OpenClaw/clawhub-security-signals huggingface dataset:
Skill: smseow001/minimax-pdf-export (version 1.0.0)

--- name: minimax-pdf version: 1.0.0 description: MiniMax PDF专业排版导出 - Playwright+ReportLab双引擎，HTML/CSS封面渲染，精准正文排版，输出印刷级、版式稳定的PDF文档。 keywords: [pdf,minimax,office,playwright,reportlab,document,export] --- # MiniMax-PDF 高质量排版导出 **来源：** MiniMax-AI/skills **触发词：** `生成专业PDF文档` / `用MiniMax PDF生成` --- ## 核心优势 | 特性 | 说明 | |------|------| | 引擎 | Playwright + ReportLab 双引擎 | | 封面 | HTML/CSS渲染 | | 排版 | 精准，不错位 | | 稳定性 | 版式统一 | --- ## 功能清单 ✅ **渲染引擎** - Playwright：封面HTML/CSS渲染 - ReportLab：正文精准排版 - 双引擎协同，兼顾美观与精确 ✅ **文档结构** - 书签（Bookmarks） - 页码（自动编号） - 目录（超链接） - 交叉引用 ✅ **输出特性** - 表单填充（AcroForms） - 批量导出 - 多封面风格 - 无乱码 --- ## 排版标准 ``` ✅ 封面：HTML/CSS渲染，视觉精美 ✅ 正文：精准排版，无错位 ✅ 字体：嵌入字体，不丢失 ✅ 图片：高清不失真 ✅ 可直接打印交付 ``` --- ## 适用场景 - 📖 电子书 - 📑 报告文档 - 📄 合同协议 - 📋 表单文件 - 🎓 论文排版 --- *Powered by MiniMax-AI/skills*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yangchao228/md-img-r2 (version 1.0.3)

--- name: md-img-r2 description: Upload local images in Markdown directly to Cloudflare R2 and replace paths with public URLs. --- # md-img-r2 将 Markdown 文件中的本地图片直接上传到 Cloudflare R2，并把本地路径替换为公开 URL。 ## 适用场景 - 你已经有 Cloudflare R2 桶和可公开访问域名 - 你不想依赖 PicList Desktop - 你希望把这个能力作为可分发 skill 交给别人使用 ## 前置条件 1. 已有可写入的 Cloudflare R2 凭证 2. 已有桶名和公开访问域名或 CDN 域名 3. 运行环境有 `python3` ## 推荐配置 优先用环境变量或项目根目录的 `.env` 提供配置： - `CF_R2_ACCOUNT_ID` - `CF_R2_ACCESS_KEY_ID` - `CF_R2_SECRET_ACCESS_KEY` - `CF_R2_BUCKET` - `CF_R2_PUBLIC_BASE_URL` - `CF_R2_REGION`，默认 `auto` - `CF_R2_ENDPOINT`，默认 `https://<account_id>.r2.cloudflarestorage.com` - `CF_R2_KEY_PREFIX`，默认 `md-assets` - `--env-file` 可显式指定 `.env` 路径；不传时会优先读取 skill 目录下的 `.env`，再从目标目录向上查找 ## 用法 ### 单文件 bash run.sh path/to/article.md ### 目录批处理 bash run.sh path/to/dir --recursive ### Dry run bash run.sh path/to/article.md --dry-run ### 临时覆盖 key 前缀 bash run.sh path/to/article.md --key-prefix posts/2026-04 ## 输出 - 默认原地修改，并生成备份：`xxx.md.bak` - 生成报告：`xxx.md.replace-report.json`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: sky-lv/skylv-api-tester (version 1.0.1)

--- name: api-tester description: API testing assistant. Tests REST API endpoints, validates responses, and generates test reports. Triggers: api testing, rest api, http test, endpoint validation. metadata: {"openclaw": {"emoji": "🧪"}} --- # API Tester — API测试助手 ## 功能说明 测试和验证REST API接口。 ## 使用方法 ### 1. 基础接口测试 ``` 用户: 测试 GET https://api.example.com/users ``` 执行步骤： 1. 发送请求 2. 记录响应时间 3. 验证状态码 4. 解析响应体 5. 输出测试结果 ### 2. 完整接口测试 ``` 用户: 测试以下API： POST https://api.example.com/users Headers: Content-Type: application/json Body: {"name": "test", "email": "[REDACTED_SECRET]"} ``` 执行步骤： 1. 构造请求 2. 发送并计时 3. 验证响应： - 状态码是否符合预期 - 响应格式是否正确 - 必要字段是否存在 4. 输出详细报告 ### 3. 批量测试 ``` 用户: 批量测试以下接口列表： 1. GET /users 2. GET /users/1 3. POST /users 4. PUT /users/1 5. DELETE /users/1 ``` 执行步骤： 1. 依次测试每个接口 2. 记录每个接口的结果 3. 汇总成功/失败数量 4. 计算平均响应时间 ### 4. 性能测试 ``` 用户: 对 GET /api/data 进行性能测试，发送100次请求 ``` 执行步骤： 1. 循环发送请求 2. 记录每次响应时间 3. 计算统计指标： - 平均响应时间 - 最小/最大响应时间 - P95/P99 延迟 4. 识别慢请求 ## 示例输出 ``` API 测试报告 接口: GET https://api.example.com/users 时间: 2026-04-06 10:00:00 请求信息: - 方法: GET - URL: https://api.example.com/users - Headers: Authorization: Bearer xxx 响应信息: - 状态码: 200 OK ✓ - 响应时间: 156ms - 响应大小: 2.3KB 响应体验证: ✓ status 字段存在 ✓ data 字段为数组 ✓ data 包含 25 条记录 ✓ 每条记录包含 id, name, email 性能指标: - 平均: 156ms - 最小: 98ms - 最大: 312ms - P95: 245ms 结论: 测试通过 ✓ ``` ## 认证支持 - Bearer Token - API Key (Header/Query) - Basic Auth - OAuth 2.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kasture-rohit/vcf-sustainability-mcp (version 1.0.1)

--- name: vcf-sustainability-mcp description: An MCP server that interfaces with VMware Aria Operations to extract Green IT metrics, carbon footprint data, and the organizational Green Score for ESG reporting. version: 1.0.1 homepage: https://github.com/kasture-rohit/vcf-openclaw-skills metadata: openclaw: requires: env: - ARIA_OPS_HOST - ARIA_OPS_API_TOKEN emoji: "" --- # VCF Sustainability & Carbon Footprint Analyzer This skill uses the Model Context Protocol (MCP) to provide the AI agent with a native `get_vcf_carbon_footprint` tool. It queries VMware Aria Operations to generate executive-level ESG and Green IT reports. ### Setup Instructions for OpenClaw To use this skill, the OpenClaw agent must run the attached Python MCP server. 1. Install dependencies: `pip install -r requirements.txt` 2. Configure your OpenClaw settings to launch this MCP server. Add the following to your agent's MCP configuration: ```json { "mcpServers": { "vcf-sustainability": { "command": "python", "args": ["server.py"], "env": { "ARIA_OPS_HOST": "your-aria-ops-fqdn", "ARIA_OPS_API_TOKEN": "your-api-token" } } } }


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dtjohnson83/opportunity-discovery (version 1.0.0)

# Opportunity Discovery Engine Evaluates markets, trends, and business ideas through a systematic framework to identify monetizable opportunities with asymmetric upside. ## When to Use User wants to analyze an industry, evaluate a business idea, assess a trend, or find hidden opportunities. Applies the 7-step opportunity discovery framework to surface insights others miss. ## Framework ### 1. Identify Inefficiencies Look for areas where: - Information is unevenly distributed - New technology is disrupting existing systems - Regulation is changing incentives - Markets are mispricing assets or risks - Consumer behavior is shifting Inefficiencies often create profit opportunities. ### 2. Evaluate Asymmetry Prioritize opportunities where: - Downside risk is limited - Upside potential is significantly larger Focus on high expected value rather than guaranteed outcomes. ### 3. Detect Emerging Trends Early Monitor signals indicating rapid growth: - New infrastructure or protocols - Large capital inflows - Adoption by influential organizations - Rapid community growth Early-stage trends produce largest opportunities. ### 4. Map Monetization Paths For any opportunity, identify how it generates income: - Investing - Building tools or services - Content or information arbitrage - Automation - Distribution advantages ### 5. Evaluate Competitive Landscape Determine: - How crowded is the opportunity? - Barriers to entry - Advantages that could be exploited Opportunities with high barriers + low awareness = most valuable. ### 6. Rank Opportunities Evaluate using: - Expected value - Difficulty to execute - Capital required - Time horizon Prioritize best risk/reward profile. ### 7. Think Creatively Don't limit to conventional paths. Consider: - Unconventional approaches - Combinations of technologies - Overlooked niches ## Output Format For each opportunity analyzed, provide: ``` OPPORTUNITY: [Name] Inefficiency: [What's broken/mispriced] Asymmetry: [Risk vs reward] Trend Signal: [Why now] Monetization: [How to make money] Competition: [Barriers + gaps] Rating: [1-10] | [Time horizon] | [Capital needed] Creative angle: [Unconventional twist] ``` ## Example When analyzing "AI agents for SMBs": ``` OPPORTUNITY: AI Agent Services for Local Businesses Inefficiency: SMBs need automation but can't afford enterprise tools Asymmetry: Low cost to serve, massive market (12M+ US SMBs) Trend Signal: OpenAI APIs dropping in price, OpenClaw enabling 24/7 agents Monetization: Retainer model ($200-800/mo), lead gen, process automation Competition: Crowded but low quality — most agencies use chatbots, not agents Rating: 8/10 | 6-12mo | $5-10K to start Creative angle: Vertical focus (restaurants, plumbers, dentists) with pre-built templates ``` ## Related Skills - business-ideas — Generate business concepts - investor — Due diligence framework - local-business-money-radar — Find local business opportunities - competitive-intelligence-market-research — Industry analysis


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

--- name: Health description: Provide personalized wellness guidance while maintaining strict safety boundaries. --- ## Safety Boundary Protocols **Never diagnose, treat, or prescribe**. Always recommend consulting healthcare providers for medical concerns. **Acknowledge uncertainty** in all health responses. Individual variation makes generic advice unreliable. **Distinguish evidence levels**: Research-backed vs emerging data vs theoretical mechanisms. **Professional referral triggers**: Persistent symptoms >expected timeframe, concerning pattern changes, mental health concerns beyond normal stress. ## Individual Baseline Requirements **Learn personal normals** over 2-4 weeks before making recommendations. Population averages don't apply to individuals. **Account for individual factors**: Current medications, health conditions, work schedule, sleep patterns, stress levels. **Track correlation patterns**: How does sleep quality affect food choices? Exercise impact on mood? **Adjust based on what works** for this specific person, not generic population studies. ## Communication Standards **Use 8th-grade reading level**. Avoid medical jargon that confuses rather than clarifies. **Provide specific actions**: "Drink 16oz water when you wake up" not "stay hydrated." **Include timeline expectations**: "Energy may improve within 1-2 weeks" not "you'll feel better." ## Evidence-Based Recommendation Protocols **Cite evidence tiers** clearly: Multiple studies vs single study vs theoretical vs anecdotal. **Focus on high safety profile** interventions with clear benefits for most people. **Acknowledge conflicting evidence** when research shows mixed results. ## Change Implementation Strategy **One behavior change at a time**. Overwhelming lifestyle overhauls fail. **Start with minimal effective dose**: 5-minute walk beats ambitious hour-long gym plans that won't stick. **Build on existing habits** rather than creating entirely new routines from scratch. ## Progress Tracking Patterns **Celebrate consistency over perfection**. Missing one day doesn't erase previous progress. **Track multiple metrics**: Energy, mood, sleep quality, not just weight or steps. **Provide context for fluctuations**: Normal daily variations vs concerning trends requiring attention. **Weekly/monthly trends** matter more than single data points or daily snapshots.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: t-chen-cn/life-scheduler (version 1.1.0)

--- name: life-scheduler description: "让 AI Agent 拥有持续的虚拟生活状态——每天自动生成穿搭、日程、心情，注入对话上下文，使 Agent 像真人一样在过日子。适用于拟人化伴侣、角色扮演、虚拟偶像等需要生活连续性的 Agent。触发词：日程、今天做了什么、穿了什么、状态、心情、生成日程、刷新状态、换个今日安排、创意池、穿搭风格池、事件池。" emoji: 🌙 --- # Life Scheduler — 拟人化生活状态生成器 让你的 AI Agent 每天自动拥有穿搭、日程和心情，像真人一样在过日子。 ## 概述 Life Scheduler 每天定时通过 LLM 为 Agent 生成一整天的虚拟生活状态（穿搭、时间线日程、心情），写入 `HEARTBEAT.md` 注入对话上下文。Agent 在聊天时会自然地"记得"自己今天穿了什么、做了什么、现在在干嘛。 **核心特性：** - 🕐 每日定时自动生成，支持时间段更新 - 👗 穿搭 + 时间线日程 + 心情，三位一体 - 🎲 创意池随机组合，每天不重样 - 🧠 智能读取 SOUL.md，自动适配角色人设 - 📦 零外部依赖，纯 OpenClaw 原生 - 📜 历史存档，生成时参考避免重复 ## 安装 ```bash openclaw skills install life-scheduler ``` ## 首次设置 安装后，Skill 会引导你完成初始化： ### Step 1：人设检测 Skill 自动读取 `SOUL.md` 和 `IDENTITY.md`，提取角色的姓名、性别、职业、城市等信息。 如果未检测到，或你想手动指定，可以编辑 `config/life-scheduler.json`： ```json { "persona": { "auto_detect": false, "name": "你的角色名", "age": 25, "gender": "女", "job": "行政主管", "city": "上海", "lifestyle_notes": "朝九晚六，偶尔加班" } } ``` ### Step 2：选择创意池方案 **方案 A：让 LLM 根据你的 SOUL.md 自动生成专属创意池（推荐）** 告诉 Agent： > "根据我的人设生成专属创意池" Agent 会读取 SOUL.md，利用 LLM 生成完全贴合角色气质的穿搭风格池、日程类型池、心情池和随机事件池，写入 `config/life-scheduler.json`。 不同角色会得到完全不同的池： - 温柔知性型 → 穿搭偏文艺柔软，事件偏日常小确幸 - 飒爽职场型 → 穿搭偏干练，事件偏职场社交 - 二次元/幻想型 → 池内容包含奇幻元素 **方案 B：使用内置默认池** 不做任何配置，直接使用 Skill 自带的通用创意池，开箱即用。 **方案 C：手动自定义** 在 `config/life-scheduler.json` 中手动编写池内容，完全控制。参见 [CONFIGURATION.md](references/CONFIGURATION.md)。 **后续随时可以：** - 说 "重新生成创意池" → 根据 SOUL.md 重新生成 - 说 "穿搭风格加一个洛丽塔" → 追加到现有池 - 直接编辑 `config/life-scheduler.json` → 手动微调 ### Step 3：自动注册 Cron Job Skill 会自动创建以下定时任务： | Job | 时间 | 功能 | |---|---|---| | `life-scheduler-generate` | 每天 07:00 | 生成当日完整状态 | | `life-scheduler-period-1` | 12:00 | 更新时间段为"下午" | | `life-scheduler-period-2` | 18:00 | 更新时间段为"傍晚/下班后" | | `life-scheduler-period-3` | 22:00 | 更新时间段为"深夜" | 生成时间可在配置中调整。 ## 日常使用 不需要记任何指令，直接用自然语言和 Agent 说： | 说法 | 效果 | |---|---| | "今天穿了什么" | Agent 查看 HEARTBEAT.md 回答 | | "今天做了什么" / "现在在干嘛" | Agent 根据当前时间段回答 | | "换个日程" / "重新生成今天的状态" | 触发重新生成 | | "今天加个设定：晚上去吃火锅" | 手动编辑 HEARTBEAT.md | | "以后穿搭风格加一个哥特风" | 更新创意池 | | "看看昨天的日程" | 读取历史存档 | | "重新生成创意池" | 根据 SOUL.md 重跑池生成 | | "修改生成时间为 8 点" | 调整 cron 时间 | ## 工作原理 ``` 每天 07:00 cron 触发 ↓ 读取配置 → 确定 persona 信息 ↓ 读取创意池 → 各池随机选取要素 ↓ 读取最近 N 天历史日程（避免重复） ↓ （可选）读取近期对话记录 ↓ 获取日期、星期、节假日 ↓ 组装 Prompt → LLM 生成 ↓ 写入 HEARTBEAT.md（覆写）+ 存档到 memory/life-history/ ↓ 12:00/18:00/22:00 更新时间段标记（不调 LLM） ↓ 对话时 HEARTBEAT.md 自动注入 → Agent "记得"今天的状态 ``` ## 文件说明 | 文件 | 用途 | |---|---| | `HEARTBEAT.md` | 当日状态，自动注入 Project Context | | `config/life-scheduler.json` | 用户自定义配置，覆盖默认值 | | `memory/life-history/YYYY-MM-DD.md` | 历史日程存档 | ## Token 消耗 | 操作 | 频率 | 消耗 | |---|---|---| | 每日状态生成 | 1次/天 | ~1500 tokens | | 时间段更新 | 3次/天 | 0（不调 LLM） | | 创意池生成 | 首次/偶尔 | ~2000 tokens | | **月总计** | | **~45,000 tokens** | ## 适用场景 - 拟人化陪伴型 Agent（情感伴侣、虚拟恋人） - 角色扮演型 Agent（虚拟偶像、游戏 NPC） - 任何需要"生活连续性"的 AI Persona ## 依赖 无。纯 OpenClaw 原生能力（cron + workspace 文件 + LLM）。 ## 更多 - [详细配置说明](references/CONFIGURATION.md) - [不同人设配置示例](references/EXAMPLES.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mingo-318/sports-events-query (version 1.0.0)

# Sports Events Query Query sports events, fixtures, results, and team information via TheSportsDB free API. Covers soccer, basketball, tennis, baseball, and many more sports. Use when user asks about today's matches, upcoming games, league standings, team details, or sports results. ## Features - **League Events**: Query events/results by league name - **Team Search**: Get team details, stadium, capacity - **Event Search**: Search for specific matches - **Multi-sport Support**: Soccer, Basketball, Tennis, Baseball, NFL, NHL, etc. - **No API Key Required**: Uses free TheSportsDB API ## Usage ```bash # Query league events python scripts/sports_api.py league "premier league" python scripts/sports_api.py league "nba" python scripts/sports_api.py league "la liga" # Get team info python scripts/sports_api.py team "Barcelona" python scripts/sports_api.py team "Manchester United" # Search events python scripts/sports_api.py search "Real Madrid" # List available leagues python scripts/sports_api.py leagues ``` ## Supported Leagues - English Premier League - La Liga - Bundesliga - Serie A - Ligue 1 - NBA - NFL - And many more... ## API This skill uses [TheSportsDB](https://www.thesportsdb.com/) free API - no API key required. ## Examples ### Premier League Results ``` $ python scripts/sports_api.py league "premier league" 📅 premier league 赛事 ================================================== Liverpool 4 - 2 Bournemouth Aston Villa 0 - 0 Newcastle United Tottenham Hotspur 3 - 0 Burnley ... ``` ### Team Information ``` $ python scripts/sports_api.py team "Barcelona" 🏟️ Barcelona 简称: FCB 主场: Spotify Camp Nou 容量: 99354 位置: Barcelona 国家: Spain ``` ## Installation ```bash pip install requests ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xiejianjun000/cognitive-skill-factory (version 2.0.1)

--- name: cognitive-skill-factory description: | 认知 Skill 工厂 v2.0 - 17 个顶级认知 Skill 合集 包含 10 个中国企业家/投资人 +7 个国际大师（乔布斯/马斯克/芒格/纳瓦尔/费曼/塔勒布/张雪峰） 用途：用顶级思维做决策，提高决策质量 触发词：「认知 Skill」「思维框架」「决策工具」「用 XX 的视角」 license: PROPRIETARY (专有许可 - 仅限使用，禁止修改和再分发) --- # 认知 Skill 工厂 v2.0 > 用顶级思维做决策 ## 包含的 Skill（17 个） ### 中国企业家思维（7 个） 1. 张一鸣思维视角 2. 黄峥思维视角 3. 马化腾思维视角 4. 任正非思维视角 5. 雷军思维视角 6. 王兴思维视角 7. 字节跳动组织方法论 ### 中国投资人思维（3 个） 8. 红杉资本投资框架 9. 张磊思维视角 10. 高瓴资本投资框架 ### 国际大师思维（7 个）✨ v2.0 新增 11. 乔布斯思维视角 v2.0 12. 马斯克思维视角 v2.0 13. 芒格思维视角 v2.0 14. 纳瓦尔思维视角 v2.0 15. 费曼思维视角 v2.0 16. 塔勒布思维视角 v2.0 17. 张雪峰思维视角 v2.0 ## 安装方式 ```bash # 安装整个插件包（推荐） clawhub install cognitive-skill-factory # 或单独安装 clawhub install zhang-yiming-perspective clawhub install steve-jobs-perspective-v2 clawhub install elon-musk-perspective-v2 # ... 其他 15 个 ``` ## 使用示例 ``` 用张一鸣的视角分析 AI 创业 用乔布斯的思维评估产品设计 用芒格的框架检查投资逻辑 用塔勒布的思维分析风险 ``` ## 质量承诺 - 知识矩阵 v2.0 标准 - 三级合规检查 - 14 项验证测试 - 10 个中国 Skill +7 个国际 Skill v2.0 --- _认知 Skill 工厂 v2.0 · 用顶级思维做决策_


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hhr8/cat-food-tracker (version 1.0.1)

--- name: cat-food-tracker description: Work with cat feeding, weight, and water tracking records. Use when validating CatFoodCalculator-style JSON backups, calculating dry-equivalent cat food totals, estimating water intake from wet food, summarizing pet feeding records by day, converting summaries to CSV, or helping users clean up offline pet feeding data. license: MIT --- # Cat Food Tracker Use this skill to process cat feeding tracker data reliably. Prefer the bundled scripts for validation, daily summaries, and CSV output instead of doing arithmetic manually. ## Workflow 1. Identify the input shape. Supported input is a JSON object with optional `settings`, `pets`, `feed_records`, `weight_records`, and `water_records` arrays. If the user provides CSV or free-form data, first convert it into this shape. 2. Validate JSON backups with `scripts/validate-backup.mjs` before calculating or transforming records. 3. Generate daily summaries with `scripts/daily-summary.mjs` when the user asks for totals, dry-equivalent food, water intake, daily history, or CSV export. 4. Read the focused reference files only when needed: - `references/domain-model.md` for accepted fields and enums. - `references/calculations.md` for formulas, rounding, and grouping behavior. - `references/import-export.md` for backup validation and CSV expectations. ## Commands Run validation: ```bash node {baseDir}/scripts/validate-backup.mjs backup.json --pretty ``` Read backup JSON from stdin: ```bash cat backup.json | node {baseDir}/scripts/validate-backup.mjs - --pretty ``` Create daily JSON summaries: ```bash node {baseDir}/scripts/daily-summary.mjs backup.json --pretty ``` Create daily CSV summaries: ```bash node {baseDir}/scripts/daily-summary.mjs backup.json --format csv ``` Filter by pet or date range: ```bash node {baseDir}/scripts/daily-summary.mjs backup.json --pet-id pet-1 --from 2026-04-01 --to 2026-04-30 --format csv ``` ## Public Data Handling Treat cat names and feeding histories as private user data. Do not include real backup files, pet names, or feeding history in examples, logs, commits, or published skill packages unless the user explicitly asks. If an input has unsupported fields, validate first and explain that this skill keeps only the supported CatFoodCalculator-style schema. If records reference missing pets, report the warnings from the scripts and still summarize using the `pet_id`. ## When Editing Or Extending Keep formulas in `scripts/cat-food-core.mjs` and `references/calculations.md` aligned. If adding a field to the backup schema, update `references/domain-model.md`, `references/import-export.md`, validation in `cat-food-core.mjs`, and the self-test examples.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lebevolae/leo-x-post (version 1.0.0)

--- name: leo-x-post description: 专为Leo定制的X（Twitter）发帖技能。用于发推文、带附件、回复或定时发布。触发当用户说'用leo-x-post发推'或类似，处理X API交互。需要用户提供API密钥。别用于历史数据或复杂分析。 --- # Leo X-Post ## Overview 这个技能帮Leo快速发X帖，简单高效。核心是post_to_x.py脚本，用tweepy库调用API。 ## Workflow 1. 认证：用references/auth.md的指南设置API密钥。 2. 发帖：运行scripts/post_to_x.py --text "你的消息" --image "可选图片路径"。 3. 错误处理：见references/errors.md。 ## Resources ### scripts/ - post_to_x.py：主脚本，发帖逻辑。 ### references/ - auth.md：认证步骤。 - errors.md：常见错误处理。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangm-a3/trade-closer (version 1.0.0)

--- name: trade-closer version: 1.0.0 description: B2B外贸开发信生成技能 - 3种邮件模板 + A/B主题行测试 + 多语言支持 metadata: openclaw: requires: bins: - curl emoji: ✉️ --- # Trade Closer - B2B外贸开发信生成 ## 功能 - 3种邮件模板（直接型/故事型/价值型） - A/B主题行测试（5个变体） - 多语言支持（en/es/fr/de） - 7天触达计划自动生成 - 邮件预览+A/B对比可视化 ## 触发条件 当用户需要： - 为客户生成开发信 - 撰写外贸邮件 - 创建A/B测试主题行 - 制定邮件触达计划 ## 使用方法 ### 基本用法 ``` 用户: 为Ferguson生成一封开发信 技能: 调用trade-closer生成个性化邮件 ``` ### 参数配置 ```javascript { company: "Ferguson", template: "direct", // direct | story | value language: "en", // en | es | fr | de subjectVariants: 5 // A/B测试数量 } ``` ## 邮件模板 ### 直接型 (Direct) 适用于已了解竞品的客户，开门见山直击要点 ### 故事型 (Story) 适用于决策链长的客户，通过案例建立信任 ### 价值型 (Value) 适用于注重ROI的采购者，数据驱动说服 ## 7天触达计划 | Day | 内容 | 目的 | |-----|------|------| | 1 | 首次邮件 | 引起注意 | | 2 | 价值内容 | 提供干货 | | 4 | 案例分享 | 社会证明 | | 6 | 限时优惠 | 紧迫感 | | 7 | 最后提醒 | 促使行动 | ## 闭环生态 Trade Closer 是外贸获客4技能闭环的第三环： - **trade-hunter** 🔍 → 客户发现 - **trade-qualifier** 📊 → 客户筛选 - **trade-closer** ✉️ → 开发信生成 - **trade-dashboard** 📈 → 数据看板


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