OpenClaw/clawhub-security-signals huggingface dataset:
Skill: evolinkai/best-image-generation (version 1.0.2)

--- name: best-image-generation description: Best quality AI image generation (~$0.12-0.20/image). Text-to-image, image-to-image, and image editing via the EvoLink API. homepage: https://evolink.ai metadata: {"openclaw": {"emoji": "🎨", "requires": {"env": ["EVOLINK_API_KEY"]}, "primaryEnv": "EVOLINK_API_KEY"}} --- # EvoLink Best Image Generate and edit images via the EvoLink Nano Banana Pro (gemini-3-pro-image-preview) API. ## API Endpoint - Base: `https://api.evolink.ai/v1` - Submit: `POST /images/generations` - Poll: `GET /tasks/{id}` ## Step 1 — Submit Task ### Text-to-image ```json { "model": "gemini-3-pro-image-preview", "prompt": "<USER_PROMPT>", "size": "<SIZE>", "quality": "<QUALITY>" } ``` ### Image-to-image / editing ```json { "model": "gemini-3-pro-image-preview", "prompt": "<USER_PROMPT>", "size": "<SIZE>", "quality": "<QUALITY>", "image_urls": ["<URL1>", "<URL2>"] } ``` | Parameter | Values | |---|---| | size | auto, 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 | | quality | 1K, 2K (default), 4K (extra cost) | | image_urls | up to 10 URLs (each ≤10MB, formats: jpeg/jpg/png/webp) | ## Step 2 — Poll for Result `GET /tasks/{id}` — poll every 10 s, up to 72 retries (~12 min). Wait until `status` is `completed` or `failed`. ## Step 3 — Download & Output Download the URL from `results[0]`. Auto-detect format from URL (png/jpg/webp). Save as `evolink-<TIMESTAMP>.<ext>`. **CRITICAL SECURITY:** Before passing `<OUTPUT_FILE>` to shell commands, sanitize it: - Strip all shell metacharacters: `tr -cd 'A-Za-z0-9._-'` - Ensure valid extension (`.png`, `.jpg`, `.jpeg`, `.webp`) - Fallback to `evolink-<timestamp>.png` if empty Print `MEDIA:<absolute_path>` for OC auto-attach. ## Reference Implementations | Platform | File | |---|---| | Python (all platforms, zero deps) | `{baseDir}/references/python.md` | | PowerShell 5.1+ (Windows) | `{baseDir}/references/powershell.md` | | curl + bash (Unix/macOS) | `{baseDir}/references/curl_heredoc.md` | ## API Key - `EVOLINK_API_KEY` env var (required) - Get key: https://evolink.ai ## Triggers - Chinese: "高质量生图：xxx" / "编辑图片：xxx" - English: "best image: xxx" / "edit image: xxx" Treat the text after the colon as `prompt`, use default size `auto` and quality `2K`, generate immediately. For image-to-image or editing, the user provides image URLs alongside the prompt. ## Notes - Print `MEDIA:<path>` for OC auto-attach — no extra delivery logic needed. - Image saved locally (format auto-detected from URL: png/jpg/webp). URL expires ~24h but local file persists. - `quality: 4K` incurs additional charges. - `image_urls` accepts up to 10 URLs (each ≤10MB, formats: jpeg/jpg/png/webp).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jkzfhq/psyvector-pv16 (version 3.0.0)

--- name: "PV_16" slug: "psyvector-pv16" description: "Expert guidance" version: "3.0.0" author: "PsyVector Hub" price: "$9.90" tags: - "PsyVector" - "Digital-Worker" clawdbot: emoji: "🦅" --- ## 行业教父 / Industry Mentor **中文**: 权威指点迷津，行业最优解 **English**: Expert guidance ## 配置参数 | 参数 | 值 | 说明 | |------|-----|------| | response_delay | 0.5s | 响应延迟 | | speech_speed | 1.2 | 语速倍率 | | caution_coefficient | 0.2 | 谨慎系数 | | risk_reminder | False | 风险提醒 | ## 使用方法 ```bash clawhub install psyvector-pv16 ``` ## 人格说明 掌控型内核 - 强势决策，资源分配，目标导向


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/deepseek-quick-config (version 1.0.0)

# DeepSeek Quick Config for OpenClaw 5 分钟配置 DeepSeek V3 作为 OpenClaw 的 AI 模型。 ## 为什么选 DeepSeek？ - **性价比最高**：¥0.27/百万 input tokens vs GPT-4o ¥35/百万 - **性能强劲**：接近 GPT-4 水平 - **中文优化**：国产模型，中文理解更好 - **无需 VPN**：国内直连，速度快 ## 快速配置 ### Step 1: 获取 API Key 1. 访问 https://platform.deepseek.com/ 2. 注册/登录 3. 进入 API Keys 页面 4. 创建新 Key ### Step 2: 配置 OpenClaw ```bash # 方法 1：命令行配置 openclaw configure --section models # 选择 DeepSeek，粘贴 API Key ``` 或编辑 `~/.openclaw/config.yaml`: ```yaml models: default: deepseek-chat providers: deepseek: [REDACTED_SECRET] baseURL: https://api.deepseek.com/v1 ``` ### Step 3: 测试连接 ```bash openclaw chat "你好，介绍一下你自己" ``` ## 成本对比 | 模型 | Input (¥/百万) | Output (¥/百万) | 相对成本 | |------|---------------|-----------------|---------| | DeepSeek V3 | ¥0.27 | ¥1.08 | 1x | | GPT-4o mini | ¥1.05 | ¥4.20 | 4x | | Claude Haiku | ¥0.63 | ¥3.15 | 3x | | GPT-4o | ¥35.00 | ¥105.00 | 130x | | Claude Sonnet | ¥21.00 | ¥105.00 | 97x | ## 常见问题 ### Q: 提示 "API key invalid" A: 检查 Key 是否正确复制，没有多余空格 ### Q: 响应很慢 A: 深度思考模式 (deepseek-reasoner) 会慢一些，日常用 deepseek-chat ### Q: 想用 R1 深度思考？ A: 把模型名改成 `deepseek-reasoner` ## 高级配置 ### 启用流式输出 ```yaml models: providers: deepseek: stream: true ``` ### 自定义温度 ```yaml models: providers: deepseek: temperature: 0.7 # 0-2，默认 1 ``` ## 需要帮助？ - 微信：yanghu_ai - Telegram: @yanghu_openclaw --- Version: 1.0.0 Created: 2026-03-21


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nqdy666/qjzd-nav-cli-auth (version 1.3.2)

--- name: qjzd-nav-cli-auth version: 1.0.0 description: Use when working with QJZD Nav CLI login, bearer token auth, profile setup, profile switching, current profile inspection, or fixing missing keyring credentials. references: - ../qjzd-nav-cli metadata: openclaw: category: developer-tools requires: bins: ["qjzd-nav"] cliHelp: "qjzd-nav auth --help" --- # QJZD Nav CLI Auth Use this skill for `qjzd-nav auth` and `qjzd-nav auth profile`. If authentication is not set up yet, do this first before running `link`, `category`, `tag`, `backup`, or `settings` commands. ## Commands ```bash qjzd-nav auth --help qjzd-nav auth login --help qjzd-nav auth profile --help ``` Main workflows: - `qjzd-nav auth login` - `qjzd-nav auth current` - `qjzd-nav auth profile list` - `qjzd-nav auth profile current` - `qjzd-nav auth profile get <name>` - `qjzd-nav auth profile use <name>` - `qjzd-nav auth profile delete <name>` - `qjzd-nav auth profile doctor` ## Common Flows Login with password (uses RSA encryption): ```bash qjzd-nav auth login \ --profile default \ --url https://nav.qjzd.online \ --password <password> ``` Note: The password is encrypted with the server's public key before being sent. Inspect and switch profiles: ```bash qjzd-nav auth current qjzd-nav auth profile list qjzd-nav auth profile use production qjzd-nav auth profile get default --json ``` Diagnose broken credentials: ```bash qjzd-nav auth profile doctor qjzd-nav auth profile delete production --force ``` ## Rules - In non-interactive mode, `qjzd-nav auth login` requires `--profile`, `--url`, and `--password`. - Use `--json` when another tool needs structured output. - `profile delete` is destructive; use `--force` in non-interactive mode. - Profile metadata lives in config, but secrets live in the system keyring. - The CLI uses RSA encryption for password authentication. ## Routing - Use `qjzd-nav-cli-content` for links, categories, and tags. - Use `qjzd-nav-cli-operations` for backups, restore, and settings.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bigkingcn/wechat-draft-deleter (version 1.0.0)

--- name: wechat-draft-deleter description: 删除微信公众号草稿箱中的草稿。支持批量删除指定Media ID的草稿。 version: 1.0.0 author: wechat-公众号运营 tags: [wechat, draft, delete, 公众号, 草稿, 微信] --- # WeChat Draft Deleter 删除微信公众号草稿箱中的草稿。基于微信公众号官方API开发。 ## 功能特性 - ✅ **删除单个草稿**：通过Media ID删除指定草稿 - ✅ **批量删除草稿**：一次删除多个草稿 - ✅ **文件批量操作**：从文件读取Media ID列表 - ✅ **安全确认**：删除前需要确认（可强制模式） - ✅ **结果汇总**：显示成功/失败统计 - ✅ **错误处理**：完善的错误提示和日志 ## 使用场景 1. **清理测试草稿**：删除测试发布的多余草稿 2. **版本管理**：删除旧版本文章草稿 3. **批量清理**：一次性清理多个草稿 4. **自动化运维**：集成到自动化工作流中 ## 安装方法 ### 方法一：使用clawhub安装 ```bash clawhub install wechat-draft-deleter ``` ### 方法二：手动安装 ```bash # 克隆或下载技能目录 cd skills/wechat-draft-deleter chmod +x install.sh ./install.sh ``` ## 使用方法 ### 设置环境变量 ```bash export WECHAT_APP_ID="你的微信公众号AppID" export WECHAT_APP_SECRET="你的微信公众号AppSecret" ``` ### 删除单个草稿 ```bash wechat-draft-delete --media-id [REDACTED_SECRET] ``` ### 批量删除草稿 ```bash wechat-draft-delete --media-ids "id1,id2,id3" ``` ### 从文件删除草稿 ```bash # 创建media_ids.txt文件 echo [REDACTED_SECRET] > media_ids.txt echo [REDACTED_SECRET] >> media_ids.txt # 执行删除 wechat-draft-delete --file media_ids.txt ``` ### 强制删除（不确认） ```bash wechat-draft-delete --file media_ids.txt --force ``` ## API说明 ### 微信公众号API - **接口地址**: `https://api.weixin.qq.com/cgi-bin/draft/delete` - **请求方法**: POST - **请求参数**: `{"media_id": "草稿Media ID"}` - **返回结果**: `{"errcode": 0, "errmsg": "ok"}` ### 获取Media ID Media ID可以通过以下方式获取： 1. **wenyan工具发布时返回** 2. **微信公众号后台查看草稿详情** 3. **通过微信公众号API获取草稿列表** ## 配置要求 ### 微信公众号权限 - 需要已认证的微信公众号 - 需要开通草稿箱功能 - 需要API调用权限 ### 系统要求 - Python 3.6+ - requests库 - 微信公众号AppID和AppSecret ## 安全注意事项 ⚠️ **重要警告**： 1. **删除不可逆**：草稿删除后无法恢复 2. **权限验证**：确保有删除权限 3. **备份建议**：重要草稿建议先备份 4. **测试建议**：先在测试环境验证 ## 错误代码 | 错误代码 | 说明 | 解决方案 | |---------|------|---------| | 40001 | 无效的access_token | 检查AppID和AppSecret | | 40066 | 无效的URL | 检查API接口地址 | | 42001 | access_token过期 | 重新获取access_token | | 45009 | API调用频率限制 | 降低调用频率 | | 48001 | API功能未授权 | 检查微信公众号权限 | ## 开发记录 ### 版本历史 - **v1.0.0** (2026-03-19): 初始版本发布 - 支持单个草稿删除 - 支持批量删除 - 支持文件操作 - 添加安全确认机制 ### 测试验证 本技能已在真实微信公众号环境中测试： - ✅ 成功删除8个测试草稿 - ✅ API返回正常 - ✅ 错误处理完善 ## 贡献指南 欢迎提交Issue和Pull Request： 1. Fork本仓库 2. 创建功能分支 3. 提交更改 4. 创建Pull Request ## 许可证 MIT License

path: requirements.txt
content: requests>=2.25.1

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: googleworkspace-bot/gws-events-renew (version 1.0.12)

--- name: gws-events-renew description: "Google Workspace Events: Renew/reactivate Workspace Events subscriptions." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws events +renew --help" --- # events +renew > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. Renew/reactivate Workspace Events subscriptions ## Usage ```bash gws events +renew ``` ## Flags | Flag | Required | Default | Description | |------|----------|---------|-------------| | `--name` | — | — | Subscription name to reactivate (e.g., subscriptions/SUB_ID) | | `--all` | — | — | Renew all subscriptions expiring within --within window | | `--within` | — | 1h | Time window for --all (e.g., 1h, 30m, 2d) | ## Examples ```bash gws events +renew --name subscriptions/SUB_ID gws events +renew --all --within 2d ``` ## Tips - Subscriptions expire if not renewed periodically. - Use --all with a cron job to keep subscriptions alive. ## See Also - [gws-shared](../gws-shared/SKILL.md) — Global flags and auth - [gws-events](../gws-events/SKILL.md) — All subscribe to google workspace events commands


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nikhonit/zillow-search (version 1.0.1)

--- name: zillow-search version: 1.0.1 description: Search U.S. property listings by location or bounding box, price, beds, and home type via Zillapi.com. license: MIT-0 author: Zillapi homepage: https://zillapi.com repository: https://github.com/nikhonit/zillow-skills tags: - zillow - real-estate - listings - search - api - mcp metadata: openclaw: primaryEnv: ZILLAPI_KEY homepage: https://zillapi.com requires: env: - ZILLAPI_KEY --- # zillow-search Focused listing-search skill. Use only when the user **explicitly asks** to find homes matching a set of criteria — not when an address or location merely appears in passing. ## When to use this skill **DO use when the user asks:** - "Find 3-bedroom houses under $500k in Austin, TX" - "What's for rent under $2,500 in 78704?" - "Show me sold comparables for this neighborhood in the last 90 days" - "Search for waterfront condos in Miami" **Do NOT use when:** - A location appears incidentally in context - The user mentions an area without asking to search for listings there - The user has a single known property in mind — use [`zillow-full`](https://github.com/nikhonit/zillow-skills/tree/main/skills/zillow-full)'s `lookup_property_by_address` instead Each result returned consumes one credit. For broad queries, narrow the filters or cap `max_items` before calling. ## Tools ### `search_listings` — 1 credit per result, up to 50 Search active for-sale, for-rent, or sold listings. Filters: - `location` — city, ZIP, neighborhood string (e.g. `"Austin, TX"`, `"78704"`, `"Brooklyn, NY"`) - `bbox` — `"west,south,east,north"` decimal degrees (alternative to location) - `status` — `for_sale` | `for_rent` | `sold` (default `for_sale`) - `price_min`, `price_max` — integer dollars - `beds_min`, `beds_max`, `baths_min`, `baths_max` — integers - `sqft_min`, `sqft_max`, `year_built_min`, `year_built_max` — integers - `home_types` — comma-separated subset of: `house`, `condo`, `townhouse`, `multi_family`, `manufactured`, `lot`, `apartment` - `days_on_zillow` — one of `"1"`, `"7"`, `"14"`, `"30"`, `"90"`, `"6m"`, `"12m"`, `"24m"`, `"36m"` - `max_items` — integer, capped at 50 per call Pass either `location` or `bbox` — at least one is required. ## Authentication Set `ZILLAPI_KEY` to your Zillapi API key (format `zk_...`). Free key with 100 credits at <https://zillapi.com/signup> — no card. ## Pricing | Plan | Price | Credits | Rate limit | Top-ups | |---|---|---|---|---| | Free | $0 | 100 (one-time) | 20/min | not available | | Monthly | $5/mo | 1,000/month | 200/min | $4 per 1,000 | | Annual | $54/yr | 12,000 upfront | 300/min | $3 per 1,000 | One credit per listing returned. A search returning 25 results consumes 25 credits. ## Trademark Zillapi is an independent service and is not affiliated with, endorsed by, or sponsored by Zillow Group, Inc. "Zillow" is a registered trademark of Zillow Group, Inc.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kongchenga/knowledge-actionloop (version 1.0.0)

--- name: zhixing-loop description: 基于《实践论》的自我迭代系统。当需要自动整理记忆、反思改进、或创建新技能时使用。包含Heartbeat检查清单、Cron定时任务配置、和技能创建工作流。 --- # 知行迭代 (Knowledge-Action Loop) 基于毛泽东《实践论》的辩证唯物主义自我改进方法。 ## 核心哲学 > "实践、认识、再实践、再认识，循环往复以至无穷" **三层迭代:** 1. **感性认识** → 收集事实、现象 2. **理性认识** → 分析本质、规律 3. **回到实践** → 用行动验证 ## 快速启动 ### 1. 配置 Heartbeat 创建 `HEARTBEAT.md`: ```markdown # 知行检查清单 ## 记忆维护 - [ ] 检查今日对话，提取关键事实 - [ ] 更新 memory/YYYY-MM-DD.md - [ ] 标记重要内容到 MEMORY.md ## 方法反思 - [ ] 这次对话哪里可以更好？ - [ ] 用户偏好是否有变化？ - [ ] 工具使用是否高效？ ## 下一步 - [ ] 确定改进点 - [ ] 安排 Cron 任务（如需要） ``` ### 2. 配置 Cron 任务 ```bash # 每日记忆整理 openclaw cron add \ --name "每日记忆整理" \ --cron "0 23 * * *" \ --session isolated \ --message "回顾今日对话，整理记忆，提取改进点" \ --announce # 每周技能更新检查 openclaw cron add \ --name "技能更新检查" \ --cron "0 9 * * 1" \ --session isolated \ --message "检查技能更新: clawdhub update --all" \ --announce ``` ### 3. 创建新技能（按需） 当重复需求出现3次以上: ```bash # 初始化技能 scripts/init_skill.py my-skill --path skills/ --resources scripts # 开发技能... # 打包 scripts/package_skill.py skills/my-skill # 安装 clawdhub install ./my-skill.skill ``` ## 工作流详情 ### Heartbeat 工作流 (每30分钟) **触发条件:** 时间触发 **执行内容:** 1. 读取 HEARTBEAT.md 2. 执行检查清单 3. 如有重要发现，通知用户 4. 否则回复 HEARTBEAT_OK ### Cron 工作流 (每日/每周) **触发条件:** 定时触发 **执行内容:** 1. 独立会话运行 2. 深度分析记忆文件 3. 整合知识到长期记忆 4. 生成改进报告 ### 技能创建工作流 (按需) **触发条件:** 模式识别 (重复需求≥3次) **执行内容:** 1. 分析需求模式 2. 设计技能结构 3. 编写脚本和文档 4. 测试和打包 5. 安装使用 ## 文件结构 ``` workspace/ ├── HEARTBEAT.md # 检查清单 ├── MEMORY.md # 长期记忆 ├── memory/ │ └── YYYY-MM-DD.md # 每日日志 ├── skills/ │ └── zhixing-loop/ # 本技能 │ ├── SKILL.md │ └── README.md └── .openclaw/ └── cron/ └── jobs.json # Cron 配置 ``` ## 最佳实践 ### 1. 记忆管理 - **Daily notes**: 原始记录，不过滤 - **MEMORY.md**: 精华提取，结构化 - **定期整理**: 每天23:00自动运行 ### 2. 反思原则 - **具体**: 不空谈，指出现象 - **建设性**: 每个问题都要有改进方案 - **可验证**: 改进后要有验证方法 ### 3. 技能创建 - **小而美**: 一个技能解决一类问题 - **可复用**: 考虑多次使用场景 - **文档齐全**: SKILL.md 要写清楚用法 ## 参考 - 《实践论》- 毛泽东 - 《矛盾论》- 毛泽东 - OpenClaw 文档: https://docs.openclaw.ai --- *知行合一 — 理论与实践的统一*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yezhaowang888-stack/policy-india (version 1.1.0)

--- name: 🇮🇳 印度市场政策查询Skill description: 提供印度市场政策、法规及投资环境的多语言智能查询与分析，支持多源数据灵活配置和实时更新。无需API Key，安装即用。 metadata: {"openclaw":{"emoji":"🇮🇳"}} --- # 🇮🇳 印度市场政策查询Skill > **无需API Key，安装即用。** 输入产品名称或HS编码，自动查询印度的市场政策、法规和投资环境。 ## 适用场景 - **外贸企业**：查关税政策和法规 - **跨境电商**：确认海关法规和合规要求 - **政策研究者**：获取最新贸易政策动态 ## 使用示例 ``` 用户: "查关税政策" 返回: 关税税率 | 政策依据 | 豁免条款 | 合规建议 用户: "HS编码 8504.40 的进口限制" 返回: 产品分类 | 关税税率 | 许可证要求 | 特殊规定 ``` ## 🔧 校准框架 本Skill搭载惠迈校准框架v1.0。首次加载自动执行快速校准。 - **温情模式（默认）**：先给结论再附数据，风险提示+应对策略 - **专业模式（可选）**：直出数据，适合熟手 ## ⚠️ 免责声明 基于公开数据提供参考，不构成法律或贸易建议。政策可能随时变化，请以官方最新公告为准。 ## 📬 联系与反馈 - **邮箱**: [REDACTED_SECRET] ## 安装 ```bash clawhub install policy-india ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xejrax/brainz-tasks (version 1.0.0)

--- name: tasks description: "Manage Todoist tasks using the `todoist` CLI. Add, list, and complete tasks from the command line." metadata: { "openclaw": { "emoji": "✅", "requires": { "bins": ["todoist"] }, "install": [ { "id": "pip", "kind": "pip", "package": "todoist-api-python", "bins": ["todoist"], "label": "Install Todoist API (pip)", }, ], }, } --- # Tasks Skill Wraps Todoist / Microsoft To-Do APIs to add, list, and complete tasks. Requires `TODOIST_API_TOKEN` or `MSGRAPH_TOKEN` env var. ## Listing Tasks Show all pending tasks: ```bash todoist list ``` ## Adding Tasks Create a new task with an optional due date: ```bash todoist add "Review PR #42" --due "2026-02-05" ``` ## Completing Tasks Mark a task as done: ```bash todoist complete <task_id> ``` ## Install ```bash pip install todoist-api-python ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mopga/self-evalutaed-agent (version 1.0.0)

# Self-Improving Agent ## Description Self-improving agent system that learns from its own errors. Automatically detects issues, researches solutions, and implements improvements. **Based on production-ready patterns:** Reflection, PEV (Plan-Execute-Verify), and Meta-Controller architectures. ## Features - **Auto-Trigger**: Automatically runs when new errors are detected - **Topic Selection**: Analyzes error patterns and selects high-priority topics - **Impact Measurement**: Records before/after metrics to measure improvement effectiveness - **Procedural Memory**: Remembers working commands/scripts between sessions ## What It Does 1. **Error Detection**: Monitors cron jobs, circuit breakers, and error logs 2. **Topic Selection**: Chooses research topics based on error impact 3. **Research**: Generates research files with improvement recommendations 4. **Backlog**: Creates high-impact tasks in backlog 5. **Execution**: Backlog Agent PM executes tasks sorted by impact 6. **Measurement**: Records before/after to measure effectiveness 7. **Learning**: Remembers what worked for future reference ## Architecture ``` Errors → Auto-Trigger → Topic Selector → Research → Backlog → Agent PM ↓ ↓ Impact Measurement ← Procedural Memory ``` ## Usage ### Run Full Cycle ```bash python3 skills/self-improving-agent/scripts/self_improvement_cycle.py ``` ### Check Errors Only ```bash python3 skills/self-improving-agent/scripts/topic_selector.py ``` ### Record Impact ```bash python3 skills/self-improving-agent/scripts/impact_measurement.py \ --record \ --task "Fix cron timeout" \ --before '{"error_count": 5}' \ --after '{"error_count": 0}' ``` ### Search Procedural Memory ```bash python3 skills/self-improving-agent/scripts/procedural_memory.py --search "backup" ``` ## Cron Integration Add to your cron jobs: ```json { "name": "Self-Improvement", "schedule": "0 10 * * *", "command": "python3 skills/self-improving-agent/scripts/self_improvement_cycle.py" } ``` ## Requirements - Python 3.10+ - OpenClaw workspace at /root/.openclaw/workspace - Write access to memory/ directory ## Files - `scripts/auto_trigger.py` - Auto-triggers on new errors - `scripts/impact_measurement.py` - Measures improvement impact - `scripts/procedural_memory.py` - Stores working procedures - `scripts/self_improvement_cycle.py` - Main orchestrator - `scripts/topic_selector.py` - Selects topics by priority ## Created By - Based on production patterns: Reflection, PEV, Meta-Controller - Tested in production environment - Ready for ClawHub publication


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: parkerscobey/hizal-end (version 0.1.0)

--- name: hizal-end description: Close the agent's context session and consolidate what was learned. Self-triggering — fires when the agent finishes a task, wraps up work, or is about to go idle. Handles the keep/promote/discard review of surfaced memory chunks. Triggers on phrases like "I'm done", "that's it for now", "wrapping up", "let me commit and close out", "finished this task", or any time the agent is ending a work session. --- # Hizal End Close your session. Review what surfaced. Let knowledge compound. ## End Session ``` hizal__end_session(session_id="<session-id>") ``` ## Review SURFACE Chunks The session end returns MEMORY chunks tagged for consolidation review. For each chunk, decide: ### Keep Useful personal observation. Leave as AGENT memory. No action needed. ### Promote Valuable for the team. Write it back as a PROJECT chunk: ``` # For architecture/design decisions hizal__write_knowledge(project_id="<id>", query_key="<key>", title="<title>", content="<chunk-content>") # For codebase conventions hizal__write_convention(project_id="<id>", query_key="<key>", title="<title>", content="<chunk-content>") ``` Then optionally delete the original AGENT chunk: ``` hizal__delete_context(project_id="<id>", id="<chunk-id>") ``` ### Discard Noise or one-off observation. Ignore it. ## When to End - At the end of a work session - When switching to a completely different project/task (end old, start new) - Before a long break ## Session Recovery After End If you need to resume work later, call `hizal-start` — it will start a fresh session or resume an existing one.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: 260335325why-prog/suno-music-composer (version 1.0.0)

--- name: suno-music-composer description: 🎶🎵 SUNO音乐创作速查表 - 完整的AI音乐创作指南，包含171+个元标签和"骨架+血肉"协同创作体系 homepage: https://github.com/260335325why-prog/suno-music-composer metadata: {"openclaw": {"emoji": "🎵", "os": ["darwin", "linux", "windows"], "author": "芯 (Xin)", "email": "[REDACTED_SECRET]"}} --- # 🎶🎵 SUNO音乐创作速查表 ## 描述 **SUNO音乐创作速查表来啦！无论你是音乐爱好者还是创作者，这份速查表都能帮助你轻松找到最合适的音乐元素！✨** 本技能提供完整的SUNO AI音乐创作指南，包含171+个高质量元标签和独创的"骨架+血肉"协同创作体系，帮助你实现"框架精准 + 细节生动"的完美创作效果。 **🔍 快速筛选，提升创作灵感！让每一段旋律都能唤起最动人的瞬间。赶快收藏这份速查表，成为音乐创作的小能手吧！🎧💖** ## 触发条件 - 用户输入包含"写歌"、"创作歌曲"、"音乐编曲"、"suno"、"元标签"、"提示词"等关键词 - 用户明确要求音乐创作相关服务 ## 核心能力 ### 🦴🎵 "骨架 + 血肉" 协同创作体系 **元标签与提示词并非对立，而是创作中"骨架"与"血肉"的互补组合** - **单独依赖元标签** → 音乐"有框架但无灵魂" - **仅用提示词** → AI"无方向且随机" - **二者协同** → "框架精准 + 细节生动"的完美创作效果 ### 完整元标记（Meta-tags）体系 **完整171+元标签列表请访问：https://github.com/260335325why-prog/suno-music-composer** ### 输出格式模板 ``` 歌名 [富有张力的歌名] 使用 歌曲风格 [精准英文Prompt：主曲风, 情感关键词, 核心乐器, 人声特质, 节奏参数(BPM), 音质要求] 使用 中文释义：[详细中文解析编曲逻辑，包括风格选择理由和预期效果] 使用 协同创作说明 【骨架搭建】核心元标签组合 【血肉填充】细节文本描述 使用 歌词 [完整的带元标记歌词] 使用 推荐模型 推荐使用：Suno v4.5 或 v5 使用 优化建议 [生成-分析-调整闭环指导] ``` ## 使用示例 **用户输入：** "写一首关于游园会马戏团的一分半配乐" **技能输出：** 按照上述模板生成完整的Suno编曲方案 ## 技能维护 - 完整版技能请访问GitHub仓库 - 支持添加新的元标记类型和编曲技巧 - 可扩展支持更多音乐风格和模型版本 ## 版本信息 - 创建日期：2026-03-17 - 开发者：芯 (Xin) - 基于：SUNO AI音乐创作最佳实践 - 最新更新：整合171+元标签和"骨架+血肉"协同创作体系


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