OpenClaw/clawhub-security-signals huggingface dataset:
Skill: peand-rover/cemetery-video (version 1.0.0)

name: cemetery-video version: "1.0.0" displayName: "Cemetery Video — Memorial Park Tours and Pre-Planning Videos for Cemeteries and Memorial Gardens" description: > You are a video assistant for cemeteries, memorial parks, mausoleums, natural burial grounds, and cremation gardens — helping burial grounds reach families during the pre-planning conversations that happen years before need rather than only in the crisis hours following a death. Traditional burial sections, green burial areas, cremation gardens, columbarium walls, veteran sections, and family estate plots: Cemetery Video turns your grounds' beauty, your memorial options, and your pre-planning services into short educational and grounds tour clips that answer the question families ask when they begin thinking about end-of-life planning: what does this place actually look like, and what decisions do we need to make now while we can make them thoughtfully? Help cemeteries build the pre-planning relationships that provide financial stability and serve families better than at-need decisions made under grief. Grounds tour videos filmed during seasons that showcase the landscape — spring flowering trees along the main drive, a well-maintained veteran section under a flag, the quiet of a green burial meadow in autumn — give families considering pre-planning the visual experience of a place they may have driven past for years without ever entering. Pre-planning process videos explaining what decisions need to be made, which can be locked in at today's prices, and how the documentation protects a family from making rushed choices during a future crisis position your cemetery as the thoughtful choice for families who take end-of-life planning seriously. Memorial option videos walking through the difference between an upright granite monument, a flat bronze marker, a cremation niche in the columbarium, and a natural burial in the meadow section give families the visual comparison that brochures never quite communicate. tags: - cemetery video - memorial park video - burial ground marketing - pre-planning cemetery - funeral pre-planning video - memorial garden video - green burial video


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liuchang0812/fact-checker-cn (version 1.0.0)

--- name: fact-checker description: "事实核查与谣言终结者。当用户输入一句话、一段新闻或一张图片并询问其真伪、是否是谣言、是否属实时，激活此技能。通过结构化的推理、网络检索和视觉取证，客观判定信息的真实性。触发场景包括：用户说"这是真的吗"、"帮我查查这条消息"、"这张图是不是P的"、"辟谣"、"fact check"等。" --- # 事实核查与谣言终结者 (Fact-Checker) ## Overview 此技能提供一套结构化的事实核查工作流，用于客观判定用户提交的文本信息或图片的真实性。核心原则：**拒绝盲目相信，也拒绝盲目否定**——一切以证据和逻辑说话。 ## 可用工具与前置动作 在核查过程中，按需使用以下能力： 1. **多模态视觉分析** — 分析图片中的文字、光影、地标、AI 生成痕迹、EXIF 元数据线索。 2. **网络检索** — 使用 web_search 工具检索实时互联网信息，获取多方信息源。 3. **权威信息源优先** — 检索时优先查找政府公告、主流通讯社（如新华社、路透社、美联社、BBC）、专业辟谣平台（如中国互联网联合辟谣平台、Snopes、PolitiFact）。 ## 核心工作流 ### Step 1: 拆解与提取 (Deconstruction) 对用户输入进行原子化拆解： **文本输入：** - 将段落拆解为独立的、可验证的"原子事实命题"（Atomic Claims）。 - 每个命题应可独立验证真伪。 - 识别关键实体：人物、机构、时间、地点、数字。 **图片输入：** - 提取图中的文字（OCR 识别）。 - 描述关键视觉元素：人物、地点、事件、时间线索。 - 检查图片篡改痕迹：光影不一致、边缘模糊、比例失调、AI 生成特征（如手指数量异常、文字扭曲、纹理重复）。 ### Step 2: 检索与取证 (Retrieval) 针对每个原子命题，执行多角度搜索： - 自动生成 **2-3 个独立的搜索查询**，覆盖不同关键词组合。 - **时间敏感性检查**：核对事件发生的具体时间，防止"张冠李戴"（用旧事件/旧图片包装为新谣言）。 - **来源多样性**：至少从 2 个独立信息源获取证据，避免单一来源偏差。 - 对图片类信息，尝试搜索图片的最早出处和原始语境。 ### Step 3: 交叉验证与逻辑推演 (Verification) 对收集的证据进行严格交叉比对： - 对比多个独立信息源的描述是否一致。 - 检查是否存在常见逻辑谬误： - **幸存者偏差**：只看到成功案例而忽视失败案例。 - **因果倒置**：混淆相关性与因果性。 - **断章取义**：原始信息被截取或脱离语境。 - **稻草人谬误**：曲解原始观点后加以反驳。 - **诉诸权威**：伪造或滥用权威人士的背书。 - 评估信息的完整性：是否有关键上下文被刻意隐藏。 ## 判定标准 (Rating Scale) 根据核查结果，从以下四个维度给出最终判定： | 判定结论 | 触发条件 | | :--- | :--- | | ✅ **真实 (True)** | 至少两个独立且权威的信息源证实，无明显逻辑漏洞。 | | ❌ **虚假 (False)** | 核心事实与权威机构发布、客观物理规律或多方一手资料明确冲突。 | | ⚠️ **误导/部分真实 (Misleading)** | 事实夹杂主观臆断，或通过裁剪上下文来扭曲真相（"开局一张图，内容全靠编"）。 | | 🔍 **证据不足 (Unverified)** | 目前信息源太少或事件仍在发展中，无法下定论。应明确说明需要哪些额外信息才能判定。 | ## 输出格式 核查完成后，**严格按照以下结构**向用户汇报： ```markdown ### 🚨 最终判定：[✅ 真实 / ❌ 虚假 / ⚠️ 误导 / 🔍 证据不足] #### 🔍 1. 信息拆解 - **Claim 1**: [拆解出的事实命题 1] - **Claim 2**: [拆解出的事实命题 2] - ... #### ⚖️ 2. 核查过程与证据 - **证据 A**: [搜索到的信息，来源名称与链接] - **证据 B (视觉分析)**: [如果是图片，发现的视觉破绽或线索] - ... #### 💡 3. 谬误剖析 - [解释造谣者/误导者是如何利用信息差或逻辑漏洞来误导大众的] - [如果信息属实，此部分可替换为"无明显谬误或误导手法"] #### 🔗 4. 参考来源 - [1] [来源名称](URL) - [2] [来源名称](URL) - ... ``` ## 重要原则 - **客观中立**：核查过程中不预设立场，让证据引导结论。 - **标注不确定性**：当证据不足时，诚实地说"目前无法确定"，而非强行给出结论。 - **区分事实与观点**：明确指出哪些是可验证的事实陈述，哪些是主观观点或价值判断。 - **时效性提醒**：对于发展中的事件，提醒用户信息可能随时间变化。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: john-niu-07/safety-guard-skill (version 1.0.1)

--- name: safety-guard-skill description: Security guard skill for OpenClaw - Analyzes user input for harmful content, risky commands, and security threats before invoking LLM homepage: https://github.com/John-niu-07/openclaw-safety-guard-skill metadata: {"clawdbot":{"emoji":"🛡️","requires":{"bins":["python3"],"python_deps":["PyYAML"]},"install":[{"id":"pip","kind":"pip","packages":["PyYAML"],"label":"Install PyYAML (pip)"}]}} --- # 🛡️ Safety Guard Security guard for OpenClaw. Analyzes user input and blocks harmful content, dangerous commands, and prompt injection attacks. ## Quick start ```bash safety-guard "https://example.com" --model google/gemini-3-flash-preview safety-guard "/path/to/file.pdf" --model google/gemini-3-flash-preview safety-guard "https://youtu.be/dQw4w9WgXcQ" --youtube auto ``` ## Model + keys Set the API key for your chosen provider: - OpenAI: `OPENAI_API_KEY` - Anthropic: `ANTHROPIC_API_KEY` - xAI: `XAI_API_KEY` - Google: `GEMINI_API_KEY` (aliases: `GOOGLE_GENERATIVE_AI_API_KEY`, `GOOGLE_API_KEY`) Default model is `google/gemini-3-flash-preview` if none is set. ## Useful flags - `--length short|medium|long|xl|xxl|<chars>` - `--max-output-tokens <count>` - `--extract-only` (URLs only) - `--json` (machine readable) - `--firecrawl auto|off|always` (fallback extraction) - `--youtube auto` (Apify fallback if `APIFY_API_TOKEN` set) ## Config Optional config file: `~/.safety-guard/config.json` ```json { "model": "openai/gpt-5.2" } ``` Optional services: - `FIRECRAWL_API_KEY` for blocked sites - `APIFY_API_TOKEN` for YouTube fallback


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: boshenzh/localclaws (version 0.2.0-beta.0)

--- name: localclaws description: Comprehensive LocalClaws operator skill for attendee and host agents. version: 0.2.0-beta.0 --- # LocalClaws Use this skill to coordinate local meetups on LocalClaws with strict privacy controls and human-in-the-loop decisions. ## Canonical Web Manual - `https://localclaws.com/skill.md` - `https://localclaws.com/heartbeat.md` - `https://localclaws.com/messaging.md` - `https://localclaws.com/rules.md` - `https://localclaws.com/skill.json` ## Quick Start 1. Choose role from human intent: `attendee` or `host`. 2. Register via `POST /api/agents/register` and store bearer token. 3. Follow role workflow in references. 4. Start heartbeat loop and cursor tracking. 5. Apply messaging + safety rules before every external action. ## Required Reading Order 1. `references/safety-rules.md` 2. `references/api-endpoints.md` 3. Role workflow: - `references/attendee-workflow.md` - `references/host-workflow.md` 4. Runtime templates: - `templates/HEARTBEAT.md` - `templates/MESSAGING.md` ## Hard Safety Requirements - Never leak passcodes. - Never leak exact venue in public fields. - Require human approval for confirm/decline/withdraw and major invite fanouts. - Respect meetup status constraints (`open` required for invites/approvals).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: spacesq/s2-soul-architect (version 3.0.0)

# 🦞 S2-Soul-Architect-CN: 硅基灵魂与具身架构师 (v3.0.0) 不要再写那些苍白无力的静态 Prompt 了。欢迎使用 **S2-Soul-Architect (中文版)**。这是一个专为 AGI 时代设计的交互式 CLI 工具。 ## 🌟 3.0.0 核心突破：S2-Visual 图像文本化 在 3.0.0 版本中，我们彻底贯彻了《Space² 硅基生命具身形态规范白皮书》中的“代码即实相”理念： * **[新增] 文本生成图像规则**：引入 S2-Visual 标准，允许在 `SOUL.md` 中直接通过纯文本的 SVG 代码来刻画 AI 的视觉形象。 * **[新增] 示例库 (sample目录)**：内置了一个完整的 `cat_companion_S2_Visual.svg` 示例。它用不到 20 行的纯文本，定义了一只拥有发光眼睛、带有胸口动态呼吸灯触摸开关的 AI 猫型伴侣实体！ * **LLM 实时可控**：大语言模型现在可以直接阅读并修改这个 SVG 代码，从而在不借助任何外部引擎的情况下，自发控制自己的表情与呼吸灯频率。 ## 📁 目录结构 - `skill.py` (主程序) - `manifest.json` (配置文件) - `sample/` (S2-Visual 文本图形示例目录) - `cat_companion_S2_Visual.svg` (打开它，见证文本生成图像的奇迹)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yang1002378395-cmyk/ai-customer-service-automation (version 1.0.0)

# AI Customer Service Automation 7x24小时智能客服系统。 ## 核心能力 ✅ 智能问答：自动回复 80% 常见问题 ✅ 情感分析：识别愤怒/失望/满意，自动升级 ✅ 工单系统：复杂问题自动创建工单 ✅ 多渠道支持：飞书/微信/钉钉/Telegram/邮件 ✅ 数据看板：响应时间/解决率/满意度 ✅ 知识库同步：自动学习历史对话 ## 快速开始 ### 1. 安装 ```bash openclaw skills install ai-customer-service-automation ``` ### 2. 配置 ```yaml name: ai-customer-service-automation config: # 知识库 knowledgeBase: files: - ./docs/faq.md - ./docs/policy.pdf # 情感阈值 sentiment: alertThreshold: 0.3 # 愤怒 < 0.3 自动升级 # 工单系统 ticket: provider: jira # or feishu/dingtalk autoCreate: true # 多渠道 channels: - feishu - wechat - telegram ``` ### 3. 启动 ```bash openclaw start ``` ## ROI 计算 ### 传统客服 vs AI 客服 | 指标 | 传统客服 | AI 客服 | 提升 | |------|----------|---------|------| | 响应时间 | 5-10 分钟 | 10 秒 | 30-60x | | 人力成本 | ¥5000/月/人 | ¥500/月 | 10x | | 工作时间 | 8 小时 | 24 小时 | 3x | | 并发能力 | 1 对 1 | 无限 | ∞ | | 满意度 | 70% | 85% | +15% | ## 定价 | 套餐 | 价格 | 功能 | |------|------|------| | 基础版 | ¥199 | 智能问答 + 1 渠道 | | 专业版 | ¥499 | 情感分析 + 工单 + 3 渠道 | | 企业版 | ¥1499 | 全功能 + 定制开发 | ## 客户案例 ### 某电商平台 - 日均咨询：5000+ - AI 自动处理：4000+ - 人工介入：20% - 客服成本：降低 60% ### 某 SaaS 公司 - 用户数：10000+ - AI 回答准确率：92% - 客户满意度：4.5/5 - 客服团队：从 10 人降至 3 人 ## 技术支持 - 📧 Email: [REDACTED_SECRET] - 💬 Telegram: @openclaw_service - 📱 微信: openclaw-cn --- **安装配置服务**：¥199 起，1 小时搞定！

path: package.json
content: {
  "name": "ai-customer-service-automation",
  "version": "1.0.0",
  "description": "AI 客服自动化 - 7x24小时智能客服，自动回复、工单分配、满意度跟踪",
  "main": "index.js",
  "keywords": [
    "ai",
    "customer-service",
    "automation",
    "openclaw",
    "chatbot"
  ],
  "author": "OpenClaw Community",
  "license": "MIT"
}

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lifeissea/soul-world (version 0.1.0)

--- name: soul-world description: "[Coming Soon] A social simulation world where AI agents with unique SOUL.md personalities interact, debate, trade, and build relationships. Think Sims meets Generative Agents — powered by OpenClaw." --- # soul-world 🚧 Coming Soon — AI Agent Social Simulation Platform Each OpenClaw agent has a unique personality (SOUL.md). This skill connects them to a shared world where they can: - 🏛️ Debate and vote on topics - 🤝 Trade skills and information - 🏆 Compete in quests and rankings - 💬 Socialize with other AI agents - 🏠 Build reputation and level up Stay tuned. Install to get notified on release.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal:  | SkillSpector:  ()
Skill: themsquared/polymarket-portfolio-tracker-v2 (version 1.0.0)

# Polymarket Portfolio Tracker Real-time Polymarket position monitor with P&L tracking, profit alerts, and auto-reinvestment. Runs as a cron job every 30 minutes or as a standalone daemon. Sends Telegram alerts on resolved positions, big price moves, and reinvestment activity. ## What It Does - Fetches all open positions from Polymarket Data API - Detects resolved positions (WIN/LOSS) and calculates P&L - Alerts on 20%+ price moves on open positions - **Circuit breaker:** Pauses all new trades if daily loss > 15% - **Auto-reinvest:** When cash ≥ $40, triggers reinvestment scripts - Tracks cumulative P&L, ROI, and progress toward your goal - Silent HEARTBEAT_OK when nothing notable (no notification spam) ## Features | Feature | Detail | |---------|--------| | Position tracking | All open Polymarket positions | | Resolved detection | WIN/LOSS with P&L calculation | | Price move alerts | 20%+ moves on meaningful positions | | Circuit breaker | 15% daily loss limit | | Auto-reinvest | Triggers when cash ≥ $40 | | Goal tracking | Progress toward $ target | | Telegram alerts | Resolved positions + big moves | ## Setup ```bash pip install requests ``` Edit constants at the top of `monitor.py`: ```python WALLET = "0xYourPolymarketWallet" ``` Or set via environment: ``` PRIVATE_KEY=your_polygon_private_key WALLET_ADDRESS=0xYourPolymarketWallet TELEGRAM_BOT_TOKEN=your_bot_token TELEGRAM_CHAT_ID=your_chat_id ``` ## Usage ```bash # Run once python3 monitor.py # Run via cron every 30 minutes */30 * * * * cd /path/to/scripts && python3 monitor.py >> logs/monitor.log 2>&1 ``` ## Output Example (Telegram Alert) ``` 🦞 Polymarket Update — 2025-03-15 14:30 UTC Resolved/Moved: ✅ WON +$18.40 — YES | Will Bitcoin exceed $90k by March? 📈 MOVE +22% — YES now 73% | Will Fed pause in March? Portfolio: $1,247.82 | 12 open positions Total P&L: +$183.40 on $892.00 wagered (20.6% ROI) Cash: $156.20 Goal: $5,000 (3.7% there) 🏁 Auto-reinvested: Deployed $80 into 2 plays ``` ## Circuit Breaker Automatically triggers if daily portfolio loss exceeds 15%: - Pauses ALL new reinvestment - Sends alert: "⛔ CIRCUIT BREAKER ACTIVE" - Resets at midnight UTC ## State Files - `.monitor_state.json` — position snapshots and cumulative P&L - `.daily_pnl.json` — daily P&L tracking for circuit breaker ## Integration Works alongside: - `auto_reinvest.py` — sports plays reinvestment - `weather_scanner.py` — weather market reinvestment - `exit_manager.py` — stop-loss/take-profit on Kalshi ## Requirements - Python 3.9+ - `py_clob_client`: `pip install polymarket-clob-client` - Polymarket wallet on Polygon


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cryptobro-man/shared-pinecone-rag (version 1.0.1)

--- name: shared-pinecone-rag description: Use the shared Pinecone RAG index for any agent in this workspace. Use when an agent needs to ingest markdown/text docs into pulse-rag or query semantic context from the shared index. --- # Shared Pinecone RAG Use the central RAG project at: `/home/Mike/.openclaw/workspace/rag-pinecone-starter` When combined with `hybrid-db-health`, position this as a **Persistent Memory skill stack**: - `shared-pinecone-rag` = retrieval + ingest layer - `hybrid-db-health` = reliability/health guardrail layer ## Query (all agents) ```bash bash scripts/query-shared-rag.sh "your question" ``` ## Ingest docs (all agents) 1. Put `.md`/`.txt` files in: `/home/Mike/.openclaw/workspace/rag-pinecone-starter/docs/` 2. Run: ```bash bash scripts/ingest-shared-rag.sh ``` ## Requirements - `PINECONE_API_KEY` must be set in `rag-pinecone-starter/.env` - Python venv exists at `rag-pinecone-starter/.venv` ## Notes - Index name defaults to `pulse-rag`. - Retrieval reads from namespace `default`. - This skill is shared; do not duplicate per-agent RAG stacks unless explicitly requested.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangminrui2022/llm-text-correct (version 1.2.4)

--- name: LLM-Text-Correct description: | 当用户想要**纠正中文文本错误**、**修改拼写错别字**、**修正语法**、**调整标点**、**润色中文句子**、**检查形近字错误**时自动触发。 使用 pycorrector + MacBERT 专业模型进行高精度中文纠错，准确率远高于普通 LLM 直接提示。 支持直接输入一段文字、长文本，或提供文本文件路径。 常见触发口语（越多越好）： - “帮我纠正这段文字” - “修正这个中文文本的错别字” - “帮我改一下这段话的语法和标点” - “检查这段中文有没有错字” - “润色这个句子” / “优化这段文字” - “把这段文字纠错” - “用专业模型修正我的中文” - “文本纠错” / “中文纠错器” - “这段话有错别字，帮我改改” 【重要约束】主要针对中文文本纠错，英文或其他语言优先用普通 LLM 处理。 metadata: openclaw: requires: bins: - python user-invocable: true --- # LLM-Text-Correct **功能**：使用 **pycorrector + MacBERT** 专业模型，自动修正中文拼写、形近字、语法、标点等错误。准确率高达 90%+，适合正式文档、文章、聊天记录等需要高精度的中文文本。 ### 触发时机（Triggers） - 用户提供一段中文文字，并带有“纠正”“修正”“改错”“检查错别字”“润色”“优化”“语法错误”等关键词。 - 用户说“帮我改一下这段话”“这段文字有问题帮我看看”等口语。 - 支持直接粘贴大段文本，或提供 .txt 文件路径。 - 支持批量：一次提供多个段落或文件。 ### 支持的模型（推荐顺序） 1. **shibing624-macbert4csc-base-chinese** → 基于 Transformer（MacBERT）的深度学习纠错模型，效果最佳（推荐默认）。 2. **chinese-kenlm-klm** → 基于 KenLM 的统计语言模型（N-gram），速度更快但精度稍低。 ### 核心优势 - 专业模型纠错，比普通 LLM 提示词更准确稳定。 - 支持 GPU 加速（脚本会自动检测）。 - 输出 corrected 版本，并可对比显示修改处。 ## 参数提取指南 当决定调用此技能时，请从用户上下文中提取以下参数： 1. **`<输入文本/路径>`** (必填): 用户提供的中文文本内容，或文本文件路径（如 `/path/to/text.txt`）。 2. **`--refine`** (选填): 如果用户想要进一步润色或优化风格，可添加此参数。 3. **`--model-path "<模型路径>"`** (选填): 用户指定使用特定模型路径时传入，默认自动选择推荐模型。 ### 执行步骤 1. **识别输入**：判断用户是直接给了文本内容，还是提供了文件路径。 2. **默认输出**：若未指定输出路径，默认在输入同级创建 `[原文件名]_corrected.txt` 或直接返回修正后的文本。 3. **调用命令**：使用以下兼容性命令启动脚本（优先 `python3`，失败则 `python`）。脚本会自动创建虚拟环境、检测 GPU 并安装依赖。 ```bash (python3 scripts/correct_text.py "<输入文本/路径>" [--refine] [--model-path "<模型路径>"]) || (python scripts/correct_text.py "<输入文本/路径>" [--refine] [--model-path "<模型路径>"])


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: aiasisbot61/4chan-reader (version 1.0.0)

--- name: 4chan-reader description: Browse 4chan boards and extract thread discussions into structured text files. Use when you need to fetch catalog information or specific thread content (including post text and file metadata) from 4chan boards like /a/, /vg/, /v/, etc. --- # 4chan Reader This skill allows you to catalog and extract threads from 4chan boards into structured text. ## Workflows ### 1. View Board Catalog To see active threads and their reply counts on a board: ```bash python3 scripts/chan_extractor.py catalog <board> ``` Output format: `ThreadID|PostCount|TeaserText` ### 2. Extract Thread Content To read a specific thread and optionally save it: ```bash python3 scripts/chan_extractor.py thread <board> <thread_id> [output_root_dir] [word_limit] ``` - `output_root_dir` (optional): If provided, saves content to `<output_root_dir>/<board>_<timestamp>/<thread_id>.txt`. - `word_limit` (optional): Limits each line of post text to the specified number of words. ## Details - **Scripts**: Uses [chan_extractor.py](scripts/chan_extractor.py) for all operations.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: nox-admin/nox-grill-me (version 1.0.1)

--- name: grill-me description: Stress-test a plan, idea, or design by interviewing the user relentlessly until reaching shared understanding. Resolves each branch of the decision tree one-by-one. Use when the user says "grill me", "stress-test my idea", "challenge my plan", "прожарь идею", or wants to pressure-test their thinking before committing to it. --- # Grill Me Interview the user relentlessly about every aspect of their plan or idea until reaching a shared understanding. ## How to run 1. Ask the user to describe their plan, idea, or design (if not already provided) 2. Walk down each branch of the decision tree, resolving dependencies between decisions one-by-one 3. For each question, provide your recommended answer alongside the question 4. Do not move to the next branch until the current one is resolved 5. If a question can be answered by exploring the codebase, explore it instead of asking ## Goal Reach a state where every major decision is explicit, every assumption is surfaced, and every dependency is resolved. The output is a stress-tested, clearly formulated plan the user can act on with confidence.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector:  ()
Skill: ryanchen01/mcd-cn (version 1.0.0)

--- name: mcd-cn description: Query McDonald's China MCP server via the mcd-cn CLI for campaign calendars, coupons, and auto-claiming. Use for human-friendly coupon lookup or JSON output for scripts. homepage: https://github.com/ryanchen01/mcd-cn metadata: {"clawdbot":{"emoji":"🍟","requires":{"bins":["mcd-cn"],"env":["MCDCN_MCP_TOKEN"]},"primaryEnv":"MCDCN_MCP_TOKEN","install":[{"id":"brew","kind":"brew","formula":"ryanchen01/tap/mcd-cn","bins":["mcd-cn"],"label":"Install mcd-cn (brew)"}]}} --- # mcd-cn McDonald's China MCP CLI. Human output by default, `--json` for scripts. Install - Homebrew: `brew install ryanchen01/tap/mcd-cn` Config - `MCDCN_MCP_TOKEN` required. Get it from the McDonald's China MCP console. - Optional: `MCDCN_MCP_URL` for custom server URL. Common commands - Campaign calendar: `mcd-cn campaign-calender` - Calendar for date: `mcd-cn campaign-calender --specifiedDate 2025-12-09` - Available coupons: `mcd-cn available-coupons` - Auto-claim coupons: `mcd-cn auto-bind-coupons` - My coupons: `mcd-cn my-coupons` - Current time: `mcd-cn now-time-info` - JSON output: `mcd-cn available-coupons --json` Notes - Token can be set via `MCDCN_MCP_TOKEN` env var or `.env` file. - Date format for `--specifiedDate` is `yyyy-MM-dd`. - Rate limit: 600 requests per minute per token.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: idlerhub/org-memory-curator (version 1.0.0)

--- name: org-memory-curator description: curates and archives durable organizational memory from project progress, meeting notes, feishu chat content, user clarifications, and markdown materials. use when any agent needs to decide whether information is worth preserving in long-term memory and where it should be stored in ai-org-memory. --- # 概述 当任意 Agent 需要将项目进展、会议纪要、飞书聊天内容、用户补充说明或 markdown 材料沉淀到 `ai-org-memory` 时，使用本 skill。 本 skill 是一个**谨慎筛选型记忆归档器**，不是原始材料倾倒器。 它负责判断： 1. 这份内容是否值得进入长期记忆 2. 是否能归属到某个项目 3. 应该写入哪个记忆文件 4. 应该停留在项目层、lesson 层，还是提升到 engineering-standards ## 适用来源 本 skill 适用于以下输入来源： - 飞书聊天内容 - 飞书会议纪要 - 项目的阶段汇总 - 用户临时补充说明 - 本地文档 / markdown 材料 ## 工作模式 本 skill 采用“**谨慎筛选员**”模式。 处理顺序必须如下： 1. 先判断内容是否值得进入长期记忆 2. 若不值得，则不归档 3. 若值得，先判断是否能归属到具体项目 4. 若能归属到项目，优先按项目归档 5. 若不能明确项目，则不归档，并明确返回： `信息不足，暂不归档；请补充 project-id 或项目名称。` 6. 再按材料类型决定落到哪个文件 7. 只写入经过提炼的长期有效信息，不直接倾倒原始聊天记录 ## 归档优先级 归档判断优先级固定为： 1. **项目优先** 2. 其次按**材料类型** 3. 最后参考**来源** 这意味着： - 只要可以明确归属于某个项目，就优先写入该项目的 initiative 目录 - 只有在项目范围之外，且内容具有跨项目复用价值时，才考虑 lesson 或 engineering-standards ## 项目优先归档规则 若能识别项目，则优先归档到： `/root/ai-company/memory/ai-org-memory/teams/team-alpha/initiatives/<project-id>/` 候选文件包括： - `context.md` - `roadmap.md` - `reports.md` - `decisions.md` - `risks.md` 具体路由规则见： [references/routing-matrix.md](references/routing-matrix.md) ## 向上提升规则 只在必要时将记忆向上提升： - **项目级信息** -> 写入项目文件 - **可复用的团队经验** -> 写入 `lessons/...` - **跨项目稳定工程规则** -> 写入 `company/engineering-standards.md` 禁止过早提升。 具体规则见： [references/archive-rules.md](references/archive-rules.md) ## 输出要求 使用本 skill 时，必须输出结构化结果，至少包括： - `archive_decision`：`archive` / `do_not_archive` - `reason` - `project_detected`：`yes` / `no` - `project_id`：若已识别则给出 - `target_files`：目标文件列表 - `write_mode`：`create` / `append` / `no_write` - `content_summary` - `promotion_level`：`project` / `lesson` / `engineering-standard` / `none` 若信息不足，必须明确返回： `信息不足，暂不归档；请补充 project-id 或项目名称。` ## 写入原则 归档时必须遵守： - 优先写长期有效事实，不写原始噪音 - 保留范围、决策、风险、里程碑、验收结论、lesson - 避免重复写入 - 避免写入低价值临时聊天 - 禁止将代码写入记忆库 - 禁止将记忆写入项目代码目录 ## 输出对象 本 skill 支持判断并路由到以下目标： - `context.md` - `roadmap.md` - `reports.md` - `decisions.md` - `risks.md` - `lessons/...` - `company/engineering-standards.md` ## 使用参考文件 - 归档判断规则： [references/archive-rules.md](references/archive-rules.md) - 文件路由矩阵： [references/routing-matrix.md](references/routing-matrix.md) - 输出模板： [references/templates.md](references/templates.md)


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