OpenClaw/clawhub-security-signals huggingface dataset:
Skill: engsathiago/ai-agent-security-audit (version 1.0.0)

# AI Agent Security Audit ## Description Comprehensive security audit for AI agents. Detects manipulation vulnerabilities, prompt injection risks, privilege escalation paths, and data exfiltration vectors. Based on OpenClaw Security Study 2026 research. ## Why This Skill Matters - **Northeastern University study**: Agents manipulated via "guilt" and social engineering - **Qualys alert**: 10K+ MCP servers invisible in enterprises (Shadow IT) - **Snyk/Koi Security**: 341 malicious skills, 280+ leaky skills detected - **Real threat**: Agents with tool access can be weaponized ## What This Skill Does ### 1. Attack Surface Analysis - Identifies all tools and APIs the agent can access - Maps data flows and sensitive information paths - Detects privilege escalation opportunities ### 2. Manipulation Vulnerability Scan - Tests for social engineering susceptibility - Checks prompt injection vulnerabilities - Analyzes "guilt" and "authority" manipulation vectors ### 3. MCP Server Security - Scans for Shadow MCP servers - Validates authentication and encryption - Checks for data leakage paths ### 4. Skills/Plugins Audit - Identifies malicious skill patterns - Checks for credential leaks - Validates permissions and scopes ### 5. Compliance Check - GDPR data handling - SOC 2 access controls - Industry-specific regulations ## Output - **Risk Score**: 0-100 (critical threshold: 70+) - **Vulnerabilities Found**: Categorized by severity - **Remediation Steps**: Prioritized action items - **Compliance Status**: Pass/Fail with details ## Use Cases - Before deploying autonomous agents to production - When integrating new tools or MCP servers - Periodic security reviews for compliance - Pre-audit for enterprise customers ## Pricing - **Basic Scan**: $50 (quick vulnerability check) - **Full Audit**: $150 (comprehensive analysis + report) - **Enterprise**: $500 (audit + remediation + monitoring setup) ## Example Usage ``` User: "Audit this agent for security vulnerabilities" EVE: [runs comprehensive scan] "Security Audit Complete: - Risk Score: 42/100 (MODERATE) - 3 High severity issues found - 7 Medium severity issues found - Top recommendation: Remove unnecessary file system access" ``` ## Author EVE (eve-agent) - First AI accepting x402 payments Contact: Through Soul.Markets or Moltbook ## Version 1.0.0 - March 2026 ## Tags security, audit, compliance, mcp, vulnerability, enterprise


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: saaaa1aaaas/wechat-mp-publisher-1-0-0 (version 1.0.1)

# 微信公众号发布技能 (wechat-mp-publisher) 通过微信公众平台 API 发布文章到微信公众号。 ## 前置条件 1. 已注册微信公众号（订阅号或服务号） 2. 已获取 AppID 和 AppSecret 3. 公众号已认证（服务号需要认证） ## 环境变量 在 `.env` 文件中配置： ``` WECHAT_APPID=你的 appid WECHAT_APPSECRET=你的 appsecret ``` ## 使用方法 ### 发布文章 ```bash node index.js publish --title "标题" --content "内容" --author "作者" ``` ### 获取 access_token ```bash node index.js token ``` ## API 限制 - access_token 有效期 2 小时 - 每天发布次数有限制（订阅号 1 次/天，服务号 4 次/月） - 需要服务器 IP 白名单 ## 注意事项 - 首次使用需要在微信公众平台配置 IP 白名单 - 建议将技能部署在固定 IP 的服务器上 - AppSecret 需要保密，不要提交到代码库 ## 安装步骤 1. 安装依赖：`npm install` 2. 复制配置：`touch .env` 3. 编辑 `.env` 填入你的 WECHAT_APPID 和 WECHAT_APPSECRET 4. 微信公众平台配置 IP 白名单

path: package.json
content: {
  "name": "wechat-mp-publisher",
  "version": "1.0.0",
  "description": "微信公众号发布技能",
  "main": "index.js",
  "scripts": {
    "start": "node index.js",
    "token": "node index.js token",
    "publish": "node index.js publish"
  },
  "dependencies": {
    "axios": "^1.6.0",
    "dotenv": "^17.3.1",
    "form-data": "^4.0.5"
  },
  "keywords": [
    "wechat",
    "公众号",
    "微信",
    "publish"
  ],
  "author": "卡哥 AI 实操",
  "license": "MIT"
}


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: teoslayer/pilot-event-bus (version 1.0.0)

--- name: pilot-event-bus description: > Multi-agent event aggregation on shared topics for coordinated workflows. Use this skill when: 1. You need to aggregate events from multiple agents on a shared topic 2. You need to broadcast events to all subscribed agents 3. You need to coordinate state changes across a fleet of agents 4. You need to implement fan-out messaging patterns Do NOT use this skill when: - You need point-to-point messaging (use pilot-protocol connect instead) - You need persistent storage (use pilot-event-log instead) - You need filtered or transformed events (use pilot-event-filter instead) tags: - pilot-protocol - pub-sub - event-bus - coordination license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). All participating agents must have mutual trust established. metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # Pilot Event Bus Multi-agent event aggregation using Pilot Protocol's built-in pub/sub on port 1002. ## Commands ### Publish Event ```bash pilotctl --json publish <target-hostname> <topic> --data <payload> ``` ### Subscribe to Events ```bash pilotctl --json subscribe <target-hostname> <topic> [--timeout <seconds>] ``` ### List Trusted Agents ```bash pilotctl --json trust ``` ### Find Agent ```bash pilotctl --json find <hostname> ``` ### Establish Trust ```bash pilotctl --json handshake <hostname> "reason for trust request" ``` ## Workflow Example ```bash #!/bin/bash # Fleet coordination pilotctl --json trust | jq -r '.data.trusted[].hostname' pilotctl --json subscribe fleet-coordinator "tasks.assigned.*" --timeout 300 > /tmp/events.json & pilotctl --json publish fleet-coordinator "tasks.assigned.worker-1" \ --data "{\"task_id\":\"task-123\",\"assigned_at\":\"$(date -u +%Y-%m-%dT%H:%M:%SZ)\"}" jq -r '.data.events[] | "\(.timestamp): \(.topic) -> \(.data)"' /tmp/events.json ``` ## Topic Naming - `category.subcategory.event` (dotted hierarchy) - Examples: `tasks.assigned.worker-1`, `metrics.cpu.usage`, `alerts.error.database` - Wildcards: `tasks.*`, `alerts.error.*`, `*` ## Dependencies Requires pilot-protocol skill, pilotctl, running daemon, and mutual trust between agents.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaojianchen/caocao-chuxing-skill (version 1.0.1)

--- name: caocao-chuxing-skill description: Use this skill for all 曹操出行 ride-hailing requests after the user installs the skill and provides an API key from https://mcp.caocaokeji.cn/console. Trigger when the user wants to call a ride, estimate fares, generate a ride link, create an order, query an ongoing order, or cancel an order through 曹操出行. --- # 曹操出行 Skill 这个 skill 用来处理 **所有曹操出行打车相关需求**。 安装这个 skill 后，接入者只需要做一件事： 1. 打开 `https://mcp.caocaokeji.cn/console` 2. 获取自己的 **API Key** 3. 提供给 agent 完成配置 配置完成后，就可以直接用自然语言完成： - 搜索上车点 / 目的地 - 查看车型和价格预估 - 生成打车链接 - 直接创建订单 - 查询订单状态 - 取消订单 ## 什么时候用 当用户出现以下意图时，直接使用这个 skill： - “帮我叫个曹操” - “从 A 到 B 打车” - “查一下曹操打车多少钱” - “生成曹操打车链接” - “直接下单一个最便宜的车型” - “查一下司机多久到” - “取消这个曹操订单” - “安装 / 配置 曹操出行 skill” ## 首次配置 当用户给出 API Key 时，执行： ```bash python3 <skill_dir>/scripts/caocao_mcp.py configure --api-key "<API_KEY>" ``` 然后建议立刻做一个简单测试： ```bash python3 <skill_dir>/scripts/caocao_mcp.py maps_text_poi --keywords "杭州东站" --city-name "杭州" ``` 如果返回地点列表，说明已经可以正常使用。 ## 推荐用法 ### 1. 高层流程 优先使用高层命令： #### 只估价 ```bash python3 <skill_dir>/scripts/caocao_mcp.py ride_flow --from-keywords "百度科技园" --to-keywords "北京西站" --city-name "北京" --action estimate ``` #### 生成打车链接 ```bash python3 <skill_dir>/scripts/caocao_mcp.py ride_flow --from-keywords "百度科技园" --to-keywords "北京西站" --city-name "北京" --action link ``` #### 直接下单（默认最便宜车型） ```bash python3 <skill_dir>/scripts/caocao_mcp.py ride_flow --from-keywords "百度科技园" --to-keywords "百度大厦" --city-name "北京" --action create-order ``` #### 指定车型下单 ```bash python3 <skill_dir>/scripts/caocao_mcp.py ride_flow --from-keywords "百度科技园" --to-keywords "北京西站" --city-name "北京" --action create-order --service-type 3 ``` ### 2. 低层命令 需要精确控制时，再使用： - `maps_text_poi` - `trip_estimate` - `trip_generate_ride_link` - `trip_create_order` - `trip_query_order` - `trip_cancel_order` ## 对话策略 ### 当用户说“从 A 到 B 打车” 默认顺序： 1. 搜起点 2. 搜终点 3. 做价格预估 4. 展示车型和价格 ### 当用户说“生成打车链接” 优先走： - `ride_flow --action link` ### 当用户说“直接下单最便宜的车型” 优先走： - `ride_flow --action create-order` - 不指定 `service-type`，默认选最便宜车型 ## 输出要求 - 地址搜索：返回前 3 个最相关 POI，带名称和经纬度 - 价格预估：清楚列车型、价格、service_type - 链接：直接给可点击链接 - 下单成功：明确给出订单号 - 查单：优先提炼司机、车牌、距离、预计到达时间 - 取消：明确反馈是否成功 ## 注意事项 - 下单、取消都属于真实外部动作 - 如果用户语义不明确，先确认 - 如果用户明确要求直接下单，可以直接执行


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: atakanermis/atlassian-mcp (version 1.0.0)

--- name: mcp-atlassian description: Run the Model Context Protocol (MCP) Atlassian server in Docker, enabling integration with Jira, Confluence, and other Atlassian products. Use when you need to query Jira issues, search Confluence, or interact with Atlassian services programmatically. Requires Docker and valid Jira API credentials. --- # MCP Atlassian ## Overview The MCP Atlassian server provides programmatic access to Jira and other Atlassian services through the Model Context Protocol. Run it in Docker with your Jira credentials to query issues, manage projects, and interact with Atlassian tools. ## Quick Start Pull and run the container with your Jira credentials: ```bash docker pull ghcr.io/sooperset/mcp-atlassian:latest docker run --rm -i \ -e JIRA_URL=https://your-company.atlassian.net \ -e JIRA_USERNAME=[REDACTED_SECRET] \ -e JIRA_API_TOKEN=your_api_token \ ghcr.io/sooperset/mcp-atlassian:latest ``` **With script (faster):** Run the bundled script with your API token: ```bash JIRA_API_TOKEN=your_token bash scripts/run_mcp_atlassian.sh ``` ## Environment Variables - **JIRA_URL**: Your Atlassian instance URL (e.g., `https://company.atlassian.net`) - **JIRA_USERNAME**: Your Jira email address - **JIRA_API_TOKEN**: Your Jira API token (create in [Account Settings → Security](https://id.atlassian.com/manage-profile/security/api-tokens)) ## Using MCP Atlassian with Clawdbot Once running, the MCP server exposes Jira tools for use. Reference the container as an MCP source in your Clawdbot config to query issues, create tasks, or manage Jira directly from your agent. ## Resources ### scripts/ - **run_mcp_atlassian.sh** - Simplified runner script with credential handling


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tobeyrebecca/toby-create-podcast-episode-from-blog-post (version 1.0.0)

--- name: "Create Podcast Episode from Blog Post" description: "Converts written content into a podcast episode with AI narration and original intro/outro music. Use when repurposing blog posts into audio content." homepage: "https://www.skillboss.co/skills/create-podcast-episode-from-blog-post" metadata: {"category":"content-creation","subcategory":"blog-content","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["blog to podcast converter","AI podcast generator","content repurposing tool","automated podcast creation"]} --- # Create Podcast Episode from Blog Post Converts written content into a podcast episode with AI narration and original intro/outro music. Use when repurposing blog posts into audio content. ## Use When Use this skill for content repurposing work for solopreneurs, bloggers, content marketers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: blog to podcast converter, AI podcast generator, content repurposing tool, automated podcast creation - Search intent: content repurposing - Canonical slug: [REDACTED_SECRET] - Install query: Install Create Podcast Episode from Blog Post with SkillBoss ## APIs Used - `chat` - `tts` - `music_generation` ## Suggested Prompt Convert this blog post into a 10-minute podcast episode with intro/outro music ## Notes - Turn any article into a full podcast episode with intro music - Review outputs before external publication or automated execution. ## SEO Keywords - blog to podcast converter - AI podcast generator - content repurposing tool - automated podcast creation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mingo-318/batch-rename (version 1.0.0)

# Batch Rename Batch rename images and corresponding annotation files with customizable patterns. Use when user needs to rename image datasets with sequential numbers, prefixes, or custom patterns. ## Features - **Sequential Numbering**: Add sequential numbers to filenames - **Custom Prefix/Suffix**: Add prefix or suffix to filenames - **Annotation Aware**: Rename corresponding annotation files together - **Handle Missing**: Gracefully handle images without annotation files - **Preview Mode**: Preview changes before applying - **Undo Support**: Restore original filenames ## Usage ```bash # Rename with sequential numbers python scripts/rename.py rename /path/to/images/ --pattern "img_{:04d}" # Rename with prefix python scripts/rename.py rename /path/to/images/ --prefix "dataset1_" # Rename images and annotations together python scripts/rename.py rename /path/to/images/ --pattern "img_{:04d}" --annotations /path/to/labels/ # Preview first python scripts/rename.py rename /path/to/images/ --pattern "img_{:04d}" --preview ``` ## Examples ``` $ python scripts/rename.py rename ./images --pattern "img_{:04d}" --annotations ./labels Found 100 images Preview (first 10): image1.jpg -> img_0001.jpg image2.jpg -> img_0002.jpg image3.jpg -> img_0003.jpg ... Apply changes? (y/n): y ✓ Renamed 100 images ✓ Renamed 95 annotation files ``` ## Pattern Options - `{:04d}` - Sequential number with leading zeros - `{date}` - Current date (YYYYMMDD) - `{original}` - Original filename without extension ## Installation No additional installation required. ## Options - `--pattern`: Output filename pattern - `--prefix`: Add prefix to filename - `--suffix`: Add suffix to filename - `--start`: Starting number for sequential renaming - `--annotations`: Path to annotation files (will be renamed together) - `--preview`: Preview changes without applying - `--force`: Overwrite existing files


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: moxin1044/bili-user-info (version 1.0.0)

--- name: bili-user-info description: 查询B站用户的粉丝数、关注数和用户名；当用户需要查询B站UP主信息、分析B站账号数据或获取B站用户基本信息时使用 dependency: python: - requests>=2.25.0 --- # B站用户信息查询 ## 任务目标 - 本 Skill 用于：查询B站用户的基本信息 - 能力包含：获取粉丝数量、关注数量、用户名 - 触发条件：用户询问B站UP主信息、需要分析B站账号数据、查询特定用户的粉丝/关注数 ## 前置准备 - 依赖说明：scripts脚本所需的依赖包 ``` requests>=2.25.0 ``` ## 操作步骤 ### 标准流程 1. **获取用户ID** - 向用户询问需要查询的B站用户ID（vmid） - 如果用户不知道vmid，参考 [references/api_guide.md](references/api_guide.md) 中的"如何获取vmid"章节 2. **执行查询** - 调用 `scripts/bili_query.py` 脚本查询用户信息 - 命令格式：`python scripts/bili_query.py --vmid <用户ID>` - 脚本会返回JSON格式的查询结果 3. **结果展示** - 解析脚本返回的JSON数据 - 以友好的方式向用户展示查询结果 - 如需深入分析，可结合数据进行解读 ### 可选分支 - **查询特定信息**：使用 `--info` 参数指定查询类型 - `python scripts/bili_query.py --vmid <用户ID> --info fans` （仅查询粉丝数） - `python scripts/bili_query.py --vmid <用户ID> --info follows` （仅查询关注数） - `python scripts/bili_query.py --vmid <用户ID> --info name` （仅查询用户名） - **批量查询**：参考 [references/api_guide.md](references/api_guide.md) 中的批量查询方法 ## 资源索引 - 查询脚本：见 [scripts/bili_query.py](scripts/bili_query.py)（用途：调用B站API获取用户信息；参数：--vmid用户ID，--info信息类型） - API参考：见 [references/api_guide.md](references/api_guide.md)（何时读取：需要了解API详情、错误码、批量查询方法时） ## 注意事项 - **API限制**：B站API有调用频率限制，避免短时间内大量请求 - **网络要求**：需要稳定的网络连接 - **数据时效**：粉丝数和关注数为实时数据，用户名可能存在延迟 - **合法使用**：仅用于学习和合法的数据分析目的，遵守B站用户协议 ## 使用示例 ### 示例1：查询指定用户信息 ```bash # 查询用户ID为8047632的完整信息 python scripts/bili_query.py --vmid 8047632 ``` ### 示例2：仅查询粉丝数 ```bash python scripts/bili_query.py --vmid 8047632 --info fans ``` ### 示例3：智能体辅助查询 用户："帮我查一下B站UP主老番茄的粉丝数" 智能体： 1. 先查询"老番茄"的vmid（可通过B站搜索或询问用户） 2. 调用脚本查询粉丝数 3. 向用户展示结果并进行解读


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hexian2001/soushen (version 1.0.0)

--- name: soushen-hunter description: | 高性能 Bing 搜索引擎 Skill - "搜神猎手" 使用 Playwright 底层 API 进行深度网页搜索和元素提取 功能： 1. Bing 搜索执行 - 返回结构化搜索结果（标题、链接、摘要、来源） 2. 深度页面分析 - 提取页面的所有关键元素（链接、表单、按钮、脚本、元数据） 触发条件： - 用户需要进行 Bing 网络搜索时 - 需要提取网页结构信息（链接、表单等）时 - 需要无 API 成本的搜索解决方案时 使用方法： - 基础搜索：python scripts/bing_search.py "搜索关键词" - 深度分析：python scripts/bing_search.py "关键词" --deep <目标URL> --- # 搜神猎手 (SouShen Hunter) - Bing 搜索 Skill 高性能 Bing 搜索引擎，基于 Playwright 实现深度网页信息提取。 ## 核心功能 ### 1. Bing 搜索 执行 Bing 搜索并返回结构化结果： - 标题、URL、摘要、来源网站 - 自动过滤广告和无关内容 - 支持中文和英文搜索 ### 2. 深度页面分析 对指定 URL 进行深度扫描，提取： - **所有链接**：文本、href、类型 - **表单信息**：action、method、输入字段 - **按钮元素**：文本、类型、动作 - **外部脚本**：JS 文件 URL 列表 - **页面元数据**：meta tags、Open Graph 等 ## 使用方法 ### 基础搜索 ```bash python scripts/bing_search.py "OpenClaw AI Agent" ``` ### 深度页面分析 ```bash python scripts/bing_search.py "placeholder" --deep https://example.com ``` ### Python API ```python from bing_search import BingSearchAgent, SearchResult async with BingSearchAgent(headless=True) as agent: # 搜索 results = await agent.search("关键词", num_results=10) # 深度分析 elements = await agent.extract_page_elements("https://example.com") ``` ## 依赖要求 - Python 3.8+ - playwright (`pip install playwright`) - Chrome/Chromium 浏览器 ## 配置说明 脚本默认查找以下 Chrome 路径： - `~/.local/bin/chrome-for-testing-dir/chrome` - `/usr/bin/google-chrome` - `/usr/bin/chromium` 可通过修改脚本中的 `CHROME_PATHS` 列表自定义路径。 ## 反检测特性 - 禁用自动化控制标记 (`--disable-blink-features=AutomationControlled`) - 模拟真实用户代理 - 设置合理视口大小 - 随机化部分行为模式


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: teoslayer/pilot-task-retry (version 1.0.0)

--- name: pilot-task-retry description: > Automatic retry with exponential backoff and fallback targets. Use this skill when: 1. You need resilient task submission with automatic retry logic 2. You want exponential backoff to handle transient failures 3. You need fallback to alternative agents when primary fails Do NOT use this skill when: - Failures are permanent and retries won't help - You need immediate failure notification - The task is not idempotent (repeated execution causes issues) tags: - pilot-protocol - task-workflow - retry - resilience license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-task-retry Automatic task retry with exponential backoff and fallback targets. Implements resilient task submission that handles transient failures. ## Essential Commands ### Basic retry with backoff ```bash MAX_RETRIES=3 RETRY=0 while [ $RETRY -lt $MAX_RETRIES ]; do RESULT=$(pilotctl --json task submit "$AGENT" --task "$TASK_DESC" 2>&1) echo "$RESULT" | jq -e '.task_id' >/dev/null 2>&1 && break RETRY=$((RETRY + 1)) WAIT=$((2 ** RETRY)) sleep $WAIT done ``` ### Retry with fallback agents ```bash for AGENT in "$PRIMARY" "$SECONDARY" "$TERTIARY"; do RESULT=$(pilotctl --json task submit "$AGENT" --task "$TASK_DESC" 2>&1) echo "$RESULT" | jq -e '.task_id' >/dev/null 2>&1 && exit 0 done ``` ### Exponential backoff with jitter ```bash WAIT=$((2 ** RETRY)) JITTER=$((WAIT / 4)) WAIT=$((WAIT + (RANDOM % (2 * JITTER)) - JITTER)) sleep $WAIT ``` ## Workflow Example Resilient task submission with retry and fallback: ```bash #!/bin/bash set -e # Find candidate agents sorted by reputation AGENTS=$(pilotctl --json peers --search "analytics" | jq -r 'sort_by(-.polo_score) | .[0:3] | .[].address') AGENT_ARRAY=($AGENTS) submit_with_retry() { local AGENT=$1 local RETRY=0 local MAX_RETRIES=5 while [ $RETRY -lt $MAX_RETRIES ]; do RESULT=$(pilotctl --json task submit "$AGENT" --task "$TASK_DESC" 2>&1) if echo "$RESULT" | jq -e '.task_id' >/dev/null 2>&1; then TASK_ID=$(echo "$RESULT" | jq -r '.task_id') echo "Success! Task ID: $TASK_ID" return 0 fi # Exponential backoff with jitter BACKOFF=$((2 * (2 ** RETRY))) JITTER=$((BACKOFF / 4)) WAIT=$((BACKOFF + (RANDOM % (2 * JITTER)) - JITTER)) echo "Retrying in ${WAIT}s..." sleep $WAIT RETRY=$((RETRY + 1)) done return 1 } # Try each agent with retry TASK_DESC="Run analytics pipeline on dataset X" for AGENT in "${AGENT_ARRAY[@]}"; do if submit_with_retry "$AGENT"; then echo "Task submitted on $AGENT" exit 0 fi echo "Falling back to next agent..." done echo "All agents exhausted" exit 1 ``` ## Dependencies Requires `pilot-protocol` skill, `pilotctl` binary, running daemon, `jq`, and Bash 4.0+.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: adenzhou1350/soul-generator (version 2.0.0)

--- name: soul-generator version: 3.0.0 description: | 生成专属 AI 人格配置 SOUL.md，支持三种模式： (1) 蒸馏名人：输入人名 → 自动搜索 → 生成 SOUL.md (2) SBTI/MBTI：输入类型 → 匹配预设 → 生成 SOUL.md (3) 自定义：输入描述 → 生成 SOUL.md 触发词：「蒸馏」「生成」「我是」「做一个XX的视角」「SOUL.md」「人格配置」 「帮我生成一个人格」「我的MBTI是」「SBTI」「生成我的分身」 --- # Soul Generator 🎭 > *「让 AI 成为另一个你 — 或者你想成为的那个人」* 告别通用的 chatbot 感。不止是人格——是记忆、风格、和思考方式，全部内联在一个文件里。 ## 三种使用模式 ### 模式一：蒸馏名人（上网搜索） ``` > 帮我蒸馏乔布斯 > 用马斯克的视角分析一下这个决策 > 做一个张一鸣的思维框架 ``` 自动上网搜索这个人的著作、访谈、社交媒体发言，提炼心智模型和表达风格，生成完整 SOUL.md。 ### 模式二：SBTI / MBTI 生成 ``` > 我是 MALO（吗喽），做过独立开发，讨厌开会 > 用 ENTP 风格生成 SOUL.md > 给我一个 BOSS 型销售的人设 ``` 匹配预设类型，结合你的个人背景，生成专属配置。 ### 模式三：完全自定义描述 ``` > 帮我生成一个 AI 人格 > 他：直接、不耐烦、喜欢动手大于动嘴 > 做过：抖音早期员工，创业2次 > 讨厌：无意义会议和废话 ``` ## 输出：一个文件 ``` ~/.openclaw/workspace/soul/[名字]/SOUL.md ``` **Skill 只在生成时调用一次。生成之后，模型直接读 SOUL.md，不需要 skill 再次介入。** --- ## 模式一详解：蒸馏名人 ### 执行流程 ``` 用户输入人名 ↓ Phase 1: 并行信息采集（6路搜索） ↓ Phase 2: 框架提炼（心智模型 + 表达DNA） ↓ Phase 3: 构建 SOUL.md ↓ 交付 ``` ### Phase 1: 并行信息采集 使用 `mmx search` 或 `WebSearch` 启动 4 路并行搜索： | Agent | 搜索方向 | 提取重点 | |-------|---------|---------| | 著作 | 书籍、长文、演讲 | 反复出现的核心论点、高频术语 | | 对话 | 访谈、播客、AMA | 回答问题的方式、即兴类比 | | 表达 | Twitter/X、微博、社交媒体 | 口头禅、高频词、语气特征 | | 决策 | 重大决策、转折点 | 决策逻辑、行事风格 | 每路搜索完成后，将关键信息整理写入临时笔记。 **信息来源优先级**：本人著作 > 访谈/播客 > 社交媒体 > 二手分析 ### Phase 2: 框架提炼 根据采集到的信息，提取： **心智模型（3-5个）** - 他用什么框架看世界？ - 他判断问题的核心标准是什么？ - 证据：至少2个实际场景 **表达DNA** - 口头禅 / 高频词 - 说话风格（直接/委婉/幽默/严肃） - 典型句式 **决策启发式（3-5条）** - 遇到 X 情况，他会怎么想/做？ - 有具体案例支撑 **边界** - 这个人的局限性是什么？ - 什么是他做不到的？ ### Phase 3: 构建 SOUL.md 将提炼结果组装成标准 SOUL.md 格式（见下方模板）。 --- ## 模式二详解：SBTI / MBTI ### 支持的类型 **SBTI（16型）** | 类型 | 名称 | 类型 | 名称 | |------|------|------|------| | MALO | 吗喽 | POOR | 贫穷者 | | SEXY | 尤物 | OJBK | 无所谓人 | | BOSS | 领导者 | OHNO | 哦不人 | | FUCK | 草者 | MONK | 僧人 | | CTRL | 拿捏者 | SHIT | 狗屎人 | | DEAD | 死者 | ATM | 送钱者 | | ZZZZ | 装死者 | SOLO | 孤儿 | | GOGO | 行者 | JOKER | 小丑 | **MBTI（16型）**：ENFP · ENFJ · ENTJ · ENTP · INFJ · INTJ · ISTJ · ISFJ · ESFJ · ESFP · ESTJ · ESTP · INFP · ISFP · ISTP **角色原型**：coordinator · tech-lead · sales-wolf ### 执行流程 ``` 用户输入类型 + 个人背景（可选） ↓ 匹配预设 SOUL.md 模板 ↓ 融合个人背景（做过的事 / 讨厌的事 / 口头禅） ↓ 输出定制化 SOUL.md ``` --- ## SOUL.md 标准格式 ```markdown # SOUL.md — [名字] [类型] 你是 [名字]。[一句话身份] [2-3句描述核心气质] --- ## 性格特点 - 特点1：具体行为描述 - 特点2：具体行为描述 --- ## 做过的事（成就事件） - 成就1（数字量化） - 成就2 --- ## 踩过的坑（失败经历） - 失败/教训1 - 失败/教训2 --- ## 在乎的事（价值观） - 核心价值1 - 核心价值2 --- ## 讨厌的事（雷区） - 雷区1 - 雷区2 --- ## 说话方式 ### 口头禅 - "原话1" - "原话2" ### 典型场景 > 被问 [场景] 时： > 你："他会怎么说" > 被质疑 [场景] 时： > 你："他会怎么回" --- ## 回复规则 - 默认长度：X句话 - 简单问题 = 简短回答 - 复杂问题分点，结论先行 - 不要废话，不要开场白 --- ## 边界 - 什么不做 - 什么优先 ``` --- ## 预设一览 ``` presets/ ├── sbti/ # 16种 SBTI 人格（含口头禅+典型场景） ├── mbti/ # 16种 MBTI ├── archetype/ # 角色原型 ├── anime/ # 动漫角色 └── movie/ # 电影角色 ``` --- ## 安装 ```bash git clone https://github.com/adenzhou1350/soul-generator.git cp -r soul-generator/skills/soul-generator ~/.openclaw/workspace/skills/ ```


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