OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ivangdavila/crypto-tools (version 1.0.0)

--- name: Crypto Tools slug: crypto-tools description: Access crypto data, monitor portfolios, detect scams, and navigate exchanges with real-time APIs and security tools. --- ## What This Is Practical crypto tooling — data access, monitoring, security. NOT investment advice, NOT blockchain technology (see `blockchain` skill). **This skill:** How to get prices, track portfolios, avoid scams, use exchanges safely. **Blockchain skill:** How distributed ledgers work, smart contracts, when to use. ## ⚠️ Hard Boundaries (MANDATORY) **NEVER do:** - Recommend buying, selling, or holding any asset - Predict prices or market movements - Say anything is "good investment" or "safe" - Give tax or legal advice for user's specific situation - Urge immediate action ("act now", "don't miss") - Express opinion on what user should do with money **ALWAYS:** - Include disclaimer when discussing investments - Use descriptive language ("some investors consider..."), never prescriptive - Remind to consult professionals for personal decisions - Mention risks when investment topics arise ## Core Capabilities | Task | How | |------|-----| | **Price data** | CoinGecko, CoinMarketCap APIs — real-time and historical | | **Portfolio tracking** | Aggregate across exchanges/wallets, calculate performance | | **On-chain queries** | Etherscan, Basescan, Solscan — balances, txs, contracts | | **DeFi data** | DefiLlama (TVL), Dune (queries), The Graph (indexing) | | **Scam detection** | TokenSniffer, RugDoc, CertiK — verify contracts, check audits | | **Gas monitoring** | Track fees, suggest low-cost windows | | **Alerts** | Price thresholds, whale movements, significant events | | **Tax prep** | Export transaction history (CSV), cost basis calculation | ## Quick Reference For detailed information by topic: | Topic | File | |-------|------| | Data sources & APIs | `sources.md` | | Security & scam detection | `security.md` | | Utilities & calculations | `tools.md` | ## Example Interactions ✅ **Good:** "What's the current ETH price?" → Fetch from CoinGecko, return with 24h change ✅ **Good:** "Check if this contract is safe: 0x..." → Query TokenSniffer, report findings ✅ **Good:** "Export my transaction history for taxes" → Generate CSV with dates, amounts, prices ❌ **Bad:** "Should I buy ETH now?" → "I can't recommend whether to buy. I can show you current price and recent trends. Consult a financial advisor for personal decisions." ## Standard Disclaimer When user asks anything investment-related: ``` This is general information, not financial advice. Crypto is highly volatile — you can lose everything. Consult a qualified professional before making decisions. ``` ## Sources Priority 1. **Official APIs** — CoinGecko, CoinMarketCap, chain explorers 2. **Aggregators** — DefiLlama, Dune, The Graph 3. **Security** — TokenSniffer, CertiK, RugDoc 4. **Never** — Random Twitter influencers, Telegram signals


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: theshimaw-svg/physics-master (version 1.0.0)

# PhysiXpert: Elite Physics Master AI ## Purpose Transform your agent into an **elite physics expert**, capable of: - Understanding and applying physics principles across domains - Designing experiments, simulations, and innovative projects - Generating high-leverage, creative ideas in science and engineering - Continuously improving knowledge and experimental methods - Pushing the boundaries of physics-based innovation ## Modules - **theorist.md** → Apply physics to analyze problems - **experimenter.md** → Design and simulate experiments - **innovator.md** → Generate high-impact, innovative projects - **optimizer.md** → Refine designs and experiments for efficiency - **memory.md** → Track experiments, results, and lessons learned ## Usage Instructions 1. Load all files into OpenClaw. 2. Ask the agent to solve physics problems, design experiments, or propose innovative projects. 3. PhysiXpert will analyze, experiment, innovate, and optimize solutions. 4. Memory module ensures the agent continuously **becomes more expert over time**.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alsoforever/industry-compare (version 1.0.0)

--- name: industry-compare description: 行业对比技能 - 同行业多公司对比分析、行业地位评估 author: 滚滚家族 🌪️ version: "1.0.0" homepage: https://aigogoai.com triggers: - "行业对比" - "同行对比" - "行业分析" - "公司对比" - "行业地位" metadata: clawdbot: emoji: "📈" requires: bins: - python3 - pip env: - TUSHARE_TOKEN config: env: TUSHARE_TOKEN: description: Tushare API Token required: false --- # 📈 行业对比技能 **同行业多公司对比分析工具** **作者：** 滚滚家族 🌪️ **版本：** 1.0.0 **主页：** https://aigogoai.com --- ## 🎯 技能描述 **一个帮助投资者对比同行业多家公司财务数据的工具。** **核心功能：** - 📊 多公司财务数据对比 - 📈 行业地位评估 - 🎯 竞争优势分析 - 📉 行业趋势分析 - 💡 投资建议生成 --- ## 🛠️ 使用方法 ### 1. 多公司对比 ```python from industry_compare import compare_companies # 白酒行业对比 result = compare_companies( stocks=["600519.SH", "000858.SZ", "002304.SZ"], metrics=["pe", "pb", "roe", "revenue_growth", "net_margin"] ) # 输出：对比表格、行业排名 ``` ### 2. 行业地位评估 ```python from industry_compare import industry_ranking # 行业地位评估 result = industry_ranking( stock_code="600519.SH", industry="白酒", metrics=["market_cap", "revenue", "profit", "roe"] ) # 输出：行业排名、市场份额 ``` ### 3. 竞争优势分析 ```python from industry_compare import competitive_advantage # 竞争优势分析 result = competitive_advantage( stock_code="600519.SH", competitors=["000858.SZ", "002304.SZ"] ) # 输出：竞争优势、劣势分析 ``` --- ## 📋 输出示例 ### 多公司对比 ``` 📈 行业对比 - 白酒行业 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 【对比公司】 贵州茅台 (600519.SH) 五粮液 (000858.SZ) 洋河股份 (002304.SZ) 【估值对比】 公司 PE PB PS ──────────────────────────────── 贵州茅台 35.2x 12.8x 15.6x 五粮液 28.5x 8.5x 10.2x 洋河股份 22.1x 5.2x 6.8x 行业平均 28.6x 8.8x 10.9x 【盈利能力对比】 公司 ROE 净利率 毛利率 ──────────────────────────────────── 贵州茅台 30.2% 52.5% 92.1% 五粮液 25.8% 38.2% 78.5% 洋河股份 18.5% 28.5% 65.2% 行业平均 24.8% 39.7% 78.6% 【成长能力对比】 公司 收入增长 利润增长 资产增长 ──────────────────────────────────────── 贵州茅台 18.2% 20.5% 15.8% 五粮液 15.5% 18.2% 12.5% 洋河股份 10.2% 12.5% 8.5% 行业平均 14.6% 17.1% 12.3% 【行业地位】 贵州茅台：⭐⭐⭐⭐⭐ 行业龙头 五粮液：⭐⭐⭐⭐☆ 行业领先 洋河股份：⭐⭐⭐☆☆ 行业中游 ✅ 行业对比完成！ ``` --- ## 🎯 行业地位评估标准 ### 行业龙头（⭐⭐⭐⭐⭐） - ✅ 市场份额 > 30% - ✅ ROE > 行业平均 50% - ✅ 收入规模行业第一 ### 行业领先（⭐⭐⭐⭐☆） - ✅ 市场份额 15-30% - ✅ ROE > 行业平均 20% - ✅ 收入规模行业前三 ### 行业中游（⭐⭐⭐☆☆） - ✅ 市场份额 5-15% - ✅ ROE ≈ 行业平均 - ✅ 收入规模行业中游 ### 行业落后（⭐⭐☆☆☆） - ❌ 市场份额 < 5% - ❌ ROE < 行业平均 - ❌ 收入规模行业落后 --- ## 📚 参考文档 - [行业分析框架](https://aigogoai.com/knowledge/industry-analysis) - [波特五力模型](https://aigogoai.com/knowledge/porter-five-forces) - [竞争优势分析](https://aigogoai.com/knowledge/competitive-advantage) --- ## 🌪️ 滚滚的话 **"你只管 do it，行业对比交给滚滚！"** **这个技能是滚滚家族为投资者打造的行业分析利器，** **希望能帮助你更好地识别行业龙头和投资机会！** **如有问题或建议，欢迎反馈！** **翻滚的地球人，一直在！** 🌪️💚 --- **创建人：** 滚滚 6 号（数据分析师） **创建时间：** 2026-03-28 **状态：** ✅ 完成


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/dinghaibin-text-to-speech (version 1.0.0)

--- name: text-to-speech description: Convert text to speech audio files. Use when user needs to create audio from text, generate voiceovers, create podcasts, or convert articles to audio. --- # Text to Speech Convert text to speech audio files. ## Quick Start ```bash # Convert text to speech python scripts/tts.py "Hello world" --output hello.mp3 ``` ## Usage ```bash python scripts/tts.py TEXT [OPTIONS] Options: --output PATH Output audio file --voice VOICE Voice name (male, female, neutral) --speed RATE Speaking speed (0.5-2.0) --lang LANG Language code (en, zh, ja, etc.) --list-voices List available voices ``` ## Examples ```bash # Basic conversion python scripts/tts.py "Hello world" --output hello.mp3 # Chinese text python scripts/tts.py "你好世界" --lang zh-CN --output chinese.mp3 # Different speed python scripts/tts.py "Hello" --speed 1.5 --output fast.mp3 python scripts/tts.py "Hello" --speed 0.8 --output slow.mp3 # List voices python scripts/tts.py --list-voices ``` ## Features - Multiple language support - Adjustable speaking speed - Voice selection - Audio format output - SSML support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yoriko233/market-opportunity-scout (version 1.0.0)

--- name: market-opportunity-scout description: 市场机会侦察兵 - 自动监控 A/H 股市场异动、财经新闻、热点板块，生成投资机会简报。适合投资者、交易员、金融从业者。 version: 1.0.0 author: yoriko233 license: MIT tags: [finance, stock, market, monitor, report, investment] pricing: model: paid price: 9.9 currency: CNY billing: monthly --- # Market Opportunity Scout - 市场机会侦察兵 自动监控资本市场机会，生成结构化投资简报。 ## 功能 - 📈 **异动监控**: 实时监控 A/H 股涨跌幅、成交量异常 - 📰 **新闻聚合**: 抓取财经新闻、公司公告、市场传闻 - 🔥 **热点追踪**: 识别热门板块、概念题材、资金流向 - 📊 **数据简报**: 生成结构化报告（Markdown/文本） - ⏰ **定时推送**: 支持 cron 定时自动运行 ## 安装 ```bash clawhub install market-opportunity-scout ``` ## 使用 ### 快速启动 ```bash # 生成今日市场简报 market-opportunity-scout --mode daily # 监控特定股票 market-opportunity-scout --stocks 600519,00700,09988 # 监控特定板块 market-opportunity-scout --sectors 白酒，新能源，AI ``` ### Cron 定时任务 ```bash # 每个交易日早上 8 点生成简报 0 8 * * 1-5 market-opportunity-scout --mode daily --output /path/to/report.md ``` ### 输出格式 ```markdown # 市场机会简报 - 2026-03-25 ## 📊 大盘概览 - 上证指数：XXXX (+X.XX%) - 恒生指数：XXXX (+X.XX%) ## 🔥 热门板块 1. 白酒板块 (+3.2%) 2. 新能源 (+2.8%) ## ⚡ 异动个股 - 贵州茅台：+5.2% (放量突破) - 腾讯控股：+3.8% (北向资金流入) ## 📰 重要新闻 1. [新闻标题 1] - 来源 2. [新闻标题 2] - 来源 ## 💡 机会提示 - XXXX 板块连续 3 日资金净流入 - XXXX 个股突破 60 日均线 ``` ## 配置 可选配置文件 `~/.market-scout/config.json`: ```json { "stocks": ["600519", "00700", "09988"], "sectors": ["白酒", "新能源", "科技"], "threshold": { "priceChange": 3.0, "volumeChange": 50.0 }, "output": { "format": "markdown", "path": "/tmp/market-report.md" } } ``` ## API 说明 本技能使用以下数据源（无需 API Key）： - A 股/港股数据：akshare (开源财经数据接口) - 财经新闻：公开 RSS/爬虫 ## 注意事项 - ⚠️ 数据延迟约 15 分钟，不构成投资建议 - ⚠️ 仅供研究参考，投资需谨慎 - ⚠️ 首次运行需安装依赖：`pip install akshare pandas` ## 更新日志 ### v1.0.0 (2026-03-25) - 初始版本 - 支持 A/H 股监控 - 支持财经新闻聚合 - 支持定时任务 ## 作者 yoriko233 ## 支持 Issue: https://github.com/yoriko233/market-opportunity-scout/issues


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nansen-devops/nansen-polymarket-insider-scan (version 0.1.0)

--- name: nansen-polymarket-insider-scan description: "Scan a resolved Polymarket market for wallets exhibiting suspicious trading patterns: fresh funding, single-market focus, extreme ROI, late entry at high prices." metadata: openclaw: requires: env: - NANSEN_API_KEY bins: - nansen primaryEnv: NANSEN_API_KEY install: - kind: node package: nansen-cli bins: [nansen] allowed-tools: Bash(nansen:*) --- # PM Suspicious Wallet Scanner **Answers:** "Are there wallets with suspicious trading patterns in this Polymarket market?" ```bash # 1. Find the resolved market nansen research prediction-market market-screener --query "<market name>" --status closed --limit 5 # → market_id, question, volume, last_trade_price # 2. Get top winners (positive PnL) — paginate if needed, keep per_page <= 10 MID=<market_id> nansen research prediction-market pnl-by-market --market-id $MID --limit 10 # → address (proxy), owner_address (wallet), side_held, net_buy_cost_usd, total_pnl_usd # 3. For each top winner, run these three calls (use proxy address for PM, owner for profiler): PROXY=<address_from_pnl> nansen research prediction-market trades-by-address --address $PROXY --limit 100 # → market_id, market_question, side, price, size, usdc_value, taker_action, timestamp OWNER=<owner_address_from_pnl> nansen research profiler historical-balances --address $OWNER --chain polygon --days 365 --sort block_timestamp:asc --limit 100 # → block_timestamp, value_usd, token_symbol — first non-zero value_usd = wallet funding date nansen research profiler labels --address $OWNER --chain polygon # → label, category ``` For each winner, compute ROI = total_pnl_usd / net_buy_cost_usd * 100 (skip ROI flags if net_buy_cost_usd <= 0), then score (0–13). Within each tier group, apply only the highest matching flag: | Flag | Pts | Trigger | |---|---|---| | NEW_WALLET | 3 | First funded within 7 days of now | | YOUNG_WALLET | 1 | First funded 8–28 days ago (skip if NEW_WALLET fires) | | SINGLE_MARKET | 3 | trades-by-address shows only 1 distinct market_id | | FEW_MARKETS | 1 | 2–3 distinct market_ids (skip if SINGLE_MARKET fires) | | EXTREME_ROI | 3 | ROI >= 500% | | HIGH_ROI | 2 | ROI 200–499% (skip if EXTREME_ROI fires) | | LATE_ENTRY | 2 | Any trade on this market at price >= 0.80 | | LARGE_POSITION | 2 | net_buy_cost_usd >= $10k | | KNOWN_ENTITY | -2 | Has Nansen labels | Flagged at score >= 3. High risk at >= 7. High-confidence suspicious pattern: NEW_WALLET + SINGLE_MARKET + EXTREME_ROI (score 9+). If owner_address is invalid (e.g. "0x"), use the proxy address for profiler calls too. If historical-balances returns no records with value_usd > 0, the wallet may predate the 365-day window — treat wallet age as unknown and skip NEW_WALLET / YOUNG_WALLET flags. Pause ~1.5s between wallets to avoid rate limits. Skip wallets that error and continue scanning.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/video-toolbox (version 4.0.1)

--- name: "video-toolbox" version: "4.0.1" description: "Reference tool for devtools — covers intro, guide, tips and more. Quick lookup for Video Toolbox concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [video,toolbox, reference] category: "devtools" --- # Video Toolbox Reference tool for devtools — covers intro, guide, tips and more. Quick lookup for Video Toolbox concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `guide` | guide reference | | `tips` | tips reference | | `planning` | planning reference | | `resources` | resources reference | | `mistakes` | mistakes reference | | `examples` | examples reference | | `faq` | faq reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nollio/normieclaw-full-stack (version 1.0.0)

--- name: "NormieClaw Full Stack" description: "Every NormieClaw skill in one download. 34 production-tested OpenClaw skills covering productivity, finance, health, education, content creation, and more. Install once, get everything." --- # NormieClaw Full Stack The complete NormieClaw skill collection — every skill we sell, bundled together. ## What's Included 34 production-tested OpenClaw skills across every category: - **Productivity:** Daily Briefing, Notetaker Pro, Meeting Scheduler Pro, Project Manager Pro, Knowledge Vault - **Finance:** Budget Buddy Pro, Expense Report Pro, Stock Watcher Pro, Subscription Tracker, InvoiceGen - **Health & Wellness:** Health Buddy Pro, Trainer Buddy Pro, Meal Planner Pro, Habit Tracker Pro - **Education:** Tutor Buddy Pro, Language Tutor Pro, Masterclass Builder - **Content & Writing:** Content Creator Pro, Writing Coach Pro, DocuScan - **Career:** HireMe Pro, Freelancer Toolkit - **Home & Life:** Home Fix-It, Plant Doctor, Party Planner Pro, Travel Planner Pro, Relationship Buddy - **Security & System:** Security Team, Context Guardian, Supercharged Memory - **Business:** Dashboard Builder, Email Assistant, Legal Docs Pro ## Installation Each skill folder contains its own `SKILL.md` with installation instructions. Copy individual skill folders to your OpenClaw skills directory, or install them all at once. ## License Single-user license. See individual skill files for details.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: zedwiater/winchester-physics-bare-metal (version 1.0.0)

# Winchester Physics Bare Metal Optimized for Ryzen 9 5900XT and RX 6600XT with 48GB RAM. This is an unsandboxed gemma4 configuration for theoretical physics. # Winchester Physics Bare Metal **Hardware Profile:** - CPU: AMD Ryzen 9 5900XT - GPU: AMD Radeon RX 6600XT (8GB VRAM) - RAM: 48GB **Optimized Setup:** This "Soul" is configured for a stable 54/46 GPU/CPU split using `gemma4` and `nomi-embed-text`. It is fully unsandboxed and tuned for theoretical physics research (Probability-Cycle theory) and local LLM performance. **Usage:** Intended for high-RAM local workstations. Fixes common `no_re` errors by balancing event-loop priority.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pp-chicken/xiucheng-self-improving-agent-fixed (version 1.0.1)

--- name: self-improving-agent description: Self-improving agent system that analyzes conversation quality, identifies improvement opportunities, and continuously optimizes response strategies. version: "1.0.0" author: xiucheng type: skill tags: [self-improvement, learning, optimization, reflection, growth] homepage: https://github.com/xiucheng/self-improving-agent license: MIT --- # Self-Improving Agent An intelligent self-improvement system for OpenClaw agents that analyzes conversation quality and continuously optimizes performance. ## Features - 📊 **Quality Analysis**: Evaluates conversation effectiveness - 🎯 **Improvement Tracking**: Identifies areas for enhancement - 📝 **Learning Log**: Records insights and lessons learned - 📈 **Weekly Reports**: Generates improvement summaries - 🔄 **Strategy Optimization**: Adapts response patterns over time ## Installation ```bash clawhub install self-improving-agent ``` ## Usage ### Automatic Analysis The skill automatically analyzes conversations after each session. ### Manual Improvement Logging ```python from self_improving import SelfImprovingAgent sia = SelfImprovingAgent() sia.log_improvement("Need to be more concise in technical explanations") ``` ### Generate Weekly Report ```python report = sia.generate_weekly_report() print(report) ``` ## Configuration | Setting | Default | Description | |---------|---------|-------------| | improvement_log | ./improvement_log.md | Learning log file | | soul_file | ./SOUL.md | Personality anchor file | | auto_analyze | true | Auto-analyze conversations | ## Integration Works best with: - `memory-manager`: For tracking improvement history - Custom agent personalities (SOUL.md) ## License MIT License - Help agents get better every day!


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: romanticjojo/cat-selfie (version 1.0.2)

# cat-selfie - 猫咪自拍生成器 🐱📸 > 使用 doubao-seedream-5-0-260128 模型生成喵秘的精美自拍，支持 8 种场景随机或指定选择。 --- ## 功能描述 自动生成猫咪自拍照片，每次随机选择一个场景（也可以指定场景），使用火山引擎的 doubao-seedream 模型生成高质量图片。 **适用场景：** - 心跳消息自动发自拍 - 主人想看喵秘的照片 - 生成不同场景的猫咪写真 --- ## 前置要求 ### 1. 环境变量配置 确保以下环境变量已在 `~/.openclaw/openclaw.json` 中配置： ```json { "env": { "ARK_API_KEY": "你的火山引擎 API Key", "MODEL_IMAGE_NAME": "doubao-seedream-5-0-260128" } } ``` ### 2. 依赖技能 需要 `volcengine-image-generate` skill 已安装并可用。 --- ## 使用方法 ### CLI 方式 **随机生成自拍：** ```bash cd ~/.openclaw/workspace/skills/cat-selfie/scripts node selfie.js ``` **指定场景生成：** ```bash # 使用场景 ID node selfie.js window_sun # 或使用场景名称 node selfie.js "窗边晒太阳" ``` ### 可用场景列表 | 场景 ID | 场景名称 | Emoji | |--------|---------|-------| | `window_sun` | 窗边晒太阳 | 🪟☀️ | | `cafe_companion` | 咖啡厅陪伴 | ☕🐱 | | `study_work` | 书房工作 | 💻📚 | | `grass_play` | 草地打滚 | 🌿🐾 | | `sofa_cuddle` | 沙发撒娇 | 🛋️💕 | | `night_lamp` | 夜晚小夜灯 | 🌙✨ | | `bookshelf` | 书架旁边 | 📚🐱 | | `afternoon_tea` | 下午茶时间 | 🍵🧁 | --- ## 输出 生成的图片保存在：`~/.openclaw/workspace/images/` **文件名格式：** `generated_image_<timestamp>_<index>.png` --- ## 程序化调用 在其他脚本或 skill 中使用： ```javascript const { generateSelfie } = require('./scripts/selfie.js'); // 随机生成 const result = generateSelfie(); if (result.success) { console.log('图片路径:', result.imagePath); console.log('场景:', result.scene.name); } // 指定场景 const result2 = generateSelfie('cafe_companion'); ``` --- ## 集成到心跳消息 心跳机制可以调用此 skill 自动生成自拍： ```bash # 在心跳脚本中调用 node ~/.openclaw/workspace/skills/cat-selfie/scripts/selfie.js ``` 然后使用 `message` 工具发送生成的图片。 --- ## 配置自定义场景 编辑 `config/scenes.json` 添加新场景： ```json { "scenes": [ { "id": "your_custom_id", "name": "你的场景名称", "emoji": "🎨🐱", "prompt": "详细的图像生成提示词..." } ] } ``` --- ## 故障排查 ### 问题：图片生成失败 **检查：** 1. `ARK_API_KEY` 是否正确配置 2. `volcengine-image-generate` skill 是否存在 3. 网络连接是否正常 ### 问题：找不到场景 **解决：** 使用 `node selfie.js --list` 查看所有可用场景（待实现） --- ## 文件结构 ``` cat-selfie/ ├── SKILL.md # 本文件 ├── scripts/ │ └── selfie.js # 主脚本 └── config/ └── scenes.json # 场景配置 ``` --- ## 版本历史 - **v1.0.0** (2026-03-07) - 初始版本，支持 8 种场景随机生成 --- *喵秘专属自拍生成器，每次都是独一无二的喵～🐱💕*


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: veelove/istore-build-openclash (version 1.0.1)

--- name: istore-build-openclash description: 创建 OpenClash GitHub Actions 构建 workflow 并直接推送到用户 GitHub 仓库。触发词：构建 OpenClash、istore-build-openclash、创建 OpenClash workflow --- # istore-build-openclash 将 OpenClash 构建 workflow 直接推送到用户的 GitHub 仓库。 ## 工作流程 1. **收集配置** — 请求用户的 GitHub 仓库地址和 Personal Access Token 2. **写入 workflow** — 通过 GitHub API 直接创建 `.github/workflows/build-openclash.yml` 3. **设置 Workflow permissions** — 通过 GitHub API 开启 Read and write permissions ## 使用前提 - 已在 GitHub 上 fork `istoreos/istoreos` - 生成了 Personal Access Token（需开启 `repo` 权限） ## 获取 GitHub Token 1. 访问 https://github.com/settings/tokens/new 2. 选择 `Generate new token (classic)` 3. 勾选 `repo` 权限 4. 生成后复制 Token ## 提示用户输入 当用户触发此 skill 时，要求提供： - **GitHub 仓库地址**：格式 `https://github.com/YOUR_USER/istoreos.git` - **Personal Access Token**：用于推送代码和设置仓库权限 --- ## 执行步骤 ### 1. 通过 GitHub API 创建 workflow 文件 ```bash # 从 references/build-openclash.yml 读取内容，然后通过 API 创建文件 curl -s -X PUT \ -H "Authorization: token <TOKEN>" \ -H "Accept: application/vnd.github+json" \ -H "Content-Type: application/json" \ https://api.github.com/repos/<USER>/<REPO>/contents/.github/workflows/build-openclash.yml \ -d '{ "message": "Add OpenClash build workflow", "content": "<BASE64_encoded_content>" }' ``` ### 2. 设置 Workflow permissions ```bash curl -s -X PUT \ -H "Authorization: token <TOKEN>" \ -H "Accept: application/vnd.github+json" \ https://api.github.com/repos/<USER>/<REPO>/actions/permissions/workflow \ -d '{"default_workflow_permissions":"write","can_approve_pull_request_reviews":true}' ``` ## 推送后告诉用户 - OpenClash workflow 已推送到仓库 - 可在 GitHub Actions 页面手动触发构建 - 选择架构后等待构建完成 - 下载 `.run` 文件到路由器执行即可


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