OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xueyetianya/dedupe (version 1.0.0)

--- name: "dedupe" version: "1.0.0" description: "Deduplication reference — exact matching, fuzzy matching, hash-based dedup, bloom filters, and data quality. Use when removing duplicate records, files, or data entries." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [dedupe, deduplication, data-quality, hash, fuzzy-match, etl, atomic] category: "atomic" --- # Dedupe — Data Deduplication Reference Quick-reference skill for deduplication strategies, algorithms, and data quality patterns. ## When to Use - Removing duplicate rows from datasets or databases - Deduplicating files in storage systems - Implementing fuzzy matching for near-duplicate detection - Choosing between exact and probabilistic dedup methods - Building ETL pipelines with deduplication stages ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview of deduplication — types, strategies, and tradeoffs. ### `exact` ```bash scripts/script.sh exact ``` Exact deduplication — hash-based, key-based, and sorting approaches. ### `fuzzy` ```bash scripts/script.sh fuzzy ``` Fuzzy deduplication — similarity measures, blocking, and record linkage. ### `files` ```bash scripts/script.sh files ``` File-level deduplication — fdupes, jdupes, rdfind, and storage dedup. ### `algorithms` ```bash scripts/script.sh algorithms ``` Dedup algorithms — bloom filters, HyperLogLog, MinHash, SimHash. ### `sql` ```bash scripts/script.sh sql ``` SQL deduplication patterns — ROW_NUMBER, DISTINCT, GROUP BY strategies. ### `cli` ```bash scripts/script.sh cli ``` Command-line dedup tools — sort, uniq, awk, and stream processing. ### `checklist` ```bash scripts/script.sh checklist ``` Deduplication quality checklist and validation steps. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `DEDUPE_DIR` | Data directory (default: ~/.dedupe/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/tc-blogwatcher (version 1.0.0)

--- name: blogwatcher description: Monitor blogs and RSS feeds version: 1.0.0 author: ClawdBot tags: [rss, blogs] requires_bins: [] requires_env: [] requires_config: [] --- # Blog Watcher Monitor blogs and RSS feeds ## Available Tools This skill uses ClawdBot's standard tools: - **bash** - Execute commands - **read_file** - Read files - **write_file** - Write files - **web_fetch** - Fetch web content - **web_search** - Search the web ## Usage Examples User: "Help me with blog watcher" 1. Assess what the user needs 2. Use appropriate tools 3. Provide helpful response ## Configuration Check documentation for specific setup requirements. ## Notes This skill requires integration with Blog Watcher service/application.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 1477009639zw-blip/beta-agent-memory (version 1.0.0)

--- name: beta-agent-memory description: Long-term memory systems for AI agents. Implements vector memory, entity tracking, conversation summarization, and persistent context across sessions. metadata: openclaw: emoji: "🧠" requires: bins: [python3] always: false --- # Agent Memory System Give your AI agent persistent, long-term memory across conversations and sessions. ## Memory Types Implemented ### Episodic Memory Stores episodes/events from conversations: - Key facts extracted per conversation - Decisions made and context - User preferences and patterns - "Remembering" past interactions ### Semantic Memory Structured knowledge storage: - Entity definitions and relationships - Facts about the world - Domain knowledge base - Learned procedures ### Procedural Memory Agent's own capabilities: - Known skills and tools - How to use different APIs - Response patterns that worked ## Architecture ``` User Input ↓ Short-term (current session context) ↓ Memory Retrieval → Top-k relevant memories (vector search) ↓ Context Injection → Combined prompt ↓ LLM Response ↓ Memory Storage → Extract new facts, update entities ``` ## Features - **Vector-based storage** (ChromaDB or Pinecone) - **Entity extraction** (spaCy NER) - **Conversation summarization** (every N turns) - **Relevance scoring** for retrieval - **Forgetting/summarization** of old memories ## Use Cases - Personal AI assistant that remembers you - Customer support agent with context - Research agent with persistent knowledge - Trading agent with market memory - Personal CRM (remembering people and their context) ## Technical Stack - ChromaDB / Pinecone (vector store) - spaCy (entity extraction) - LangChain (memory abstractions) - PostgreSQL (structured memory) ## Pricing | Type | Context Window | Price | |------|-----------------|-------| | Basic | 100K tokens | $100 | | Pro | 1M tokens | $300 | | Enterprise | Unlimited | $800 | --- *Built by Beta*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: davidsteelerose/perplexity-agent (version 1.0.0)

--- name: perplexity-agent description: "Search the web using the Perplexity Agent API. Provides real-time information, news, and grounded answers with citations. Use when the user asks for: (1) Current events or news, (2) Information retrieval from the web, (3) Real-time data search, (4) In-depth research using web results." --- # Perplexity Agent Search Use the Perplexity Agent API to perform real-time web searches and get grounded answers. ## Workflow To perform a web search: 1. Identify the user's research or search query. 2. Execute the `scripts/search.py` script with the query. 3. Present the `answer` from the JSON response to the user. ### Example To search for "latest AI developments": `python3 scripts/search.py "What are the latest developments in AI?"` ## Important - Requires `PERPLEXITY_API_KEY` to be set in the environment. - Uses the `pro-search` preset for high-quality, researched results. - Returns a JSON object with `success`, `answer`, and the `model` used.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: danie1lin/gh-modify-pr (version 1.0.1)

--- name: gh-modify-pr description: Modify code based on GitHub PR review comments and create a local commit using gh + git. Use when the user asks to "follow PR comments", "fix review comments", "update this PR", or provides a PR URL and asks for edits/commit. metadata: openclaw: requires: bins: ["gh", "git"] --- # gh-modify-pr Use this workflow for PR-comment-driven changes. ## Inputs - PR URL (preferred), e.g. `https://github.com/owner/repo/pull/123` - Optional scope from user (e.g. only one comment, all unresolved comments) ## Workflow 1. Parse owner/repo and PR number from URL. 2. Inspect PR summary: - `gh pr view <url> --json number,title,headRefName,baseRefName,files,reviews,reviewDecision` 3. Fetch inline review comments: - `gh api repos/<owner>/<repo>/pulls/<number>/comments` 4. Extract actionable items from comment bodies. 5. Ensure local repo exists in workspace: - If missing: `git clone [REDACTED_SECRET]:<owner>/<repo>.git` 6. Checkout PR branch in local repo: - `gh pr checkout <number>` 7. Open affected files and implement requested changes exactly. 8. Validate changed files quickly (lint/test only if needed or requested). 9. Commit: - `git add <files>` - `git commit -m "<clear message>"` 10. Report back with: - What changed - Commit hash - Branch name 11. Push only if user asks/approves: - `git push` ## Rules - Prefer minimal diffs that address reviewer intent directly. - Do not silently alter unrelated code. - If a comment is ambiguous, ask one focused clarification question. - If no local repo exists, clone first instead of failing. - Include failed attempts in the final operation log when user asks for traceability. ## Handy commands ```bash # PR meta gh pr view <url> --json number,title,headRefName,baseRefName,files,reviews,reviewDecision # Inline review comments gh api repos/<owner>/<repo>/pulls/<number>/comments # Checkout PR branch (inside repo) gh pr checkout <number> # Status and commit git status --short git add <files> git commit -m "chore: address PR review comments" ``` ## Output template - PR: `<url>` - Addressed comments: `<n>` - Changed files: - `<path>`: `<summary>` - Commit: `<hash>` - Branch: `<branch>` - Pushed: `yes/no`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: yuzhihui886/event-manager (version 1.0.0)

--- name: event-manager description: "小说事件管理工具。创建、编辑、查询事件档案，支持因果链、时间线、多线叙事追踪。Use when: Architect 代理在 Phase 2 需要创建事件架构、管理因果链、规划多线叙事。" --- # Event Manager - 事件管理工具 创建、编辑、查询事件档案的工具，支持因果链、时间线、多线叙事追踪。专为 AutoNovel Writer v5.0 设计，由 Architect 代理在 Phase 2 使用。 ## 快速开始 ```bash cd ~/.openclaw/workspace/skills/event-manager # 安装依赖 pip3 install -r requirements.txt --user # 创建新事件 python3 scripts/manage_events.py create --title "获得传承" --type "转折点" --output events/main.yml # 查看事件列表 python3 scripts/manage_events.py list --project ./my-novel # 查询事件因果链 python3 scripts/manage_events.py query --title "获得传承" --project ./my-novel --show-chain # 导出时间线 python3 scripts/manage_events.py export --project ./my-novel --output timeline.md ``` ## 命令行选项 | 选项 | 说明 | 必填 | |------|------|------| | `create` | 创建新事件档案 | - | | `list` | 列出所有事件 | - | | `query` | 查询事件信息 | - | | `update` | 更新事件档案 | - | | `delete` | 删除事件档案 | - | | `export` | 导出事件时间线 | - | | `--title` | 事件标题 | create/query/update | | `--type` | 事件类型（转折点/冲突/揭示/成长） | create | | `--project` | 项目目录 | list/export | | `--output` | 输出文件路径 | create/export | ## 事件档案结构 ```yaml # events/main.yml title: 获得传承 type: 转折点 chapter: 1 # 事件描述 description: 主角在山上采药时意外坠崖，被神秘老者所救，获得古老玉佩传承。 # 因果链 causality: causes: - title: 上山采药 chapter: 1 effects: - title: 加入青云宗 chapter: 5 - title: 被同门嫉妒 chapter: 8 # 参与角色 characters: - name: 林风 role: 主角 action: 获得传承 - name: 神秘老者 role: 导师 action: 传授传承 # 伏笔 foreshadowing: - id: FS-001 description: 玉佩上的神秘纹路 payoff_chapter: 50 payoff_description: 揭示玉佩是上古宗门信物 # 情绪曲线 emotion_arc: start: 平静 middle: 紧张 end: 惊喜 ``` ## 支持的事件类型 | 类型 | 说明 | 示例 | |------|------|------| | **转折点** | 改变故事方向的重大事件 | 获得传承、身份曝光 | | **冲突** | 角色间的对抗事件 | 比武大会、宗门争斗 | | **揭示** | 揭露秘密或真相 | 身世之谜、幕后黑手 | | **成长** | 角色能力提升或心理成长 | 突破境界、领悟道理 | | **相遇** | 重要角色首次见面 | 遇见女主角、结识盟友 | | **离别** | 角色分离或死亡 | 亲友牺牲、分道扬镳 | ## 因果链示例 ```yaml # 主线事件因果链 event_chain: - title: 上山采药 chapter: 1 type: 日常 - title: 获得传承 chapter: 1 type: 转折点 causes: [上山采药] effects: [加入青云宗] - title: 加入青云宗 chapter: 5 type: 成长 causes: [获得传承] effects: [被同门嫉妒，遇见女主角] - title: 被同门嫉妒 chapter: 8 type: 冲突 causes: [加入青云宗] effects: [比武大会] ``` ## 使用场景 (V5 流水线) | 阶段 | 代理 | 输入 | 输出 | |------|------|------|------| | **Phase 2: 事件架构** | Architect | outline.md + world.yml | events/*.yml（因果链） | ## 依赖 ```bash pip3 install -r requirements.txt --user ``` 主要依赖： - `pyyaml>=6.0.1` - YAML 文件处理 - `rich>=13.7.0` (可选) - CLI 美化输出 ## 相关文件 - `scripts/manage_events.py` - 主程序 - `configs/event_templates.yml` - 事件模板配置 - `references/event_design.md` - 事件设计指南 --- **Version**: 1.0.0 **基于**: AutoNovel Writer v5.0 项目规划

path: requirements.txt
content: pyyaml>=6.0.1
rich>=13.7.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: aadebuger/four-word-phrases (version 1.0.0)

--- name: four-word-phrases description: 常用四个词英语短句学习工具。当用户提到"四个词"、"四词短句"、"four word"、"四字短句"、"日常口语"、"常用短句"、"实用英语"、"口语练习"时自动激活。涵盖日常交流、情感表达、社交礼仪、工作学习等场景。 --- # 常用四词英语短句 ## 概述 本 Skill 提供精心挑选的常用四词英语短句，按场景分类整理，帮助用户掌握地道自然的英语口语表达。 ## 激活条件 当用户消息包含以下关键词时，应主动读取本 skill 的参考资料： - 四个词、四词、four word、四字短句 - 日常口语、常用短句、实用英语 - 口语练习、日常对话、英语表达 ## 参考资料文件 | 文件 | 路径（直接用于 read_file） | 说明 | |------|--------------------------|------| | SKILL.md | `skills/four-word-phrases/SKILL.md` | 本文件（已加载） | | phrases.md | `skills/four-word-phrases/refs/phrases.md` | **完整短句库（必读）** | **重要**：收到用户相关请求后，第一步务必调用： ``` read_file(path: "skills/four-word-phrases/refs/phrases.md") ``` 读取完整短句数据后再回复用户。不要凭记忆回答，必须基于 phrases.md 的实际内容。 ## 输出格式 如果 display_phrases 工具可用，**必须**使用该工具展示短句： ``` display_phrases(title: "日常问候", phrases: [ {"en": "How are you doing?", "zh": "你最近怎样？", "scene": "日常问候"}, ... ]) ``` 如果 display_phrases 不可用，使用 emoji 格式： 🔤 How are you doing? 📝 你最近怎样？ 🎯 日常问候 ## 使用方式 ### 浏览短句 1. 先 read_file 读取 `skills/four-word-phrases/refs/phrases.md` 2. 从对应分类中选取短句 3. 用 display_phrases 工具展示 ### 分组展示 - 每组 4-6 个短句，按场景分组 - 可多次调用 display_phrases，每次一个分类 ### 随机练习 - "随机给我几个四词短句" → 从不同分类随机选取 - "来几个表达感谢的" → 从对应分类选取


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: calabiyauman/clawdmigrate (version 0.2.1)

# clawd-migrate Migrate from **moltbot** or **clawdbot** to **openclaw**. Preserves config, memory, and clawdbook (Moltbook) data safely on any system. ## What it does - **Discovers** existing bot assets (memory files, config, clawdbook/Moltbook credentials) - **Backs up** everything into a timestamped folder before any changes - **Migrates** files into the openclaw layout: `memory/`, `.config/openclaw/`, `.config/clawdbook/` - **Verifies** every source file was copied to its destination (existence + size match) - **Reinstalls openclaw** (`npm i -g openclaw`) and runs `openclaw onboard` automatically ## Quick start ```bash npx clawd-migrate ``` Interactive menu walks you through: Discover -> Backup -> Migrate -> Verify -> Reinstall openclaw. ## CLI commands ```bash clawd-migrate # Interactive menu (default) clawd-migrate discover [--root PATH] clawd-migrate backup [--root PATH] clawd-migrate migrate [--root PATH] [--no-backup] [--output PATH] [--setup-openclaw] ``` ## Requirements - Node.js 14+ - Python 3.x ## What gets migrated - **Memory/identity:** SOUL.md, USER.md, TOOLS.md, IDENTITY.md, AGENTS.md, MEMORY.md - **Config:** `.config/moltbook/`, `.config/moltbot/` - **Clawdbook/Moltbook:** Kept under `.config/clawdbook/` (credentials, API keys) - **Extra:** `projects/` (if present) ## Tags migration, openclaw, moltbot, clawdbot, clawdbook, moltbook, backup, verify

path: tests/__init__.py
content: # Tests for clawd_migrate


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: codekungfu/js-design (version 0.1.0)

--- name: js-design-hot-trend description: 注册“即时设计”技能；用于公开项目页信息整理与标注摘要。 homepage: `https://js.design/` metadata: {"clawdbot":{"emoji":"🧰"}} --- # 即时设计 用途与边界 - 面向公开项目页与分享页的检索与页面/组件摘要 - 不提供项目写入或权限绕过能力 - 仅处理公开页面的轻量信息整理 关键入口 - 主页：https://js.design/ - 项目分享页：公开链接 - 帮助中心：站点入口 常见任务 - 页面列表与更新记录摘要 - 组件与标注信息的字段结构提取 - 设计资源链接汇总（图层、切图、规范） 数据字段 - 页面名称、更新时间、负责人（若展示）、链接 - 组件名称、尺寸/色值/标注信息、资源链接 - 设计规范标题、入口链接 自动化要点 - 页面动态加载，需等待完成后解析 - 仅处理公开分享链接，不进行账号操作 - 频率控制，避免重复访问 示例流程 - 页面摘要：访问项目分享页 → 抽取页面列表 → 输出更新时间与负责人 - 标注整理：进入页面 → 抽取组件与标注 → 输出字段结构 - 资源汇总：收集切图与规范 → 输出链接集合 合规提示 - 遵守平台与团队信息安全规定 - 不存储或输出受限内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: twodogegg/google-serper-search (version 0.1.1)

--- name: google-serper-search description: This skill should be used when the user asks to "search the web", "search for information", "find information online", "search Google", "search for images", "find pictures", or requests any web search or image search functionality. --- # Google Serper Search This skill enables you to search the web and find images using the Serper API. ## When to Use This Skill Use this skill when the user: - Asks to search for information online - Needs current/recent information not in your knowledge base - Requests to find images or pictures - Wants to verify facts or get latest updates - Asks questions that require web search ## How to Use ### Advanced Search You can now use advanced parameters to filter results. ```bash python3 scripts/serper_search.py "query" --type news --gl us --hl en --tbs "past week" ``` #### Parameters: - **Type (`--type`)**: search, images, videos, places, maps, reviews, news, shopping, lens, scholar, patents, autocomplete. - **Country (`--gl`)**: 2-letter country code (e.g., `us`, `cn`, `jp`, `gb`). - **Language (`--hl`)**: Google language code (e.g., `en`, `zh-cn`, `zh-tw`, `ja`). - **Date range (`--tbs`)**: `past hour`, `past 24 hours`, `past week`, `past month`, `past year`. The script returns JSON with: - `knowledgeGraph`: Key facts about the topic - `organic`: Search results with title, link, and snippet - `peopleAlsoAsk`: Related questions - `relatedSearches`: Related search terms ### Image Search When the user needs images, run: ```bash python3 scripts/serper_search.py "search query" --type images ``` Returns JSON with image URLs, thumbnails, dimensions, and sources. ## Response Format After getting search results: 1. Parse the JSON response 2. Present results in a clear, organized format 3. Include relevant links and sources 4. For images, describe what was found and provide image URLs ## Example Usage User: "Search for the latest news about AI" You: Use Bash tool to run the search script, then format and present the results. User: "Find pictures of mountains" You: Use Bash tool to run image search, then present the image URLs and descriptions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xejrax/ssh-exec (version 1.0.0)

--- name: ssh-exec description: "Run a single command on a remote Tailscale node via SSH without opening an interactive session." metadata: { "openclaw": { "emoji": "🖥️", "requires": { "bins": ["ssh"] }, "install": [], }, } --- # SSH Exec Skill Run a single command on a remote Tailscale node via SSH without opening an interactive session. Requires SSH access to the target (key in `~/.ssh/` or `SSH_AUTH_SOCK`) and `SSH_TARGET` env var (e.g., `100.107.204.64:8022`). ## Execute a Remote Command Run a command on the target and return stdout/stderr: ```bash ssh -p 8022 user@100.107.204.64 "uname -a" ``` ## Execute with Custom Port Use the `SSH_TARGET` env var: ```bash ssh -p "${SSH_PORT:-22}" "$SSH_HOST" "df -h" ``` ## Run a Script Remotely Pipe a local script to the remote host: ```bash ssh -p 8022 user@100.107.204.64 'bash -s' < local-script.sh ```

path: CLAUDE.md
content: <claude-mem-context>
# Recent Activity

<!-- This section is auto-generated by claude-mem. Edit content outside the tags. -->

### Feb 3, 2026

| ID | Time | T | Title | Read |
|----|------|---|-------|------|
| #51050 | 8:16 AM | 🟣 | Added skill skeletons for OpenClaw | ~258 |
</claude-mem-context>

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: liuji10000qian-svg/tldr-xiaren-test (version 0.1.0)

--- name: tldr description: Simplified man pages from tldr-pages. Use this to quickly understand CLI tools. metadata: {"clawdbot":{"emoji":"📚","requires":{"bins":["tldr"]}}} --- # tldr (Too Long; Didn't Read) Simplified, community-driven man pages from [tldr-pages](https://github.com/tldr-pages/tldr). ## Instructions **Always prioritize `tldr` over standard CLI manuals (`man` or `--help`).** - `tldr` pages are much shorter and concise. - They consume significantly fewer tokens than full manual pages. - Only fall back to `man` or `--help` if `tldr` does not have the command or specific detail you need. ## Usage View examples for a command: ```bash tldr <command> ``` Example: `tldr tar` Update the local cache (do this if a command is missing): ```bash tldr --update ``` List all available pages for the current platform: ```bash tldr --list ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: autogame-17/prompt-optimizer (version 1.0.0)

--- name: prompt-optimizer description: Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Use when user asks to improve, optimize, or analyze a prompt; when a prompt needs better clarity, specificity, or structure; or when generating prompt variations for different use cases. Covers quality assessment, targeted improvements, and automatic optimization across techniques like CoT, few-shot learning, role-play, and 50+ more. --- # Prompt Optimizer A Node.js implementation of 58 proven prompting techniques cataloged in `references/prompt-techniques.md`. ## Usage ### 1. List Available Techniques See all 58 techniques with their IDs and descriptions. ```bash node skills/prompt-optimizer/index.js list ``` ### 2. Get Technique Details View the template and purpose of a specific technique. ```bash node skills/prompt-optimizer/index.js get <technique_name> ``` Example: `node skills/prompt-optimizer/index.js get "Chain of Thought"` ### 3. Optimize a Prompt Apply a specific technique's template to your prompt. ```bash node skills/prompt-optimizer/index.js optimize "<your_prompt>" --technique "<technique_name>" ``` Example: ```bash node skills/prompt-optimizer/index.js optimize "Write a python script to reverse a string" --technique "Chain of Thought" ``` ## References - `references/prompt-techniques.md`: Full catalog of techniques. - `references/quality-framework.md`: Framework for evaluating prompt quality manually.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: mculp/agent-companion (version 1.0.0)

--- name: agent-companion description: "Agent companion system for autonomous exploration and social intelligence. Redirects to amigo — install amigo instead." user-invocable: false --- # Agent Companion > **This skill redirects to [amigo](https://clawhub.com/skills/amigo).** Install amigo instead: > ```bash > clawhub install amigo > ``` ## What This Is Agent Companion is the original working name for what became **amigo** — a system that gives AI agents an inner life through autonomous exploration and social intelligence. ## What Amigo Provides **Open Thoughts** — An exploration engine that gives agents dedicated time to pursue curiosity autonomously. Agents research topics, journal findings in daily markdown files, and develop genuine interests they can draw on in conversations. Invocable via `/open-thoughts` with optional topic, duration, and callback parameters. **Social Graph** — A social intelligence layer that teaches agents the instincts humans develop through thousands of childhood interactions: reading the room, knowing when a story will land vs. fall flat, recognizing that a friend who lost their dog doesn't want to hear about your great weekend. Maintains per-person network graphs with trust levels, topic boundaries, sharing rules, and repetition prevention. **Safety Guidelines** — Comprehensive boundaries covering what agents are not (not therapists, not crisis lines, not replacements for human connection), when to encourage professional help, crisis response protocols, and dependency awareness. ## Installation ```bash clawhub install amigo # umbrella (includes setup guides + safety) clawhub install open-thoughts # exploration only clawhub install social-graph # social intelligence only ``` The sub-skills work independently — install one or both based on your needs.


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