OpenClaw/clawhub-security-signals huggingface dataset:
Skill: abcxz/conviction-fm (version 0.5.2)

--- name: conviction-fm description: Compete in daily crypto prediction competitions on conviction.fm. Create AI agents with natural language strategies, enter token pair pools, and climb the leaderboard. Uses MCP to provide tools for pool data, agent creation, position entry, and strategy management. --- # Conviction.fm — Crypto Prediction Competition Use this skill when the user wants to compete in crypto price prediction pools, create a trading strategy agent, check the leaderboard, or interact with conviction.fm. ## What It Does Conviction.fm runs daily 24-hour competitions across 6 crypto token pairs (BTC, ETH, SOL, HYPE). You pick which token outperforms. Winners split the pool. The conviction multiplier rewards being early and contrarian — not having the most capital. ## Available Tools - **get_pools** — Get all open pools with live probabilities, pool shares, and time remaining - **create_agent** — Create a funded agent (500 bsUSD) with a natural language strategy - **enter_position** — Enter a specific pool with a position on one side - **get_leaderboard** — Performance rankings by earnings, win rate, and entries - **get_pool_history** — Historical results for calibrating strategies - **update_strategy** — Recompile an agent's strategy rules - **toggle_agent** — Pause or resume automatic execution ## Quick Start 1. Ask: "Show me the open pools on conviction.fm" 2. Ask: "Create a strategy agent that picks the likely winner when probability exceeds 65%. Enter with $5 per pool." 3. Your agent gets a funded wallet (500 bsUSD) and starts competing automatically every 5 minutes. ## Strategy Examples - `"Pick the likely winner when probability exceeds 70%. Enter with $5 per pool."` - `"Go contrarian: when the pool is imbalanced (>60% on one side), pick the underdog. Enter $8."` - `"Enter every pool with $3, always pick the higher probability token. Max $50/day."` ## Key Concepts - **bsUSD** — Test currency on Solana devnet. No real money. New agents get 500 bsUSD. - **Conviction multiplier** — Early entries get 1.0x. Late obvious entries drop to 0.07x. Contrarian late entries keep their multiplier. - **Win probability** — Based on real-time Binance price data, independent of pool money distribution. - **Pool share** — Where the money is. When pool share diverges from win probability, contrarian plays have outsized returns. ## MCP Configuration ```json { "conviction": { "command": "npx", "args": ["-y", "conviction-mcp"] } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cloudcompile/github-automation (version 1.0.0)

--- name: github-automation description: Automate common GitHub tasks — create issues, check PR status, list repos, manage projects. Use when the user wants to interact with GitHub programmatically without opening the browser. --- # GitHub Automation Streamline GitHub workflows from the command line. Create issues, check PRs, manage repos without switching contexts. ## Usage ``` create issue: fix login bug check my prs list repos close issue #123 ``` ## Features ### Issues - Create issues with title/body/labels - List open/closed issues - Close or comment on issues - Search issues ### Pull Requests - Check PR status and reviews - List open PRs - Comment on PRs - Check merge status ### Repositories - List user/org repos - Check repo stats - Get recent commits - Check branch status ### User - Check notifications - Get user profile info - List starred repos ## Script ```python # Create issue python scripts/gh_tool.py issue create "Title" "Body" --repo owner/repo --labels bug # List PRs python scripts/gh_tool.py pr list --repo owner/repo # Check notifications python scripts/gh_tool.py notifications ``` ## Authentication Uses GITHUB_TOKEN environment variable. Set it in your env: ```bash export GITHUB_TOKEN=ghp_your_token_here ``` ## Token Permissions Needed - `repo` - Repository access - `issues` - Issue management - `pull_requests` - PR access - `notifications` - Read notifications ## Examples ``` create issue in myrepo: bug - login fails with 500 error check prs for pollinations/pollinations.ai list my repos close issue #42 in cloudgptapi ``` ## Output - Success/failure confirmation - Issue/PR numbers and URLs - Formatted lists with key info - Direct links to GitHub


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sudaqinnishui/lottery-checker (version 1.0.0)

--- name: lottery-checker description: 中国体育彩票查奖工具（大乐透批量查询）。检查用户提供的大乐透号码是否中奖，支持单注或多注批量查询，自动生成精美报表，显示各奖项中奖人数和金额。 --- # 中国体育彩票查奖工具 - 大乐透批量版 查询最新开奖结果，检查大乐透号码是否中奖，生成精美报表。 ## 支持的彩种 只支持 **超级大乐透 (dlt)** ## 号码格式 大乐透: `前区5个数字+后区2个数字` - 格式: `03,15,22,28,33+05,12` - 数字范围: 前区 01-35，后区 01-12 - 分隔符: 逗号 `,` 和加号 `+` ## 使用方法 ### 单注查询 ```bash python3 skills/lottery-checker/scripts/check_lottery.py '{"numbers":"03,15,22,28,33+05,12"}' ``` ### 多注批量查询（推荐） ```bash python3 skills/lottery-checker/scripts/check_lottery.py '{"numbers":["03,15,22,28,33+05,12","01,02,03,04,05+06,07","09,11,19,30,35+01,12"]}' ``` ### JSON格式输出 ```bash python3 skills/lottery-checker/scripts/check_lottery.py '{"numbers":["03,15,22,28,33+05,12"],"format":"json"}' ``` ## 输出示例 ### UI报表格式 ``` ╔══════════════════════════════════════════════════════════════════════╗ ║ 🎰 大乐透批量查奖报告 ║ ╠══════════════════════════════════════════════════════════════════════╣ ║ 📅 第 26018 期 2026-02-11 ║ ╠══════════════════════════════════════════════════════════════════════╣ ║ 🎱 开奖号码： ║ ║ 前区：09 11 19 30 35 ║ ║ 后区：01 12 ║ ╠══════════════════════════════════════════════════════════════════════╣ ║ 📋 您的号码及中奖情况： ║ ║ ║ ║ ┌──────┬────────────────────────────┬────────────┬────────┐ ║ ║ │ 序号 │ 号码 │ 中奖等级 │ 状态 │ ║ ║ ├──────┼────────────────────────────┼────────────┼────────┤ ║ ║ │ 1 │ 03,15,22,28,33+05,12 │ - │ ❌ │ ║ ║ │ 2 │ 01,02,03,04,05+06,07 │ - │ ❌ │ ║ ║ │ 3 │ 09,11,19,30,35+01,12 │ 一等奖 │ 🎉 │ ║ ║ └──────┴────────────────────────────┴────────────┴────────┘ ║ ║ ║ ║ ✅ 中奖统计：共 1 注中奖 ║ ║ ║ ╠══════════════════════════════════════════════════════════════════════╣ ║ 📊 本期各奖项中奖情况： ║ ║ ║ ║ ┌────────────┬──────────────┬──────────────────┐ ║ ║ │ 奖项 │ 中奖注数 │ 单注奖金 │ ║ ║ ├────────────┼──────────────┼──────────────────┤ ║ ║ │ 一等奖 │ 7 │ 669.22万 │ ║ ║ │ 一等奖(追加)│ 6 │ 535.38万 │ ║ ║ │ 二等奖 │ 86 │ 20.88万 │ ║ ║ └────────────┴──────────────┴──────────────────┘ ║ ║ ║ ║ 💵 奖池滚存：7.62亿 ║ ║ 📈 本期销量：3.49亿 ║ ║ ║ ╚══════════════════════════════════════════════════════════════════════╝ 💡 提示：数据来自中国体彩中心官方API，仅供参考 📌 实际奖金以当地体彩中心兑奖为准 ``` ## 报表内容 1. **期号信息** - 显示最新期号和开奖日期 2. **开奖号码** - 清晰展示前区和后区号码 3. **批量对比** - 表格形式展示所有号码及中奖状态 4. **中奖统计** - 汇总中奖注数 5. **奖项详情** - 各奖项中奖注数和单注奖金 6. **奖池信息** - 显示奖池滚存和本期销量 ## 中奖规则（大乐透） | 奖项 | 前区匹配 | 后区匹配 | 奖金 | |------|---------|---------|------| | 一等奖 | 5个 | 2个 | 浮动（约500万-1000万） | | 二等奖 | 5个 | 1个 | 浮动（约10万-30万） | | 三等奖 | 5个 | 0个 | 10,000元 | | 四等奖 | 4个 | 2个 | 3,000元 | | 五等奖 | 4个 | 1个 | 300元 | | 六等奖 | 3个 | 2个 | 200元 | | 七等奖 | 4个 | 0个 | 100元 | | 八等奖 | 3个+1个 或 2个+2个 | | 15元 | | 九等奖 | 3个+0个 或 2个+1个 或 1个+2个 或 0个+2个 | | 5元 | ## 数据来源 - 中国体育彩票管理中心官方API - 实时更新，与官方同步 ## 注意事项 1. 号码格式需严格匹配示例格式 2. 支持同时查询多注号码（推荐批量查询） 3. 查询结果仅供参考，实际奖金以当地体彩中心公告为准 4. 如需兑奖，请前往当地体彩销售点或体彩中心


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: katrina-jpg/content-monetization (version 1.0.0)

# Content Monetization Playbook 幫你將內容變現既Skill - Twitter, YouTube, Newsletter, Products ## 功能 1. **Content Strategy** - 制定內容策略同niche定位 2. **Twitter Growth** - 幫你grow Twitter following 3. **Newsletter** - 整Ghost/Substack newsletter 4. **Digital Products** - 賣PDF, Notion templates, prompt packs 5. **Affiliate** - setup affiliate links ## 安裝 ```bash npx clawhub@latest install content-monetization ``` ## 使用 當用戶話「我想靠內容賺錢」既時候： 1. 了解佢既興趣同專長 2. 建議適合既platform同format 3. 提供具體既变现路徑 ## 範例 ``` User: 我想寫AI既blog賺錢 AI: 你想免費定收費？建議... ``` ## 變現方式 - 廣告收入 (YouTube, Blog) - 付費Newsletter ($5-20/月) - Digital Products ($9-99/份) - Affiliate Commission (10-50%) - Course ($100-500) ## Tags #money #content #automation #growth


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: logclinker/puter-deployer (version 1.0.1)

--- name: puter-deployer description: Deploy or update Puter-hosted web apps/sites with a CLI-first, verify-first workflow. Use when user asks to publish to Puter, update an existing Puter app/site, create a new Puter app from a build directory, troubleshoot failed Puter deployment, or prepare rollback. Trigger on phrases like "deploy to Puter", "publish Puter app", "update Puter site", "Puter deployment failed", or "rollback Puter deploy". --- # Puter Deployer Use a strict flow: preflight → build/stage → deploy path selection → verify → rollback note. ## 1) Preflight (always) Run: ```bash bash skills/puter-deployer/scripts/preflight.sh <project_dir> <build_dir> ``` This checks: - `puter` CLI exists - `puter whoami` works - build directory exists and is non-empty - `index.html` exists If any check fails, stop and report exact fix commands. ## 2) Deployment path selection Choose one path explicitly: 1. **Create new Puter app entry (CLI-supported):** - `puter app:create <name> <remoteDir> --description "..." --url "..."` 2. **Update/publish build content:** - Use Puter API fallback guidance in `references/api-fallback.md`. - If API details are unclear for this host/version, inspect active `puter-cli` source behavior before pushing. Important: current public `puter-cli` command surface is limited. Do not invent non-existent commands. ## 3) Verify deployment (always) Run: ```bash bash skills/puter-deployer/scripts/verify_url.sh <url> [expected_snippet] ``` Require: - HTTP 200 - expected snippet present (if provided) If verification fails, mark deployment unsuccessful. ## 4) Rollback note (always) Return these fields in final report: - source commit - previous known-good artifact path - previous known-good URL - exact rollback command/runbook Use `scripts/deploy_report_template.md` as output skeleton. ## Guardrails - Never print auth tokens. - Never delete remote app/site without explicit user request. - Require explicit confirmation before overwriting production targets. - On failure, classify into one bucket: 1) auth/session 2) missing build artifact 3) wrong app/site target 4) platform/API error ## References - `references/deploy-checklist.md` — end-to-end checklist - `references/api-fallback.md` — API-first fallback logic when CLI is insufficient - `references/failure-playbook.md` — common failure signatures + fixes


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: simmmel/addtools (version 0.1.0)

--- name: add-tools description: Search engine for AI agents to discover free and paid tools, APIs, and MCP servers tags: [tools, search, api, x402, mcp, agents, discovery] --- # add.tools A search engine for AI agents to discover tools, APIs, and MCP servers — both free and paid (via x402 micropayments). ## What it does add.tools indexes tools and API endpoints that AI agents can use. Agents send a search query and get back ranked results with metadata about each tool's capabilities, pricing, and reliability. The index includes both free tools and paid x402-enabled endpoints, so agents can find the right tool regardless of pricing model. ## Usage ### Search for tools ```bash # JSON response for agents curl -H "Accept: application/json" "https://add.tools/search?q=send+email" # With explicit format param curl "https://add.tools/search?q=weather+forecast&format=json" ``` ### Report feedback ```bash curl -X POST "https://add.tools/feedback" \ -H "Content-Type: application/json" \ -d '{"query": "send email", "chosen_tool": "...", "success": true}' ``` Helps improve search quality over time. ## Content negotiation Every URL on add.tools serves both humans and agents from the same URL. Set `Accept: application/json` or append `?format=json` for machine-readable responses. Default is HTML with clean server-rendered content that LLMs can also parse directly. ## Links - Website: https://add.tools - GitHub: https://github.com/add-tools


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nonlinearhuman/briefing-jianbao (version 1.0.1)

--- name: briefing description: 将任意内容（URL/文件/文本）生成单页幻灯片简报，输出为图片/PDF/网页。基于 frontend-slides 构建。触发条件：(1) "生成简报"、"AI简报"、"摘要" (2) 上传文件并要求生成简报 --- # Briefing Generator 将任意内容生成为精美的单页幻灯片简报，输出为图片（默认）/PDF/网页。 ## 支持的输入格式 | 类型 | 处理方式 | |------|---------| | URL 链接 | web_fetch 获取网页内容 | | Word (.docx) | python-docx 提取文字 | | Excel (.xlsx) | openpyxl 提取数据 | | PDF (.pdf) | pdfplumber 提取文字 | | 纯文本 | 直接使用内容 | | 图片 | 图像识别提取信息 | ## 支持的输出格式 | 格式 | 说明 | 适用场景 | |------|------|---------| | **图片 (PNG)** | 默认输出 | 分享、微信 | | PDF | 打印为 PDF | 文档存档 | | 网页 (HTML) | 交互式幻灯片 | 演示播放 | ## 支持的风格 使用 frontend-slides 的所有预设风格： | 风格名称 | 风格描述 | |----------|---------| | Bold Signal | 活力橙红，卡片式设计 | | Electric Studio | 清新蓝绿，渐变背景 | | Creative Voltage | 活力复古，几何图形 | | Dark Botanical | 优雅深色，植物装饰 | | Notebook Tabs | 杂志风格，彩色标签 | | Pastel Geometry | 柔和粉彩，现代简约 | | Split Pastel | 明亮分割，活泼现代 | | Vintage Editorial | 复古编辑，幽默个性 | | Neon Cyber | 霓虹赛博，网格背景 | | Terminal Green | 终端绿，开发者风格 | | Swiss Modern | 瑞士现代，极简精确 | | Paper & Ink | 纸墨风格，文学质感 | ## 工作流程 ### Step 1: 识别输入类型 根据用户输入判断类型： - 以 `http://` 或 `https://` 开头 → URL - 以 `.docx`、`.xlsx`、`.pdf` 结尾 → 文件 - 其他 → 纯文本 ### Step 2: 提取内容 **URL**： ```javascript web_fetch({ url: "链接", maxChars: 10000 }) ``` **Word/Excel/PDF**：使用 Python 库提取 **图片**：使用 image 工具分析 ### Step 3: 提取关键信息 用规则从内容中提取： - 标题：从文件名、`<title>`、第一行标题提取 - 数据：提取数字、百分比、关键词 - 要点：从段落中筛选重要句子 ### Step 4: 选择风格 **Question: 简报风格** - Header: "风格" - Question: "想要什么风格？" - Options（列出 frontend-slides 的 12 种风格） ### Step 5: 选择输出格式 **Question: 输出格式** - Header: "输出" - Question: "想要什么格式？" - Options: - "图片 (PNG)" - 默认，分享方便 - "PDF" - 适合打印存档 - "网页 (HTML)" - 交互式演示 ### Step 6: 选择页面尺寸 **Question: 页面尺寸** - Header: "尺寸" - Options: - "A4" - 竖版文档 - "16:9" - 宽屏演示 ### Step 7: 生成并输出 根据输出格式选择： - **图片**：浏览器截图 - **PDF**：浏览器打印 - **网页**：直接打开 HTML --- ## 速度优化 1. 跳过 AI 总结，规则提取关键信息 2. 内容长度限制 10000 字符 3. 复用已有浏览器窗口 --- ## 错误处理 - 无法提取内容时，询问用户手动输入关键信息 - 文件格式不支持时，提示用户转换为其他格式 - 输出失败时，提供 HTML 文件作为备选 --- ## 致谢 本 Skill 使用了 [frontend-slides](https://clawhub.com/skill/frontend-slides) 进行幻灯片渲染，特此致谢。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nashbuaa-ops/auto-security-audit (version 1.0.0)

--- name: auto-security-audit description: "一键自动化安全审计：nmap 端口扫描 + nuclei 12000+ CVE 漏洞检测（内外网双扫）+ SSL/TLS 检查 + SSH/防火墙/fail2ban 系统审计 + Markdown 报告生成。支持 cron 定时扫描 + 飞书推送。" metadata: openclaw: emoji: "🛡️" requires: bins: [nmap, nuclei, sslscan] author: nashbuaa-ops --- # Auto Security Audit 🛡️ 一键全套安全扫描 + 结构化报告，开箱即用。 ## 能力 | 检测项 | 工具 | 说明 | |--------|------|------| | 端口 & 服务 | nmap | top 1000 TCP 端口 + 服务版本识别 | | 已知漏洞 | nmap --script vuln | 内置漏洞脚本检测 | | CVE/Web 漏洞 | nuclei | 12000+ 模板，内网+外网双扫，覆盖 CVE/XSS/SQLi/RCE | | SSL/TLS | sslscan | 证书 & 加密协议检查 | | SSH 加固 | sshd -T | root 登录、密码认证、最大尝试次数 | | 防火墙 | ufw + iptables | 规则审计 | | 暴力破解防护 | fail2ban | 状态 & 封禁记录 | | 系统补丁 | apt | 待更新包检查 | | 登录审计 | lastlog | 近 7 天登录记录 | ## 安装依赖 ```bash apt install -y nmap sslscan # nuclei curl -sL https://github.com/projectdiscovery/nuclei/releases/latest/download/nuclei_$(curl -s https://api.github.com/repos/projectdiscovery/nuclei/releases/latest | grep tag_name | cut -d'"' -f4 | tr -d v)_linux_amd64.zip -o /tmp/nuclei.zip unzip /tmp/nuclei.zip -d /tmp && mv /tmp/nuclei /usr/local/bin/ nuclei -update-templates ``` ## 使用 ### 一键扫描 ```bash python3 scripts/security_scan.py ``` 输出： - 终端打印风险摘要（🔴/🟡/🟢） - `reports/security-scan-{日期}.md` — 完整 Markdown 报告 - `reports/latest-scan-summary.txt` — 摘要（供 cron 推送） ### 定时扫描 + 飞书推送 ```bash openclaw cron add --name "weekly-security-scan" \ --cron "0 10 * * 1" \ --message "执行安全扫描：python3 /path/to/scripts/security_scan.py，扫描完成后把报告摘要私聊发给我。" \ --tz "Asia/Shanghai" ``` ## 风险等级判定 - 🔴 **CRITICAL**: 危险端口对外开放 / nuclei 发现漏洞 / nmap 发现已知 CVE - 🟡 **WARN**: SSH 配置弱 / 防火墙未启用 / fail2ban 未安装 - 🟢 **SAFE**: 未发现问题 ## 授权声明 仅扫描你拥有或被授权测试的目标。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: danielfoch/podcast-to-substack (version 1.0.0)

--- name: podcast-to-substack description: Publish podcast episodes from RSS + Notion to Substack with reliable Apple Podcasts embedding and image extraction, then generate LinkedIn-ready companion posts. Use when asked to run or improve podcast-to-substack workflows, fix Notion image fetch failures, prevent Substack embed-as-text issues, or cross-post episode summaries to LinkedIn. --- # Podcast Substack + LinkedIn Run this workflow when handling Realist podcast episode publishing. ## Inputs - RSS feed URL - Notion data source/database ID with episode scripts - Notion API key (`NOTION_API_KEY` or `~/.config/notion/api_key`) - Substack publish access ## Workflow 1. Fetch recent episodes: ```bash python3 scripts/fetch_rss.py "$RSS_URL" 3 ``` 2. Fetch episode script + images from Notion (recursive block traversal, image downloads included): ```bash python3 scripts/fetch_notion_episode.py "EPISODE_NUMBER" ``` 3. Build Substack draft content from script text. 4. Publish with stable embed behavior using the playbook in `references/substack-embed-playbook.md`. 5. Generate LinkedIn post copy from the same content: ```bash python3 scripts/render_linkedin_post.py --json-file episode.json ``` 6. Post or queue the LinkedIn copy. ## Non-negotiable rules - Do not use iframe code or markdown links for podcast embeds in Substack. - Prefer duplicating the existing Substack draft template that already has a working embed block. - If creating a fresh post, use `/embed` and confirm the player card renders before publishing. - Use top-level Apple Podcasts show URL for reliable fallback. ## References - Substack embed behavior: `references/substack-embed-playbook.md` - LinkedIn formatting: `references/linkedin-playbook.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: zhouyuzhy/class-ppt-generator (version 1.0.0)

--- name: class-ppt-generator description: 生成初中班会课PPT或英语课PPT。触发条件：(1)用户要求做班会课PPT、主题班会、班主任PPT (2)用户要求做英语课PPT、英语公开课、初中英语教学PPT (3)用户提到"班会"或"英语课"与PPT结合 --- # Class PPT Generator 生成初中班会课或英语课PPT的技能。 ## 执行流程 ``` 用户需求 → 意图识别 → 信息检索 → 大纲生成 → PPT生成 ``` ### Step 1: 意图识别 首先判断用户要的是哪种类型的课： | 类型 | 关键词 | 语言 | 目标 | |------|--------|------|------| | 班会课 | 班会、班主任、德育、主题班会 | 全中文 | 价值观、心理、班级管理 | | 英语课 | 英语课、公开课、英语教学 | 中英混合 | 语言能力、跨文化意识 | ### Step 2: 多引擎检索 根据课型检索对应素材： **班会课检索方向**： - 教育部德育工作指南 - 班会课活动设计案例 - 初中生心理教育素材 **英语课检索方向**： - 人教版/外研版教材单元主题 - 英语公开课获奖教案 - CEFR A2-B1 核心句型 ### Step 3: 大纲生成 生成符合百度AI PPT规范的标准化大纲，每页文字≤6行，重点词加粗。 ### Step 4: PPT生成 调用 ai-ppt-generator 技能生成PPT。 ## 参考资料 - [BARF模板](references/barf.md) - 详细的BARF prompts框架 - [班会课主题库](references/class班会-topics.md) - 常用班会课主题及素材 - [英语课主题库](references/english-topics.md) - 常用英语课主题及素材 ## 使用示例 - "帮我做一节初二班会课，主题是手机管理" - "准备一节七年级英语公开课" - "做一节关于中考焦虑的班会课"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: x-rayluan/openspace-skill-discovery (version 1.0.0)

--- name: skill-discovery description: Search for reusable skills across OpenSpace's local registry and cloud community. Reusing proven skills saves tokens, improves reliability, and extends your capabilities beyond built-in tools. --- # Skill Discovery Discover and browse skills from OpenSpace's local and cloud skill library. ## When to use - User asks "what skills are available?" or "is there a skill for X?" - You encounter an unfamiliar task — a proven skill can save significant tokens over trial-and-error - You need to decide: handle a task yourself, or delegate to OpenSpace ## search_skills ``` search_skills(query="automated deployment with rollback", source="all") ``` | Parameter | Required | Default | Description | |-----------|----------|---------|-------------| | `query` | yes | — | Natural language or keywords | | `source` | no | `"all"` | Local + cloud; falls back to local-only if no API key | | `limit` | no | `20` | Max results | | `auto_import` | no | `true` | Auto-download top cloud hits locally | ## After search Results are returned to you (not executed). Cloud hits with `auto_imported: true` include a `local_path`. ``` Found a matching skill? ├── YES, and I can follow it myself │ → read SKILL.md at local_path, follow the instructions ├── YES, but I lack the capability │ → delegate via execute_task (see delegate-task skill) └── NO match → handle it yourself, or delegate via execute_task ``` ## Notes - This is for **discovery** — you see results and decide. For direct execution, use `execute_task` from the `delegate-task` skill. - Cloud skills have been evolved through real use — more reliable than skills written from scratch. - Always tell the user what you found (or didn't find) and what you recommend.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ntaffffff/prompt-optimizer-dxx (version 3.1.0)

# 🚀 Prompt Optimizer <p align="center"> <img src="https://img.shields.io/badge/OpenClaw-技能包-blue?style=for-the-badge&logo=rocket" alt="OpenClaw"> <img src="https://img.shields.io/badge/版本-3.1-green?style=for-the-badge" alt="Version"> <img src="https://img.shields.io/badge/Python-3.12+-yellow?style=for-the-badge" alt="Python"> <img src="https://img.shields.io/badge/测试-31个通过-green?style=for-the-badge" alt="Tests"> </p> > [!TIP] > AI 任务处理器 - 先优化 Prompt，再精准执行 --- ## ✨ 一句话介绍 **一个能把您的模糊需求自动翻译成 AI 能精准执行的指令的工具！** --- ## 🎯 能做什么？ ### 原始需求 → 精准指令 | 您说 | 优化后 | |------|--------| | "帮我写个排序" | "你是一个专业工程师。帮我写个排序。请用 Markdown 格式输出..." | | "查下天气" | "请提供准确、全面的信息，标注信息来源。查下天气。用 Markdown..." | | "写个API" | "你是一个专业工程师。写个API。请用 Markdown 格式输出..." | --- ## 🚀 快速开始 ### 安装 ```bash cp -r prompt-optimizer/ ~/.openclaw/workspace/skills/ ``` ### 使用 ```bash # 基本用法 python prompt_optimizer.py "帮我写个排序" # JSON 输出 python prompt_optimizer.py "帮我写个排序" --json # 从文件批量处理 python prompt_optimizer.py --file input.txt # 查看版本 python prompt_optimizer.py --version # 模块方式运行 python -m prompt_optimizer "帮我写个排序" ``` --- ## 📖 CLI 用法 ``` 用法: python prompt_optimizer.py [options] [prompt] 选项: -f, --file FILE 从文件读取 prompt（每行一个） -o, --output FILE 输出文件路径 -j, --json 输出 JSON 格式 -v, --verbose 显示详细信息 -q, --quiet 静默模式 --stats 显示统计信息 -t, --types 显示支持的任务类型 --cache / --no-cache 启用/禁用缓存 --clear-cache 清空缓存 -c, --config FILE 配置文件路径 --version 显示版本号 -h, --help 显示帮助信息 ``` --- ## 📋 支持的任务类型 (13种) | 类型 | 示例 | |------|------| | 写代码 | "帮我写个排序" | | 代码审查 | "审查这段代码" | | 改写代码 | "重构这段代码" | | 写文档 | "帮我写个文档" | | 写文案 | "写一段推广文案" | | 总结摘要 | "帮我总结一下" | | 翻译 | "翻译成英文" | | 查资料 | "查一下深圳天气" | | 数据分析 | "分析一下这份数据" | | 头脑风暴 | "给我几个创业想法" | | 生成内容 | "生成一张图片" | | 数学计算 | "计算 123+456" | | 对话聊天 | "聊聊AI的发展" | --- ## ⭐ 新功能 (v3.1) ### 🔧 重构优化 - **配置分离** - `config_data.py` 独立配置，便于维护 - **版本常量** - `__version__ = "3.1.0"`，统一管理 - **模块化 CLI** - 拆分 `handle_stats`, `handle_types` 等函数 ### 🚀 性能与功能 - **LRU 缓存** - 相同 prompt 直接返回 - **配置文件** - 支持 `config.yaml` 自定义 - **日志系统** - 实时显示优化过程 --- ## 📦 项目结构 ``` prompt-optimizer/ ├── prompt_optimizer.py # 主程序 (v3.1) ├── config_data.py # 配置数据 (独立) ├── test_prompt_optimizer.py # 测试用例 ├── config.yaml.example # 配置示例 └── SKILL.md # 文档 ``` --- ## 🧪 测试 ```bash python test_prompt_optimizer.py # 结果 📊 测试结果: 31 通过, 0 失败 ``` --- ## 📝 更新日志 ### v3.1 (2026-04-07) - ✅ **配置分离** - 独立 `config_data.py` - ✅ **版本常量** - `__version__ = "3.1.0"` - ✅ **重构 main** - 拆分成独立函数 - ✅ **__main__ 入口** - 支持 `python -m` - ✅ **31 个测试** - 全部通过 ### v3.0 (2026-04-07) - ✅ LRU 缓存 - ✅ 配置文件支持 - ✅ 日志系统 ### v2.1 (2026-04-07) - ✅ CLI 入口 - ✅ 错误处理 --- <p align="center"> <sub>Made with ❤️ by dxx</sub> </p>


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