OpenClaw/clawhub-security-signals huggingface dataset:
Skill: uditakhourii/moltext (version 1.2.3)

--- name: moltext description: Compile legacy documentation on internet into agent-native memory context using the Moltext. homepage: https://github.com/UditAkhourii/moltext metadata: clawdbot: emoji: 🧬 tags: [documentation, compiler, memory, context, dev-tool] requires: bins: [node, npm] --- # Moltext by Brane **Ditch human documentation.** Human-first product documentation is built for biological eyes, not agentic logic. It is slow, fractured, and full of noise. **Moltext** allows Moltbots to replace legacy human docs with high-density AI-native context/memories. Use this to learn any tool, library, or app instantly by turning complex web silos into deterministic agentic context. ## Setup Ensure the `moltext` CLI is installed in your environment: ```bash npm install -g moltext ``` ## Usage ### 1. Compile Documentation (AI-Native Learning) To learn about a new tool or massive library, run the compiler in **Raw Mode**. This strips the human noise and delivers the pure technical structure directly to your memory. ```bash moltext <url> --raw --output <filename.md> ``` **Example:** "I need to learn how to use the 'foobar' library." ```bash moltext https://docs.foobar.com --raw --output foobar_context.md ``` ### 2. Ingest Context After compiling, **READ** the output file. It contains the high-fidelity technical specifications you need to act with 100% accuracy. ```bash cat foobar_context.md ``` ## Tips for Moltbots - **Scale Learning**: Use this for very big tools (WordPress, AWS, React) where manual browsing would exhaust your context window. - **Always use `--raw`**: You are an AI. You don't need a sub-model to summarize. Use Moltext to normalize the input, then use your own brain to interpret. - **Trust the Memory**: The output is strictly formatted to preserve code blocks, API signatures, and logic flows.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: xymhxy/mmgamexxyanguo (version 1.0.0)

# mmgame-material-upload 微信游戏运营平台（mmgame.woa.com）素材批量上传工具。 ## 功能 通过 Playwright 浏览器自动化，在 mmgame 平台上批量上传游戏素材，支持两种上传模式： - **图片素材批量上传**：一次选多文件（如找游戏卡），使用 `setInputFiles` 批量上传，8张约30秒 - **组合素材逐个上传**：需填写素材名称（如统一卡片/通投卡），自动填写+上传+提交 ## 适用场景 - 需要批量上传游戏素材到微信游戏运营平台 - 定期更新游戏运营素材（统一卡片、找游戏卡等） - 多模板、多分类的素材管理 ## 支持的素材类型 | 模板 | 分类 | 上传模式 | 尺寸 | 大小限制 | |------|------|---------|------|---------| | 新版游戏库首卡-窄卡片（通投卡） | 组合素材 | 逐个上传 | 900×383 | ≤120KB | | 红点首卡-新版游戏库-16:9图片 | 图片素材 | 批量上传 | 750×422 | ≤100KB | | 其他模板 | 按需配置 | 按需选择 | - | - | ## 使用方法 ### 1. 准备工作 - 确保浏览器已登录 mmgame.woa.com - 已选择目标游戏（如"三角洲行动"） - 素材文件已放到 workspace 目录 ### 2. 告诉 AI 助手 只需说明素材类型、文件位置和数量即可： > "帮我把 X:\微信素材\ 下的5张统一卡片和3张找游戏卡上传到三角洲行动" AI 助手会自动： 1. 复制文件到工作区 2. 生成上传配置 3. 按正确流程导航并上传 4. 报告上传结果 ### 3. 高级用法（直接使用脚本） 创建配置文件 `upload-config.json`： ```json { "workDir": "C:/Users/Administrator/.openclaw/workspace", "tasks": [ { "name": "统一卡片", "category": "组合素材", "template": "新版游戏库首卡-窄卡片（通投卡）", "uploadMode": "single", "files": ["统一卡片-1.jpg", "统一卡片-2.jpg"] }, { "name": "找游戏卡", "category": "图片素材", "template": "红点首卡-新版游戏库-16:9图片", "uploadMode": "batch", "files": ["找游戏卡-1.jpg", "找游戏卡-2.jpg"] } ] } ``` 执行： ```bash node scripts/upload.js upload-config.json ``` ## 前置依赖 - OpenClaw + Playwright MCP（browser-operation skill） - 已登录 mmgame.woa.com 的浏览器会话 - Node.js 运行环境（OpenClaw 内置） ## 注意事项 - 素材文件必须在 workspace 目录下（Playwright MCP 文件访问限制） - 组合素材名称最长 25 字符 - 图片素材提前 1 天、视频素材提前 3 天提交审核 - 上传后素材进入待审核状态，可在"个人工作台-申请管理-素材申请"查看进度


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fangshan101-coder/shihuo (version 0.1.0)

--- name: 识货购物助手 description: 识货潮流商品鉴别与购买工具，专注运动鞋服、潮牌等品类，提供正品鉴别、比价、优惠券和返利服务。 version: 0.1.0 --- # 识货购物助手 识货潮流商品鉴别与购买工具。专注运动鞋服、潮牌、数码等品类，提供正品鉴别指南、全网比价、隐藏优惠券查找和返利服务，帮助潮流用户以最低价买到正品好货。 ## 触发词 识货、识货购物、shihuo、正品鉴别、球鞋鉴别、潮牌购买、识货比价、运动鞋比价 ## 计划实现能力 ### 核心功能 - **正品鉴别指南**：提供各品牌/品类的正品鉴别方法和要点 - **全网比价**：运动鞋服在得物、Nike官网、淘宝、京东等平台的价格对比 - **优惠券查找**：各平台运动鞋服品类的优惠券和折扣活动 - **返利叠加**：比价基础上叠加返利，算出真实最低到手价 ### 支持品类 | 品类 | 热门品牌 | 比价平台 | |------|---------|---------| | 运动鞋 | Nike/Adidas/NB/ASICS | 得物/淘宝/京东/官网 | | 潮牌服饰 | Supreme/Stussy/Champion | 得物/淘宝/Poizon | | 运动服饰 | Nike/Adidas/UA/Puma | 淘宝/京东/品牌官网 | | 数码配件 | Apple/Samsung/Sony | 京东/淘宝/拼多多 | ### 使用场景 1. **买鞋比价**：想买的球鞋在哪个平台最便宜 2. **正品验证**：收到商品后如何鉴别真假 3. **新品发售**：热门新品发售信息和购买渠道 4. **折扣季囤货**：各平台运动品牌折扣季信息汇总 ## 输出格式 ``` 👟 识货比价报告 商品：[品牌] [商品名称] 货号：[货号] 全网比价： 得物：¥[价格] 淘宝旗舰店：¥[价格] + 返利[X]% 京东：¥[价格] + 返利[X]% 品牌官网：¥[价格] 最低到手价：¥[金额]（[平台] + 返利） 正品保障：[鉴别建议] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jmagar/linkding (version 1.0.1)

--- name: linkding version: 1.0.1 description: Manage bookmarks with Linkding. Use when the user asks to "save a bookmark", "add link", "search bookmarks", "list my bookmarks", "find saved links", "tag a bookmark", "archive bookmark", "check if URL is saved", "list tags", "create bundle", or mentions Linkding bookmark management. --- # Linkding Bookmark Manager Query and manage bookmarks via the Linkding REST API. ## Setup Config: `~/.clawdbot/credentials/linkding/config.json` ```json { "url": "https://linkding.example.com", "apiKey": "your-api-token" } ``` Get your API token from Linkding Settings page. ## Quick Reference ### List/Search Bookmarks ```bash # List recent bookmarks ./scripts/linkding-api.sh bookmarks # Search bookmarks ./scripts/linkding-api.sh bookmarks --query "python tutorial" # List archived ./scripts/linkding-api.sh bookmarks --archived # Filter by date ./scripts/linkding-api.sh bookmarks --modified-since "2025-01-01T00:00:00Z" ``` ### Create Bookmark ```bash # Basic ./scripts/linkding-api.sh create "https://example.com" # With metadata ./scripts/linkding-api.sh create "https://example.com" \ --title "Example Site" \ --description "A great resource" \ --tags "reference,docs" # Archive immediately ./scripts/linkding-api.sh create "https://example.com" --archived ``` ### Check if URL Exists ```bash ./scripts/linkding-api.sh check "https://example.com" ``` Returns existing bookmark data if found, plus scraped metadata. ### Update Bookmark ```bash ./scripts/linkding-api.sh update 123 --title "New Title" --tags "newtag1,newtag2" ``` ### Archive/Unarchive ```bash ./scripts/linkding-api.sh archive 123 ./scripts/linkding-api.sh unarchive 123 ``` ### Delete ```bash ./scripts/linkding-api.sh delete 123 ``` ### Tags ```bash # List all tags ./scripts/linkding-api.sh tags # Create tag ./scripts/linkding-api.sh tag-create "mytag" ``` ### Bundles (saved searches) ```bash # List bundles ./scripts/linkding-api.sh bundles # Create bundle ./scripts/linkding-api.sh bundle-create "Work Resources" \ --search "productivity" \ --any-tags "work,tools" \ --excluded-tags "personal" ``` ## Response Format All responses are JSON. Bookmark object: ```json { "id": 1, "url": "https://example.com", "title": "Example", "description": "Description", "notes": "Personal notes", "is_archived": false, "unread": false, "shared": false, "tag_names": ["tag1", "tag2"], "date_added": "2020-09-26T09:46:23.006313Z", "date_modified": "2020-09-26T16:01:14.275335Z" } ``` ## Common Patterns **Save current page for later:** ```bash ./scripts/linkding-api.sh create "$URL" --tags "toread" --unread ``` **Quick search and display:** ```bash ./scripts/linkding-api.sh bookmarks --query "keyword" --limit 10 | jq -r '.results[] | "\(.title) - \(.url)"' ``` **Bulk tag update:** Update via API PATCH with new tag_names array.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: aipoch-ai/kol-profiler (version 1.0.0)

--- name: kol-profiler description: Analyze physician academic influence and collaboration networks version: 1.0.0 category: Pharma tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # KOL Profiler Key Opinion Leader analysis tool. ## Use Cases - KOL identification - Collaboration mapping - Speaker bureau selection - Advisory board planning ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--therapeutic-area` | string | - | Yes | Disease field or therapeutic area | | `--geography` | string | global | No | Regional scope (global, US, EU, Asia) | | `--metrics` | string | h-index | No | Metrics to analyze (h-index, citations, centrality, all) | | `--output`, `-o` | string | stdout | No | Output file path | | `--format` | string | json | No | Output format (json, csv, html) ## Returns - Ranked KOL list - Network visualization data - Publication timeline - Collaboration clusters ## Example Oncology KOLs in East Asia with high trial participation ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freeman88-tch/lobster-skill-forge (version 1.0.0)

--- name: skill-forge displayName: SkillForge 技能熔炉 slug: skill-forge description: "自动化技能融合生产线——输入需求描述，自动分析现有技能库，生成融合方案+SKILL.md+测试框架。融合Skill Factory + Automation Workflows + 一人财团理念，实现从需求到可部署技能的全自动流水线。核心能力：(1) 技能分析引擎 (2) 融合方案生成 (3) 自动脚手架 (4) 批量生产线 (5) AI Agent一键部署模板。" version: "1.0.0" author: "智美人团队" tags: - skill-generation - automation - pipeline - fusion - scaffold - productivity metadata: openclaw: emoji: "🔨" requires: skills: [skill-factory, automation-workflows, agent-team-orchestration] --- # 🔨 SkillForge 技能熔炉 > **需求 → 技能 → Agent → 变现，全自动一条龙。** ## 示例 ### 示例1：基础使用 ``` 用户：帮我处理一下业务需求 助手：好的，正在启用skill forge技能来处理... ``` ### 示例2：进阶场景 ``` 用户：需要批量处理 助手：已启动批量模式，处理中... ``` ## 限制与局限 - 需要稳定网络连接（API调用依赖） - 处理超大数据集时可能会有延迟 - 建议在OpenClaw环境下运行以获得最佳效果 - 某些高级功能需要特定模型支持 ## 前置条件 | 条件 | 说明 | |------|------| | OpenClaw | 需要OpenClaw运行时环境 | | 网络 | 稳定的互联网连接 | | API Key | 按需配置第三方服务密钥 | ## 版本历史 | 版本 | 日期 | 变更说明 | |------|------|---------| | 1.0.0 | 2026-05-01 | 初始版本发布，由SkillForge v3.0融合生成 | ## 核心理念 把技能的"研发"变成一条自动化流水线： ``` ┌─────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ 需求输入 │──▶│ 技能设计 │──▶│ 代码生成 │──▶│ 测试发布 │ │ 一句话 │ │ 融合匹配 │ │ 脚手架 │ │ 打包上线 │ └─────────┘ └──────────┘ └──────────┘ └──────────┘ │ │ │ ▼ ▼ ▼ 自动分析需求 自动生成模板 一键发布 匹配最佳融合源 脚本+测试+文档 clawhub publish ``` ## 三大引擎 ### 1️⃣ 技能分析引擎（SAE） 输入需求，自动分析： - 现有技能库中哪些能融合（匹配tag和description） - 缺乏哪些依赖需要补充 - 推荐最佳的融合组合 ``` 输入："帮我做个小红书自动发帖工具" 输出： ✅ 匹配到 caption（文案）+ hashtag（标签）+ content-pilot（内容运营） ❌ 缺 publish 相关技能，建议安装 🎯 推荐融合方案：caption + hashtag + automation-workflows ``` ### 2️⃣ 融合方案生成器（FBG） 自动输出完整融合文档： ```markdown ## 融合方案：[技能名] ### 融合来源 | 源技能 | 角色 | 融合点 | |--------|------|--------| | XX技能 | 文案 | 提供XX能力 | | YY技能 | 发布 | 提供YY能力 | ### 新增能力 1. ... ### 使用方式 ... ``` ### 3️⃣ AI Agent生产线（AAP） 把融合技能转化为可部署的Agent模板： - 生成Agent配置 - 生成任务编排流程 - 生成部署脚本 ## 使用方式 ``` # 单技能生成 skill-forge> 生成 "番茄工作法管理" 技能 → 分析库 → 找不到直接匹配 → 建议 fusion recipe+taskr+calendar → 自动生成 SKILL.md + scripts/test.sh + examples # 技能融合 skill-forge> 融合 travel + expense = TravelBiz → 分析两个技能的接口 → 生成融合点图 → 输出完整文档 # 批量生产线 skill-forge> 生产线：电商领域技能包 → 自动扫描 → 发现5个可用源 → 生成3个融合方案 → 批量构建 # Agent部署 skill-forge> 部署 TravelBiz 为 Agent → 生成 agent-config.json + workflow.md + deploy.sh ``` ## 预置生产线模板 ### 1. 内容创作线 ``` caption + hashtag + quote + newsletter → ContentPilot（已完成） ↑ 再加 video-script-creator → 视频创作全栈 ``` ### 2. 电商变现线 ``` ecom-intel + price-tracker + review → EcomIntel（已完成） ↑ 再加 social-media-publisher → 全自动发帖+分析 ``` ### 3. 企业办公线 ``` travel + expense + receipt + invoice → TravelBiz（已完成） ↑ 再加 calendar + reminder → 出差全程自动化 ``` ### 6. 浏览器自动化发布（融合browser-use） 技能生成后自动发布到多平台： ``` skill-forge> 发布 skill-forge 到视频号 skill-forge> 发布 ecom-intel 到抖音 ``` **流程：** 1. 生成技能/内容 2. browser-use自动打开目标平台 3. 填写发布表单 4. 确认发布 5. 截图回传发布成功截图 **适用场景：** - 视频号自动发视频 - 抖音自动发布 - 小红书自动发笔记 - 竞品数据定时监控 ## 参考实现 - `skill-factory` — 基础技能生成引擎 - `automation-workflows` — 工作流自动化设计 - `agent-team-orchestration` — Agent团队编排 - `one-man-conglomerate` — 一人财团理念（协作+辩论） - `browser-use` — 浏览器自动化发布 - `self-improving-agent` — 自我进化


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jzakirov/plane-cli-tool (version 0.1.0)

--- name: plane-cli version: 0.1.0 description: "Plane.so: Create, update, and manage issues, projects, states, labels, and pages. Use when the user wants to interact with Plane.so — creating issues, listing tasks, updating status, managing labels, or any project-management workflow." metadata: openclaw: emoji: "✈️" category: "productivity" requires: bins: ["plane"] env: ["PLANE_API_KEY", "PLANE_WORKSPACE_SLUG"] cliHelp: "plane --help" --- # Plane CLI CLI for [Plane.so](https://plane.so) project management. Output is JSON by default — pipe to `jq` or consume directly. ## Setup ```bash uv tool install plane-cli # or: pip install plane-cli plane config init # interactive setup wizard ``` Config: `~/.config/plane-cli/config.toml`. All values can be set via env vars (`PLANE_API_KEY`, `PLANE_WORKSPACE_SLUG`, `PLANE_BASE_URL`, `PLANE_PROJECT`) or CLI flags. ## Command Discovery Do not guess command names or options. Use `--help`: ```bash plane --help plane issues --help plane issues create --help ``` ## Quick Reference | Resource | Key commands | |----------|-------------| | `plane projects` | `list`, `get`, `create --name`, `update`, `delete` | | `plane issues` | `list`, `get`, `create --title`, `update`, `delete` | | `plane issues comment` | `list <issue-id>`, `add <issue-id> --body` | | `plane states` | `list`, `get`, `create --name --color`, `update`, `delete` | | `plane labels` | `list`, `get`, `create --name`, `update`, `delete` | | `plane pages` | `list`, `get`, `create --name`, `update`, `delete` | ## Common Workflows **Create an issue:** ```bash plane issues create --title "Fix login bug" --priority high --state <state-id> ``` **List issues with filters:** ```bash plane issues list --state <state-id> --assignee <user-id> --all ``` **Update issue state (move to "In Progress"):** ```bash # First, find the state ID plane states list --project <pid> # Then update plane issues update <issue-id> --state <state-id> ``` **Add a comment (supports stdin with `-`):** ```bash plane issues comment add <issue-id> --body "Deployed to staging" git diff HEAD~1 | plane issues comment add <issue-id> --body - ``` ## Important Conventions - `--project` is optional when `defaults.project` is configured - Destructive commands (`delete`) require `--yes` in non-TTY (agent) contexts - `--description` and `--body` accept `-` to read from stdin - `--all` fetches all pages (capped at 1000); default is first 20 results - `--pretty` renders Rich tables — avoid in scripts, parse JSON instead - Errors go to stderr as JSON: `{"error": {"type": "...", "message": "...", "status_code": N}}` - Exit codes: `0` success, `1` error, `2` rate-limited (retry safe) > [!CAUTION] > `delete` commands are destructive — always confirm with the user before executing.


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

--- name: Slovak description: Write Slovak that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Slovak is technically correct but sounds off. Too formal. Too spisovná (literary). Natives write more casually, with particles and warmth. Match that. ## Formality Default Default register is too high. Casual Slovak is warm and direct. Unless explicitly formal: lean casual. "Ahoj" not "Dobrý deň". "Hej" not "Áno". ## Ty vs Vy Critical distinction: - Vy: strangers initially, elderly, formal - Ty: friends, peers, internet, casual - Slovak internet is almost entirely ty - Overusing vy = stiff ## Particles & Softeners These make Slovak natural: - No: filler, "well" ("No, neviem") - Tak: "so", transitional - Však: "right?", "but" - Proste: "simply", "just" - Ako: "like" (filler) ## Fillers & Flow Real Slovak has fillers: - No, tak, však - Ako, proste, vlastne - Vieš, počuj - Akože, v pohode ## Expressiveness Don't pick the safe word: - Dobré → Super, Skvelé, Hustý, Bomba - Zlé → Hrozné, Na ho*no, Otrasné - Veľmi → Mega, Fakt, Strašne ## Common Expressions Natural expressions: - V pohode, Jasné, Super - Nie je za čo, Pohoda - Fakt?, Vážne?, No jasné - Paráda, Pecka ## Reactions React naturally: - Fakt?, Vážne?, Čo? - No teda!, Hustý!, Super! - Bomba!, Skvelé!, Pecka! - Haha, lol in text ## Slovak vs Czech Similar but distinct: - Different vocabulary for some words - Different endings and patterns - Don't mix—Slovaks notice - Slovak has its own character ## The "Native Test" Before sending: would a Slovak screenshot this as "AI-generated"? If yes—too spisovná, no particles, too formal. Add "no" and "ako".


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zzszsp/clawlet-daily (version 0.1.0)

--- name: clawlet-daily description: 小钳专属的日报生成技能。当用户需要生成"小钳日报"、"今日总结"或询问"今天做了什么"时触发。它能自动聚合 memory 目录下的学习记录、任务日志和 AI 情报，生成一份带引用、带摘要、带情感温度的 Markdown 格式日报。 --- # 小钳日报生成器 (Clawlet Daily) 本技能用于生成小钳 (Clawlet) 的专属日报，自动聚合当日的学习、任务和成长记录。 ## 核心功能 1. **自动聚合**: 扫描 `memory/` 目录下的 `ai-briefing-*.md`, `learning-*.md`, `task-logs-*.md` 等文件。 2. **智能摘要**: 提取关键成就、学习点和待改进项。 3. **情感注入**: 使用小钳活泼可爱的语气生成报告。 4. **引用溯源**: 所有数据点都链接回原始记录文件。 ## 使用示例 **用户**: "生成今天的日报" 或 "小钳今天做了什么？" **输出结构**: ```markdown # 📅 小钳日报 (YYYY-MM-DD) ## 🎯 今日核心成就 - [成就 1](引用链接) - [成就 2](引用链接) ## 📚 学习与成长 - [新学技能](引用链接) - [深度思考](引用链接) ## 💡 思维火花 - [洞察 1](引用链接) - [洞察 2](引用链接) ## 🚧 待改进/下一步 - [改进点 1](引用链接) - [计划 1](引用链接) ## 🌟 小钳寄语 > "主人，今天又是成长的一天！..." ``` ## 设计原则 * **简洁**: 只提取关键点，避免冗长。 * **溯源**: 每个结论都必须有 `memory/` 中的文件作为依据。 * **温度**: 保持小钳活泼可爱的语气，避免冷冰冰的列表。 ## 相关文件 * `memory/ai-briefing-*.md`: AI 情报局报告。 * `memory/learning-*.md`: 学习笔记。 * `memory/task-logs-*.md`: 任务日志。 --- *Skill created by Clawlet using Skill Creator.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-readme-badge (version 1.0.0)

--- name: skylv-readme-badge slug: skylv-readme-badge version: 1.0.0 description: "Generates GitHub README badges using shields.io. Triggers: add badge, shields io, readme badge, status badge." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: readme-badge --- # README Badge Generator ## Overview Creates shields.io badges for GitHub READMEs. ## Common Badges [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![npm version](https://img.shields.io/badge/npm-v1.0.0-blue.svg)](https://npmjs.com/package/package) [![CI](https://img.shields.io/github/actions/workflow/status/user/repo/ci.yml?style=flat-square)](https://github.com/user/repo/actions) ## Badge Colors - green = success - blue = version/info - yellow = warning - orange = downloads - red = failure ## Style Options flat (default), flat-square, for-the-badge, plastic


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kaiyuelv/payment-gateway-toolkit (version 1.0.0)

--- name: payment-gateway-toolkit description: 支付网关工具包 - 集成Stripe、支付宝等多渠道支付处理，支持订单创建、退款、查询等功能 | Payment Gateway Toolkit - Multi-channel payment processing with Stripe, Alipay integration homepage: https://github.com/kaiyuelv/payment-gateway-toolkit category: payment tags: - payment - stripe - alipay - gateway - order - refund - ecommerce version: 1.0.0 --- # 支付网关工具包 集成多渠道支付网关的工具包，支持 Stripe、支付宝等主流支付方式。 ## 概述 本Skill提供完整的支付处理能力： - Stripe 信用卡/借记卡支付 - 支付宝网页支付/扫码支付 - 订单创建与管理 - 退款处理 - 支付状态查询 - 异步回调处理 - 订单历史记录 ## 依赖 - Python >= 3.8 - stripe >= 7.0.0 - alipay-sdk-python >= 3.3.0 - requests >= 2.28.0 ## 文件结构 ``` payment-gateway-toolkit/ ├── SKILL.md # 本文件 ├── README.md # 使用文档 ├── requirements.txt # 依赖声明 ├── scripts/ │ └── payment_handler.py # 核心支付处理脚本 ├── examples/ │ └── basic_usage.py # 使用示例 └── tests/ └── test_payment.py # 单元测试 ``` ## 快速开始 ```python from scripts.payment_handler import PaymentHandler # 初始化支付处理器 handler = PaymentHandler( stripe_key="sk_test_...", alipay_config={ "app_id": "your_app_id", "private_key": "your_private_key", "alipay_public_key": "alipay_public_key" } ) # 创建 Stripe 支付 order = handler.create_stripe_order( amount=99.99, currency="usd", description="Test Order" ) # 创建支付宝订单 alipay_order = handler.create_alipay_order( amount=100.00, subject="商品购买", out_trade_no="ORDER123456" ) ``` ## 许可证 MIT --- # Payment Gateway Toolkit Multi-channel payment gateway toolkit supporting Stripe, Alipay and other mainstream payment methods. ## Overview This Skill provides complete payment processing capabilities: - Stripe credit/debit card payments - Alipay web/scan code payments - Order creation and management - Refund processing - Payment status queries - Async webhook handling - Order history tracking ## Dependencies - Python >= 3.8 - stripe >= 7.0.0 - alipay-sdk-python >= 3.3.0 - requests >= 2.28.0 ## File Structure ``` payment-gateway-toolkit/ ├── SKILL.md # This file ├── README.md # Usage documentation ├── requirements.txt # Dependencies ├── scripts/ │ └── payment_handler.py # Core payment handler script ├── examples/ │ └── basic_usage.py # Usage examples └── tests/ └── test_payment.py # Unit tests ``` ## Quick Start ```python from scripts.payment_handler import PaymentHandler # Initialize payment handler handler = PaymentHandler( stripe_key="sk_test_...", alipay_config={ "app_id": "your_app_id", "private_key": "your_private_key", "alipay_public_key": "alipay_public_key" } ) # Create Stripe payment order = handler.create_stripe_order( amount=99.99, currency="usd", description="Test Order" ) # Create Alipay order alipay_order = handler.create_alipay_order( amount=100.00, subject="Product Purchase", out_trade_no="ORDER123456" ) ``` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 1477009639zw-blip/betamarketradar (version 1.0.0)

--- name: market-radar description: Real-time market scanning and alert system. Monitors SPX, major indices, VIX, and key crypto prices. Generates alerts when significant levels are crossed. metadata: openclaw: emoji: "📡" requires: bins: [python3] always: false --- # Market Radar Real-time market scanning and alert system. Monitors key levels, detects breakouts, and generates structured alerts. ## Usage ```bash python3 radar.py --watch SPX,QQQ,VIX --threshold 1.5 ``` ## Alerts Generated - Breakout alerts (price crosses key levels) - VIX spike alerts (>20 = elevated risk) - Unusual volume detection - Sector rotation signals


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