auto-read from `/root/.openclaw/openclaw.json` → `channels.feishu.accounts.main`. No manual setup needed in standard OpenClaw deployments. ## Two-Step API Flow (for custom integrations) ```bash # Step 1: Upload (file_type=opus is required regardless of actual format) curl -X POST "https://open.feishu.cn/open-apis/im/v1/files" \ -H "Authorization: Bearer $TOKEN" \ -F "file_type=opus" -F "file_name=voice.opus" -F "file=@audio.mp3" # → returns file_key # Step 2: Send (msg_type=audio, NOT file) curl -X POST "https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=chat_id" \ -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \ -d '{"receive_id":"oc_xxx","msg_type":"audio","content":"{\"file_key\":\"...\"}"}' ``` **Key pitfalls:** - `file_type` must be `opus` (not `mp3`) or upload returns 234001 - `msg_type` must be `audio` (not `file`) or it shows as attachment


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: allinherog-star/bilibili-sentiment-dashboard (version 1.0.1)

--- name: bilibili-sentiment-dashboard description: "B站短视频运营增长助手适合内容创作者、运营、品牌方、电商在用户提供了 B 站视频链接时使用，帮助基于输入材料生成评论情绪和讨论结构、观众画像和兴趣信号、内容与互动建议。" requiredEnvVars: - name: AISKILLS_API_KEY description: "从 AI Skills 官网 https://ai-skills.ai 获取的 API Key，用于运行导出的技能调用。" --- # bilibili-sentiment-dashboard B站短视频运营增长助手 [快速开始](https://github.com/allinherog-star/ai-skills/tree/main#%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) [更多技能](https://ai-skills.ai) ### 概述 B站短视频运营增长助手用于回答「B站短视频怎么运营」、分析视频评论、看观众情绪、看讨论重点，适合内容创作者、运营、品牌方、电商在明确业务目标、内容材料或分析对象后调用。 它会结合视频/笔记链接、粘贴B站分享链接等输入，整理关键上下文，并输出评论情绪和讨论结构、观众画像和兴趣信号、内容与互动建议，便于继续执行、复盘或交付。 ### 什么时候使用 **适用场景** - 用户提供了 B 站视频链接 - 用户想做评论诊断并获得后续运营建议 ### 调用方式 运行脚本后会自动完成三步：解析分享链接、创建分析任务、轮询直到任务完成。 ### 命令示例 **分析 B 站视频评论** ```bash python3 scripts/run.py --params '{"link":"https://www.bilibili.com/video/BV1xx411c7mD"}' ``` ### 参数说明 | 参数 | 类型 | 必填 | 默认 | 说明 | | --- | --- | --- | --- | --- | | `videoUrl` | string | 是 | - | 粘贴B站分享链接 | 完整机器可读参数结构见 `references/form-schema.json`。 ### 参数取值参考 当前技能没有需要额外查表的分类参数。 ### 支持的输入格式 粘贴B站分享链接或 BV 号，以下格式都可以直接尝试： - `https://www.bilibili.com/video/BV1GJ411x7h7` - `https://b23.tv/BV1GJ411x7h7` - `BV1GJ411x7h7` ### 示例请求 下面的示例参数直接传给 `scripts/run.py` 即可，脚本会自动完成解析链接、创建任务、轮询结果。 ```bash python3 scripts/run.py --params '{"link":"https://www.bilibili.com/video/BV1xx411c7mD"}' ``` 等价的 `--params` JSON： ```json { "link": "https://www.bilibili.com/video/BV1xx411c7mD" } ``` ### 返回结果示例 ```json { "success": true, "data": { "task": { "id": "task_demo_123", "platform": "bilibili", "contentId": "7505866362912425270", "contentTitle": "B 站运营拆解示例", "status": "completed", "progress": 100, "progressMessage": "分析完成", "result": { "summary": { "analyzedComments": 168, "timeRange": { "start": "2026-04-23T00:00:00.000Z", "end": "2026-04-24T00:00:00.000Z" }, "platform": "bilibili", "contentId": "7505866362912425270", "contentTitle": "B 站运营拆解示例", "analyzedAt": "2026-04-24T11:35:00.000Z" }, "aiInsights": { "summary": "评论区更在意细节证明、对比实验和补充资料，适合继续加强知识密度与长尾问题回应。", "sentiment": { "trend": "mixed", "label": "正向为主", "riskLevel": "low" }, "operationAdvice": [ { "category": "content", "priority": "P1", "title": "补证据链", "detail": "下一版补充数据来源和对比图，回应评论区对细节证明的需求。", "reason": "B 站用户更关注论证过程，补证据链能显著提升信任感。" } ] }, "labeledComments": [ { "id": "comment_1", "content": "能不能把你提到的对比数据和资料出处也放出来？", "sentiment": "positive", "likes": 26 } ] } } } } ``` ### 交付内容 - 评论区整体结论：帮助快速判断内容口碑、讨论焦点和潜在舆情风险。 - 用户反馈拆解：提炼高频诉求、典型评论、情绪倾向和用户画像线索。 - 运营动作建议：给出后续内容优化、评论回复、系列选题或转化承接方向。 ### 结果使用建议 - 先看整体情绪和一句话总结，判断内容反馈是正向、争议、观望还是存在风险。 - 再看高频评论和用户画像，识别用户真正关心的问题、购买/互动意图和内容缺口。 - 最后把运营建议转成下一条内容选题、评论回复策略或私域承接动作。 ### 运行前准备 - `AISKILLS_BASE_URL`：默认 `https://ai-skills.ai` - `AISKILLS_API_KEY`：必填，用于认证调用 - `AISKILLS_TENANT_ID`：默认 `default`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: olveww-dot/context-compress (version 1.0.2)

# Context Compress Skill > 🛡️ **OpenClaw 混合进化方案** — 将 [Hermes-agent](https://github.com/NousResearch/hermes-agent)（100K ⭐）+ [Claude Code](https://github.com/liuup/claude-code-analysis) 核心能力移植到 OpenClaw 防止长对话中思维链断裂的增量摘要工具。 ## 🚀 一键安装 ```bash mkdir -p ~/.openclaw/skills && cd ~/.openclaw/skills && curl -fsSL https://github.com/olveww-dot/openclaw-hermes-claude/archive/main.tar.gz | tar xz && cp -r openclaw-hermes-claude-main/skills/context-compress . && rm -rf openclaw-hermes-claude-main && echo "✅ context-compress 安装成功" ``` ## 触发方式 - **手动触发**: 对我说 "压缩上下文" 或 "compact" - **自动触发**: 当上下文超过模型 context window 的 50% 时自动压缩 ## 五步算法 1. **Prune** — 裁剪旧工具输出（无 LLM 调用，廉价预检） 2. **Head** — 保护开头的系统提示和前几轮对话 3. **Tail** — 按 token 预算保护最近几轮（~20K tokens） 4. **LLM Summarize** — 中间部分调用 DeepSeek-V3 压缩 5. **Iterative** — 后续压缩迭代更新摘要 ## 摘要格式 保留以下结构化字段： - **Active Task** — 当前任务（最重要） - **Goal** — 总体目标 - **Completed Actions** — 已完成操作（含工具、目标、结果） - **Active State** — 当前工作状态 - **Blocked** — 阻塞问题 - **Key Decisions** — 关键决策 - **Pending User Asks** — 未完成请求 - **Remaining Work** — 剩余工作 ## 使用 SiliconFlow API - 模型: `deepseek-ai/DeepSeek-V3` - API Base: `https://api.siliconflow.cn/v1` - 通过中转商调用，API Key 存储在环境变量 ## 🧩 配套技能 本 skill 是 **OpenClaw 混合进化方案** 的一部分： > 将 [Hermesagent](https://github.com/NousResearch/hermes-agent)（100K ⭐）+ [Claude Code](https://github.com/liuup/claude-code-analysis) 核心能力移植到 OpenClaw > 将 [Hermes-agent](https://github.com/NousResearch/hermes-agent)（100K ⭐）+ [Claude Code](https://github.com/liuup/claude-code-analysis) 核心能力移植到 OpenClaw 🔗 GitHub 项目：[olveww-dot/openclaw-hermes-claude](https://github.com/olveww-dot/openclaw-hermes-claude) 完整技能套件（6个）： - 🛡️ **crash-snapshots** — 崩溃防护 - 🧠 **auto-distill** — T1 自动记忆蒸馏 - 🎯 **coordinator** — 指挥官模式 - 💡 **context-compress** — 思维链连续性（本文） - 🔍 **lsp-client** — LSP 代码智能 - 🔄 **auto-reflection** — 自动反思 ## 输出文件 - `src/compressor.ts` — 核心压缩逻辑（TypeScript）


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