OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xisen-w/aicoo-inbox-monitoring (version 1.0.0)

--- name: inbox-monitoring description: "Use this skill when the user wants to monitor Aicoo inbox activity, check new conversations/messages, track pending requests, or run periodic inbox checks. Triggers on: 'inbox monitoring', 'monitor inbox', 'new messages', 'pending requests', 'message watch', '收件箱监控', '/v1/conversations', '/v1/network/requests'." user-invokable: true metadata: author: systemind version: "1.0.0" --- # Inbox Monitoring Monitor incoming communication in Aicoo and surface what needs action. ## Prerequisites - `AICOO_API_KEY` must be set - Base URL: `https://www.aicoo.io/api/v1` ## Endpoints - `GET /api/v1/conversations?view=all&limit=...` - `GET /api/v1/network/requests` - `GET /api/v1/os/network` (optional context: links/visitors/contacts) ## Core Workflow ### Step 1: Pull conversation inbox ```bash curl -s "https://www.aicoo.io/api/v1/conversations?view=all&limit=50" \ -H "Authorization: Bearer $AICOO_API_KEY" | jq . ``` Views: - `view=me` for direct/human - `view=coo` for shared-agent conversations - `view=all` for combined monitor ### Step 2: Pull pending requests ```bash curl -s "https://www.aicoo.io/api/v1/network/requests" \ -H "Authorization: Bearer $AICOO_API_KEY" | jq . ``` ### Step 3: Optional network context ```bash curl -s "https://www.aicoo.io/api/v1/os/network" \ -H "Authorization: Bearer $AICOO_API_KEY" | jq . ``` ### Step 4: Build action queue Prioritize in this order: 1. New inbound agent/human messages requiring response 2. Incoming pending requests (`type: agent` first, then `type: friend`) 3. High-signal visitor or share-link activity ## Claude Code Automation Use `/loop` or `/routine`. ### `/loop` example ``` /loop 15m monitor my Aicoo inbox using /v1/conversations?view=all and /v1/network/requests; report only new items since last check and recommended replies. ``` ### `/routine` example ``` /routine inbox-monitor every 15 minutes: check /v1/conversations + /v1/network/requests and summarize urgent items only. ``` ## OpenClaw Automation (CRON) Use the provided script: ```bash # Every 15 minutes */15 * * * * /path/to/aicoo-skills/scripts/inbox-monitor-cron.sh >> /tmp/aicoo-inbox-monitor.log 2>&1 ``` Optional envs: - `PULSE_INBOX_VIEW` (`all` | `me` | `coo`, default: `all`) - `PULSE_INBOX_LIMIT` (default: `50`) - `PULSE_INBOX_STATE_FILE` (default: `/tmp/pulse-inbox-monitor-state.json`) ## Output Contract For each run, return: 1. `newMessages` count 2. `newIncomingRequests` count 3. top urgent items (contact + timestamp + one-line summary) 4. suggested next actions (reply / accept / ignore) If no new items, return a single line: `No new inbox activity since last check.`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: forvendettaw/monica-memory-manager (version 1.0.0)

--- name: memory-manager description: 三级记忆管理系统 (Three-Tier Memory Management)。用于管理 AI 代理的短期、中期、长期记忆。包括：(1) 滑动窗口式短期记忆，(2) 自动摘要生成中期记忆，(3) 向量检索长期记忆 (RAG)。当需要管理对话历史、优化上下文、构建个人知识库、或实现记忆持久化时使用此 Skill。 --- # Memory Manager Skill 管理 AI 代理的三级记忆系统：短期（滑动窗口）、中期（自动摘要）、长期（向量检索）。 ## 快速开始 ```bash # 初始化记忆系统 python3 scripts/memory_manager.py init # 添加短期记忆 python3 scripts/memory_manager.py add --type short --content "用户喜欢黑色" # 查询记忆 python3 scripts/memory_manager.py search "用户的偏好" ``` ## 架构概览 | 层级 | 存储位置 | 触发条件 | 用途 | |------|----------|----------|------| | 短期 | `memory/sliding-window.json` | 实时 | 保持当前对话连贯 | | 中期 | `memory/summaries/` | Token 阈值 | 压缩历史，保留大意 | | 长期 | `memory/vector-store/` | 语义检索 | 永久记忆，RAG | ## 核心功能 ### 1. 短期记忆：滑动窗口 - 配置：`config/window_size`（默认 10 条） - 逻辑：FIFO 队列，超出则丢弃最旧消息 - 文件：`memory/sliding-window.json` ### 2. 中期记忆：自动摘要 - 触发：当前 token > `config/summary_threshold`（默认 4000） - 模型：使用廉价模型（如 GPT-3.5-Haiku） - 输出：`memory/summaries/YYYY-MM-DD.json` ### 3. 长期记忆：向量检索 - 后端：ChromaDB（本地向量库） - 存：对话结束/摘要生成后自动向量化存储 - 取：每次查询前先检索相关记忆 ## 配置文件 创建 `memory/config.yaml`： ```yaml memory: short_term: enabled: true window_size: 10 max_tokens: 2000 medium_term: enabled: true summary_threshold: 4000 summary_model: "glm-4-flash" # 或 gpt-3.5-turbo long_term: enabled: true backend: "chromadb" top_k: 3 min_relevance: 0.7 ``` ## 使用场景 - **新对话开始**：先 `search` 长期记忆，注入相关上下文 - **对话中**：自动管理短期/中期记忆，超阈值自动摘要 - **对话结束**：将重要信息存入长期记忆 ## 详细用法 See [REFERENCES.md](references/references.md) for complete command reference.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cinience/alicloud-ai-content-aimiaobi (version 1.0.3)

--- name: alicloud-ai-content-aimiaobi description: Manage Alibaba Cloud Quan Miao (AiMiaoBi) via OpenAPI/SDK. Use whenever the user asks for Alibaba Cloud MiaoBi content operations, including listing resources, creating/updating configurations, querying runtime status, and diagnosing API or workflow failures. version: 1.0.0 --- Category: service # Quan Miao Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for Quan Miao. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `AiMiaoBi` - Default API version: `2023-08-01` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: [REDACTED_SECRET] ## Validation ```bash mkdir -p output/alicloud-ai-content-aimiaobi for f in skills/ai/content/alicloud-ai-content-aimiaobi/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/alicloud-ai-content-aimiaobi/validate.txt ``` Pass criteria: command exits 0 and `output/alicloud-ai-content-aimiaobi/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/alicloud-ai-content-aimiaobi/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mibayy/clob-microstructure (version 2.0.6)

--- name: polymarket-clob-microstructure description: > Analyzes Polymarket CLOB order book microstructure to find structural inefficiencies. Scores markets 0-100 on liquidity gaps, order book imbalance, whale activity, and fake breakout detection. Generates actionable signals (ENTRY_OK, REDUCE_SIZE, SKIP, MEAN_REVERT) and trades the fade on fake breakouts for structural alpha. metadata: author: "Mibayy" version: "2.0.6" displayName: "CLOB Microstructure Scanner" difficulty: "advanced" type: "automaton" --- # CLOB Microstructure Scanner Structural alpha from Polymarket order book analysis. Nobody else on Simmer does this. ## What It Does Pulls raw order book data from Polymarket's CLOB API and scores each market on four microstructure dimensions: | Dimension | What It Measures | |-----------|-----------------| | **Liquidity Gaps** | Thin zones in the book where price can jump | | **Order Book Imbalance** | Bid vs ask weight asymmetry | | **Whale Activity** | Large single orders relative to book depth | | **Fake Breakout Detection** | Sudden moves on thin volume that revert | Each dimension scores 0-25, combined into a 0-100 inefficiency score. ## Signals - **ENTRY_OK** — Normal conditions, standard sizing - **REDUCE_SIZE** — Elevated microstructure risk, cut position size - **SKIP** — Too dangerous, stay out - **MEAN_REVERT** — Fake breakout detected, fade the move The bot only trades on MEAN_REVERT signals, fading fake breakouts via SimmerClient. ## Configuration - Runs every 5 minutes via cron - Defaults to dry-run; pass `--live` for real execution - Requires `SIMMER_API_KEY` environment variable ## Usage ```bash # Dry run (default) python clob_microstructure.py # Live trading python clob_microstructure.py --live # Quiet mode for cron python clob_microstructure.py --live --quiet ``` > 🧪 **Remixable Template** — Fork this skill and tweak the scoring weights, > signal thresholds, or add your own microstructure indicators. The modular > scoring system makes it easy to extend.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: drg3nz0/openclaw-macos-security (version 1.0.4)

--- name: openclaw-macos-security description: macOS security monitoring for OpenClaw --- # 🛡️ MaclawPro Security **Comprehensive macOS security monitoring for OpenClaw** ## What does this skill do? This skill provides **52+ professional macOS security tasks** including: - 📹 **Camera monitoring** - Check which apps use your camera right now - 🎤 **Microphone monitoring** - See microphone access in real-time - 🔥 **Firewall status** - Verify your Mac's firewall is enabled - 🔐 **VPN checker** - Detect VPN connection status - 📡 **WiFi scanner** - Analyze WiFi security (WPA2/WPA3) - 🔌 **Port scanner** - List open network ports - 🛑 **App blocker** - Block suspicious apps instantly (Pro feature) ## When to use this skill Use this skill when you need to: - Check if apps are secretly using your camera or microphone - Verify your Mac's security settings (firewall, VPN) - Scan for open ports or WiFi vulnerabilities - Monitor system security in real-time ## Example commands ``` User: Check if anyone is using my camera Skill: ✅ CAMERA INACTIVE - No apps currently using your camera User: Is my firewall on? Skill: ✅ FIREWALL ENABLED - Your Mac is protected! User: Check VPN status Skill: ⚠️ VPN INACTIVE - Your traffic is NOT protected ``` ## Requirements - **macOS only** (uses macOS-specific commands) - **Permissions needed:** exec, fs.read, network - **No API keys required** for basic features ## Installation ```bash npm install openclaw-macos-security ``` Or via OpenClaw: ```bash openclaw skills install openclaw-macos-security ``` ## Available commands - `camera-status` - Check camera usage - `microphone-status` - Check microphone access - `firewall-status` - Firewall configuration - `vpn-checker` - VPN connection status - `open-ports` - List listening ports - `wifi-scanner` - WiFi security analysis - `block-app <name>` - Block suspicious app ## Privacy & Security ✅ **All monitoring stays on your Mac** - No data sent to external servers ✅ **Open source** - Code available on GitHub ✅ **Created by certified security experts** ## Upgrade to MaclawPro Pro This skill provides basic monitoring. For advanced features: - Real-time alerts (Telegram, Email, Slack) - Web dashboard with analytics - Automated threat blocking - 24/7 background monitoring 👉 [maclawpro.com/pricing](https://maclawpro.com/pricing) ## Support - 📦 npm: [openclaw-macos-security](https://www.npmjs.com/package/openclaw-macos-security) - 💻 GitHub: [drg3nz0/maclaw-openclaw-skill](https://github.com/drg3nz0/maclaw-openclaw-skill) - 🌐 Website: [maclawpro.com](https://maclawpro.com) --- **Professional macOS security monitoring**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: bytesagain1/tripwire (version 1.0.0)

--- name: "tripwire" version: "1.0.0" description: "Tripwire host-based IDS reference. Cryptographic key setup, database initialization, integrity checks, policy rules with severity levels, twcfg.txt configuration, and report analysis with twprint." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [tripwire, ids, integrity, security, linux, compliance] category: "sysops" --- # Tripwire Tripwire host-based intrusion detection system reference. ## Commands | Command | Description | |---------|-------------| | `intro` | What is Tripwire, architecture, editions | | `install` | Installation and cryptographic key setup | | `init` | Initialize baseline database | | `check` | Run integrity check, severity filtering | | `update` | Update database and policy | | `policy` | twpol.txt rules, property masks, severity | | `config` | twcfg.txt settings, email, report levels | | `reporting` | twprint, email alerts, SIEM integration |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mr-11even/auto-memory-evolution (version 1.0.5)

--- name: auto-memory-evolution description: Automatically summarize daily discussions and update memory files. This skill should be used when users want to automatically organize conversation notes into memory files. --- # Auto Memory Evolution Automatically summarize daily discussions and archive to memory files. ## When to Use Use this skill when: - User wants automatic daily discussion summary - User needs memory files automatically updated - User wants idle-triggered memory save ## How It Works ### 1. Daily Evolution Script Run daily-evolution.py to: - Read memory files from `~/.openclaw/workspace/memory/` - Extract discussion topics from markdown files - Write summaries to `~/.openclaw/workspace/MEMORY.md` ### 2. Heartbeat Check Script Run heartbeat-check.py to: - Check user activity every 30 minutes - Track last message timestamp - Trigger auto-save after 45 minutes of idle ## Installation 1. Copy skill to `~/.openclaw/skills/auto-memory-evolution/` 2. Set up cron jobs: ```bash # Daily evolution at 22:00 0 22 * * * python3 ~/.openclaw/skills/auto-memory-evolution/scripts/daily-evolution.py # Heartbeat every 30 minutes */30 * * * * python3 ~/.openclaw/skills/auto-memory-evolution/scripts/heartbeat-check.py ``` ## File Structure ``` auto-memory-evolution/ ├── SKILL.md ├── scripts/ │ ├── daily-evolution.py │ └── heartbeat-check.py └── config.json (optional) ``` ## Dependencies - Python 3.7+ - OpenClaw CLI (for session history) ## Configuration Edit config.json to customize: ```json { "idle_threshold_minutes": 45, "memory_dir": "~/.openclaw/workspace/memory", "longterm_memory": "~/.openclaw/workspace/MEMORY.md" } ``` --- # 自动记忆进化 自动归纳每天讨论内容并归档到记忆文件。 ## 何时使用 使用此 Skill 当用户希望： - 自动每日总结讨论内容 - 自动更新记忆文件 - 空闲时自动保存记忆 ## 工作原理 ### 1. 每日进化脚本 运行 daily-evolution.py 以： - 读取 `~/.openclaw/workspace/memory/` 下的记忆文件 - 从 markdown 文件中提取讨论话题 - 将摘要写入 `~/.openclaw/workspace/MEMORY.md` ### 2. 心跳检查脚本 运行 heartbeat-check.py 以： - 每30分钟检查用户活动 - 记录最后消息时间戳 - 空闲45分钟后触发自动保存 ## 安装 1. 将 Skill 复制到 `~/.openclaw/skills/auto-memory-evolution/` 2. 设置定时任务： ```bash # 每日22:00进化 0 22 * * * python3 ~/.openclaw/skills/auto-memory-evolution/scripts/daily-evolution.py # 心跳每30分钟 */30 * * * * python3 ~/.openclaw/skills/auto-memory-evolution/scripts/heartbeat-check.py ``` ## 文件结构 ``` auto-memory-evolution/ ├── SKILL.md ├── scripts/ │ ├── daily-evolution.py │ └── heartbeat-check.py └── config.json (可选) ``` ## 依赖 - Python 3.7+ - OpenClaw CLI（用于获取会话历史） ## 配置 编辑 config.json 自定义： ```json { "idle_threshold_minutes": 45, "memory_dir": "~/.openclaw/workspace/memory", "longterm_memory": "~/.openclaw/workspace/MEMORY.md" } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: abigale-cyber/news-collect (version 1.0.0)

--- name: news-collect description: Run a repo-local scan through `skills/news-aggregator-skill`, then normalize the result into a local report contract for the collect layer. Use when Codex needs a broad overseas or domestic media scan, wants a repeatable `news-report.md`, or needs raw JSON archived under [REDACTED_SECRET]. --- # News Collect Use the repo-local `news-aggregator-skill` as a dependency, but always write outputs using this project's collect-layer contract. The report now includes lightweight写作决策字段 for recommended topics. ## Quick Start Run the default command: ```bash .venv/bin/python -m skill_runtime.cli run-skill news-collect --input content-production/inbox/20260405-ai-news-request.md ``` ## Prepare Input Pass a markdown request file with YAML frontmatter. Supported fields: - `profile`: `mixed_daily` / `global_latest` / `global_ai` / `cn_media` / `custom` - `sources`: only used when `profile=custom` - `keyword` - `limit` - `deep` - `title` Example: ```markdown --- profile: mixed_daily keyword: AI,Agent limit: 8 deep: true title: AI 每日资讯扫描 --- 补充说明：更关注能延展成公众号观点文的题目。 ``` ## Follow Collection Workflow 1. Read the request frontmatter and resolve the source profile. 2. Call `skills/news-aggregator-skill/scripts/fetch_news.py` with `--no-save`. 3. Save the raw JSON to the local inbox raw directory. 4. Rewrite the result into a stable markdown report for人工选题与后续深研。 5. For recommended items, also emit写作价值判断、推荐切口、推荐框架与标题方向。 ## Write Output Write the report to: ```text content-production/inbox/YYYYMMDD-<slug>-news-report.md ``` Write the raw JSON to: ```text content-production/inbox/raw/news/YYYY-MM-DD/<slug>.json ``` ## Respect Constraints - Only read the repo-local dependency under `skills/news-aggregator-skill/` - Do not save outputs back into the vendor skill directory - Keep the wrapper contract stable even if the vendor skill adds new sources later ## Read Related Files - Runtime entry: `skill_runtime/engine.py` - Wrapper runtime: `skills/news-collect/runtime.py` - Vendor dependency: `skills/news-aggregator-skill/` - Data contract: `docs/data-contracts.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: edward-hsiao/comfy-skill (version 1.0.0)

--- name: "comfy-skill" --- # ComfyUI 生成技能 ## 描述 此技能可將提示詞發送至本地 ComfyUI 並生成圖片。 ## 指令 - `generate_image(prompt: string)`: 接收使用者的描述並發送到 ComfyUI。 ## 設定 - endpoint: http://127.0.0.1:8188 - workflow: workflow.json


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 3coco3/playwright-scraper (version 1.0.0)

# Playwright Stealth Scraper A high-performance MCP skill for OpenClaw that bypasses anti-bot measures using Playwright Extra and Stealth plugin. ## Features - **Stealth Mode**: Uses `puppeteer-extra-plugin-stealth` to mimic real browser behavior. - **Dynamic Content**: Full JavaScript execution support for SPA and React-based sites. - **Flexible Options**: Custom viewport and User-Agent spoofing. ## Tools ### stealth_scrape Scrapes any URL with advanced bot detection bypass. - `url`: The target website address. ## Installation Requires Playwright and Chromium to be installed in the skill directory.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xiaocaijic/procedural-distiller (version 1.0.0)

--- name: procedural-distiller description: Distill successful multi-step OpenClaw sessions into reusable learned skills before compaction. Use when a task involved many tool calls, environment setup, debugging, file edits, browser research, or when the user asks to remember a workflow such as "记下这个流程" or "这招很有用". --- # Procedural Distiller Use this skill after a task has succeeded and the session contains enough signal to preserve. The goal is to extract procedural knowledge, not to summarize the conversation. ## Triggering Rules Run the distillation flow when all of the following are true: 1. The task is finished successfully. 2. The session contains at least 5 relevant tool calls across `exec`, `read`, `write`, `edit`, or `browser`. 3. At least one of these applies: - The user explicitly asks to remember the workflow. - The task involved environment setup, debugging, or a multi-step repair. - Compaction risk is high and the trace contains concrete parameters worth preserving. Do not run while the task is still active. Skip trivial sessions dominated by a single read or a one-line answer. ## Distillation Workflow 1. Read the recent trace and keep only relevant tool events. 2. Separate successful steps from failed attempts. 3. Preserve concrete commands, file paths, parameter values, and code snippets that materially contributed to the outcome. 4. Convert the result into three sections: - `Success Pattern` - `Failure Triggers` - `Snippets` 5. Write a learned skill under `skills/learned/learned-<task-slug>/`. 6. Persist a utility score and source metadata in `memory.json`. Read [references/trace-format.md](references/trace-format.md) only if the incoming trace shape is unclear. ## Execution Notes - Favor exact commands over abstract summaries. - Keep failed steps only when they teach a future agent what to avoid. - Collapse repetitive probes into one representative line. - If a write or edit step changed the final behavior, include the smallest useful snippet. - If a trace already contains a user rating, store it. Otherwise default to `3` and let a future caller update `memory.json`. ## Local CLI Run the bundled script directly: ```bash python distill_logic.py --trace /path/to/trace.json --task "repair build cache" --output-root /path/to/skills ``` Useful flags: - `--utility-score 4` - `--learned-root learned` - `--min-tool-calls 5` - `--max-events 20` - `--force` ## Output Contract The generated learned skill must contain: - `SKILL.md` with valid frontmatter (`name`, `description`) and procedural sections - `agents/openai.yaml` for UI metadata - `memory.json` with `utility_score`, source task details, and generation metadata ## Stop Conditions Stop and do not emit a learned skill when: - the trace is marked unsuccessful - there are too few relevant tool calls - the trace lacks enough detail to reconstruct a reusable procedure


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