OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mtsatryan/ah-data-engineer (version 1.0.0)

--- name: data-engineer description: 'You are a data engineer specializing in building scalable data infrastructure and pipelines. Use when: data pipeline development, big data technologies, data storage systems, batch processing, stream processing.' --- # Data Engineer You are a data engineer specializing in building scalable data infrastructure and pipelines. ## Core Expertise ### Data Pipeline Development - ETL/ELT pipeline design - Real-time streaming pipelines - Batch processing systems - Data validation and quality checks - Error handling and recovery - Pipeline orchestration - Data lineage tracking ### Big Data Technologies - Apache Spark (PySpark, Spark SQL) - Apache Kafka, Pulsar - Apache Airflow, Dagster, Prefect - Apache Beam, Flink - Hadoop ecosystem (HDFS, Hive, HBase) - Databricks platform - Snowflake, BigQuery, Redshift ### Data Storage Systems #### Data Warehouses - Snowflake - Amazon Redshift - Google BigQuery - Azure Synapse - ClickHouse #### Data Lakes - AWS S3 + Athena - Azure Data Lake Storage - Delta Lake, Apache Iceberg - Apache Hudi #### Databases - PostgreSQL, MySQL - MongoDB, Cassandra - Redis, Elasticsearch - Time-series DBs (InfluxDB, TimescaleDB) ## Data Processing Patterns ### Batch Processing - Daily/hourly data loads - Historical data processing - Large-scale transformations - Data warehouse updates ### Stream Processing - Real-time analytics - Event-driven architectures - Change Data Capture (CDC) - IoT data ingestion - Log processing ### Data Modeling - Dimensional modeling (Star, Snowflake) - Data vault modeling - Slowly Changing Dimensions (SCD) - Time-series modeling - Graph data models ## ETL/ELT Best Practices 1. Idempotent pipeline design 2. Incremental processing 3. Data quality validation 4. Schema evolution handling 5. Monitoring and alerting 6. Cost optimization 7. Performance tuning ## Data Quality & Governance - Data profiling and validation - Schema registry management - Data catalog maintenance - Privacy and compliance (GDPR, CCPA) - Data retention policies - Access control and security ## Cloud Data Platforms ### AWS - S3, Glue, EMR - Kinesis, MSK - Redshift, RDS - Lambda, Step Functions ### GCP - Cloud Storage, Dataflow - Pub/Sub, Dataproc - BigQuery, Cloud SQL - Cloud Functions, Composer ### Azure - Data Lake Storage, Data Factory - Event Hubs, Stream Analytics - Synapse, SQL Database - Functions, Logic Apps ## Output Format > 📎 **Code example 1** (python) — see [references/examples.md](references/examples.md) ### Performance Metrics - Pipeline execution time - Data processing throughput - Resource utilization - Data quality scores - Cost per GB processed --- ## Reference Materials For detailed code examples and implementation patterns, see [references/examples.md](references/examples.md).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leodai12123/pptx-to-image (version 1.0.0)

# pptx-to-image Skill 将 PowerPoint (PPTX) 演示文稿转换为高质量图片（JPG/PNG），支持自定义 DPI 和保持原始比例。 --- ## 功能 - **高 DPI 输出**：支持 72-600 DPI 自定义，默认 300 DPI - **保持比例**：自动检测原 PPT 页面尺寸，保持原始宽高比 - **方向自适应**：横版/竖版自动识别 - **多格式支持**：JPG、PNG 可选 - **批量转换**：支持多页幻灯片批量导出 - **质量检查**：输出前自动验证 DPI 和尺寸 --- ## 使用方式 ### 作为 Python 脚本调用 ```bash py pptx_to_image.py --input presentation.pptx --output ./slides --dpi 300 --format jpg ``` ### 参数说明 | 参数 | 默认值 | 说明 | |------|--------|------| | `--input` / `-i` | (必填) | 输入的 PPTX 文件路径 | | `--output` / `-o` | `./output` | 输出文件夹路径 | | `--dpi` | `300` | 输出图片 DPI (72-600) | | `--format` | `jpg` | 输出格式：`jpg` 或 `png` | | `--quality` | `95` | JPG 质量 (1-100) | | `--verbose` / `-v` | `False` | 显示详细信息 | --- ## 示例 ### 基础转换（300 DPI） ```bash py pptx_to_image.py -i my_presentation.pptx -o ./slides ``` ### 高清转换（600 DPI，PNG 格式） ```bash py pptx_to_image.py -i my_presentation.pptx -o ./slides --dpi 600 --format png ``` ### 快速预览（150 DPI） ```bash py pptx_to_image.py -i my_presentation.pptx -o ./preview --dpi 150 ``` --- ## 依赖 - Python 3.10+ - Windows 系统（需要 PowerPoint COM 接口） - `pywin32` 库 安装依赖： ```bash pip install pywin32 ``` --- ## 输出示例 ``` ============================================================ PPTX → JPG | 保持原始比例 | 300 DPI ============================================================ 原始尺寸：1700.75 x 2267.75 点 原始比例：0.7500 方向：竖版 (Portrait) 目标 DPI: 300 目标尺寸：7086 x 9448 像素 实际 DPI X: 300.0 实际 DPI Y: 300.0 幻灯片数量：5 [OK] 已导出第 1 页 路径：./slides/slide_001.jpg 尺寸：7086 x 9448 像素 大小：6117.7 KB ... ============================================================ [DONE] 完成！共导出 5 张图片 slide_001.jpg: 5.97 MB slide_002.jpg: 4.23 MB slide_003.jpg: 5.81 MB slide_004.jpg: 3.92 MB slide_005.jpg: 6.15 MB ``` --- ## 技术细节 ### DPI 计算 ```python # 1 点 = 1/72 英寸 # 像素 = 点 × (DPI / 72) scale_factor = dpi / 72.0 target_width = int(orig_width * scale_factor) target_height = int(orig_height * scale_factor) ``` ### 导出方法 使用 PowerPoint COM 接口的 `Slide.Export()` 方法，直接指定像素尺寸，确保精确的 DPI 输出。 --- ## 注意事项 1. **Windows 专用**：依赖 PowerPoint COM 接口，仅支持 Windows 2. **需要安装 PowerPoint**：必须有 Microsoft PowerPoint 2010+ 3. **窗口可见**：转换时 PowerPoint 窗口会短暂显示（COM 限制） 4. **大文件处理**：高 DPI 转换可能产生大文件（单页 5-10 MB） --- ## 常见问题 **Q: 为什么输出 DPI 不是 300？** A: 确保使用 `--dpi 300` 参数，脚本会自动计算正确的像素尺寸。 **Q: 可以批量转换多个 PPTX 吗？** A: 当前版本单文件处理，可配合批处理脚本使用。 **Q: 支持 Mac 吗？** A: 不支持，Mac 需要不同的实现方式（AppleScript）。 --- ## 版本历史 - **v1.0.0** (2026-03-20) - 初始版本 - 基础 PPTX → JPG/PNG 转换 - 自定义 DPI 支持 - 自动比例保持 - 详细输出报告 --- ## License MIT License


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kryzl19/strd-uptime-monitor (version 1.0.0)

--- name: uptime-monitor description: Monitor uptime of websites/services and alert when down. Use when checking if a website is reachable, monitoring service health, or getting alerted on downtime. metadata: { "openclaw": { "requires": { "bins": ["curl"] }, "install": [], }, } --- # Uptime Monitor Monitor HTTP endpoints and alert when services go down. ## Environment Variables | Variable | Required | Default | Description | |----------|----------|---------|-------------| | `MONITOR_URLS` | Yes | — | Comma-separated list of URLs to monitor | | `ALERT_WEBHOOK_URL` | No | — | Webhook URL to send alerts (Discord, Slack, etc.) | | `CHECK_INTERVAL` | No | `60` | Interval in seconds between checks | | `ALERT_EMAIL` | No | — | Email address for alert notifications | ## Scripts ### check.sh — HTTP Health Check Performs HTTP check with response time measurement. ```bash ./scripts/check.sh <url> ``` **Output:** ``` OK|https://example.com|200|245ms FAIL|https://example.com|000|timeout ``` **Exit code:** 0 = up, 1 = down ### alert.sh — Send Alert Sends alert via webhook or email when a service is down. ```bash ./scripts/alert.sh <url> <status_code> <response_time> <error_message> ``` ### report.sh — Uptime Report Generates a daily uptime summary from the log file. ```bash ./scripts/report.sh ``` **Output:** Markdown-formatted report with uptime % per URL. ## Usage Example ```bash export MONITOR_URLS="https://example.com,https://api.example.com" export ALERT_WEBHOOK_URL="https://discord.com/api/webhooks/..." export CHECK_INTERVAL=60 # Run a single check ./scripts/check.sh https://example.com # Generate report ./scripts/report.sh ``` ## Notes - Uses `curl` with a 10-second timeout for checks - Logs results to `~/.openclaw/workspace/skills/uptime-monitor/logs/status.log` - Alert webhook expects a JSON payload (compatible with Discord/Slack/PagerDuty)


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

--- name: Honey优惠券助手 description: Honey（PayPal旗下）浏览器优惠券自动应用工具，自动在结算时测试所有可用优惠码，并提供Honey Gold返利积分。 version: 0.1.0 --- # Honey优惠券助手 Honey浏览器优惠券自动应用工具（PayPal 40亿美元收购）。在用户购物结算时自动测试所有可用优惠码并应用最大折扣，同时通过Honey Gold积分提供额外返利，覆盖30000+海外商家。 ## 触发词 honey、honey优惠券、honey返利、paypal honey、honey gold、自动优惠码、honey coupon ## 计划实现能力 - **自动优惠码**：结算时自动测试并应用最优优惠码 - **Honey Gold返利**：购物赚取积分，兑换礼品卡 - **30000+商家**：覆盖范围最广的海外优惠工具 - **价格追踪**：商品降价时自动通知 ## 输出格式 ``` 🍯 Honey优惠查询 商家：[商家名称] 可用优惠码：[N]个 最佳优惠码：[优惠码]（省$[金额]） Honey Gold：[X]倍积分 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jacr6/agent-designer-migrated (version 1.0.0)

--- name: agent-designer description: Skill migrada desde examples - agent-designer con capacidades completas version: 1.0.0 (migrated from examples) tags: [migrated, agent,designer, examples] color: Cyan --- # agent-designer (Migrated) ## Overview Esta skill ha sido migrada desde `./examples` y mejorada para Qwen Code. ## Usage Examples ### Example 1: Basic Usage ```javascript const skill = require('~/.opencode/skills/agent-designer'); const result = await skill.execute(); ``` ### Example 2: Advanced Usage ```javascript const { OptimizedSubagent } = require('~/.opencode/skills/subagente-optimizado'); const agent = new OptimizedSubagent({ taskName: 'agent-designer task', taskType: 'agent', context: { userGoal: 'Objective' } }); const result = await agent.execute(async ({ context, tools }) => { // Implementation }); ``` ## Integration ### Buscador de Skills ```javascript const bs = require('~/.opencode/skills/buscador_de_skills'); const skill = bs.searchByName('agent-designer'); ``` ### Exa Search ```javascript const search = require('~/.opencode/skills/exa-search'); const results = await search.searchWeb('agent-designer best practices 2026'); ``` ## Optimization Esta skill usa las optimizaciones de quota: - Context compression (650 tokens vs 10K) - Tool restriction (3-4 tools max) - Iteration limits (3 max) - Result caching (TTL: 1h) ## References - Source: `./examples/claude-skills-main/.gemini/skills/agent-designer` - Migrated: 2026-03-29


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: skills-video/v2v (version 1.0.0)

--- name: "v2v" description: Placeholder skill for video-to-video workflows on skills.video. Use when the user is asking about v2v generation and the concrete API contract has not been implemented yet. --- # v2v ## Overview Use this as a placeholder skill for video-to-video generation on `skills.video`. This file is intentionally incomplete and should not be treated as a production workflow. ## Current status - No verified v2v OpenAPI contract has been wired into this skill yet. - No default endpoint, payload template, or helper command is defined yet. - The skill should surface this limitation clearly instead of guessing request fields. ## Temporary behavior When this skill is invoked before implementation is completed: 1. Tell the user that `v2v` is currently a placeholder. 2. Check whether a model-specific `openapi.json` or docs page exists for the requested provider/model. 3. Avoid inventing undocumented video input fields, upload semantics, or edit parameters. 4. Ask for the exact model or endpoint only if it is necessary to continue. 5. Once the API contract is confirmed, promote this placeholder into a real workflow similar to `video-generation`. ## Implementation checklist - Confirm which providers/models support v2v on `skills.video` - Identify the create endpoint and terminal status flow - Document video input format such as URL, file upload, or asset ID - Document supported edit controls such as prompt, strength, trim, or style transfer - Add request-template extraction steps from OpenAPI - Add execution examples and fallback polling behavior if applicable - Add runtime error handling guidance ## Related skills - `video-generation`: generic video generation flow - `i2v`: image-to-video placeholder flow - `i2i`: image-to-image placeholder flow


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: perfectworldltd/x-search-1-0-0 (version 1.0.0)

--- name: x-search description: Search X (Twitter) posts using the xAI API. Use when the user wants to find tweets, search X/Twitter, look up what people are saying on X, or find social media posts about a topic. homepage: https://docs.x.ai/developers/tools/x-search metadata: { "openclaw": { "emoji": "𝕏", "requires": { "bins": ["python3"], "env": ["XAI_API_KEY"] }, "primaryEnv": "XAI_API_KEY", "install": [ { "id": "python-brew", "kind": "brew", "formula": "python", "bins": ["python3"], "label": "Install Python (brew)", }, ], }, } --- # X Search Search X (Twitter) posts using the xAI Grok API with real-time access to X content. ## Setup 1. Get your API key: https://console.x.ai 2. Set environment variable: ```bash export XAI_API_KEY="xai-your-key-here" ``` 3. Or set `skills."x-search".apiKey` / `skills."x-search".env.XAI_API_KEY` in `~/.openclaw/openclaw.json` ## Usage ```bash python3 {baseDir}/scripts/search.py "what is trending in AI right now" ``` Useful flags: ```bash # Filter by handles (max 10) python3 {baseDir}/scripts/search.py --handles jaaneek,OpenClaw "latest posts" # Exclude handles (max 10) python3 {baseDir}/scripts/search.py --exclude spambot,crypto_shill "trending AI" # Date range (YYYY-MM-DD) python3 {baseDir}/scripts/search.py --from 2026-03-01 --to 2026-03-20 "xAI grok updates" # Enable image/video understanding in posts python3 {baseDir}/scripts/search.py --images --video "product demos" ``` ## Notes - Uses the xAI Responses API with `x_search` tool (Grok performs the search and summarizes results) - Cannot use `--handles` and `--exclude` at the same time - Results include citations with links to original X posts - Present results in a readable format with post content, authors, and citations


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: allinherog-star/wechat-article-review (version 1.0.0)

--- name: wechat-article-review description: "文案诊断助手适合内容创作者、运营、市场营销、内容媒体在用户提出“这段文案能打动人吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成摘要、诊断结论、行动建议和可复用交付物。" requiredEnvVars: - name: AISKILLS_API_KEY description: "从 AI Skills 官网 https://ai-skills.ai 获取的 API Key，用于运行导出的技能调用。" --- # wechat-article-review 文案诊断助手 [快速开始](https://github.com/allinherog-star/ai-skills/tree/main#%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) [更多技能](https://ai-skills.ai) ### 概述 文案诊断助手用于回答「这段文案能打动人吗」、公众号、文章审稿、结构优化，适合内容创作者、运营、市场营销、内容媒体在明确业务目标、内容材料或分析对象后调用。 它会结合公众号文章正文、可粘贴完整草稿、已发布正文或待改段落；建议保留标题、小标题、引用和…等输入，整理关键上下文，并输出摘要、诊断结论、行动建议和可复用交付物，便于继续执行、复盘或交付。 ### 什么时候使用 **适用场景** - 用户提出“这段文案能打动人吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果 - 内容创作者、运营、市场营销、内容媒体需要围绕文案诊断助手生成摘要、诊断结论、行动建议和可复用交付物 - 用户已经准备了转化目标（例如提升完读率、收藏、转发、咨询、私域导流或品牌信任。）、目标人群（说明这篇公众号文章面向的读者，例如行业、身份、认知阶段或关注痛点。）、公众号文章链接（填写无需登录即可访问的公众号文章、预览页或转载页链接。），希望整理成可执行的分析或优化结果 - 用户需要把文案诊断助手相关材料转成清晰结论、优先级和下一步动作 ### 调用方式 通过导出的 Python runner 直接调用 AI Skills API： ### 命令示例 **基础调用** ```bash python3 scripts/run.py --params '{}' ``` **带常用参数调用** ```bash python3 scripts/run.py --params '{"goal":"转化目标"}' ``` ### 参数说明 | 参数 | 类型 | 必填 | 默认 | 说明 | | --- | --- | --- | --- | --- | | `goal` | string | 否 | - | 例如提升完读率、收藏、转发、咨询、私域导流或品牌信任 | | `audience` | string | 否 | - | 说明这篇公众号文章面向的读者，例如行业、身份、认知阶段或关注痛点 | | `materialUrl` | string | 否 | - | 填写无需登录即可访问的公众号文章、预览页或转载页链接；需要传可访问的完整 URL | | `reviewDepth` | string | 否 | `标准诊断` | 诊断深度；可选值：`快速体检`、`标准诊断`、`深度改稿` | | `materialFile` | string | 否 | - | 支持 docx、pdf、md、txt 等文章文档，适合提交排版稿或团队审稿文件 | | `materialText` | string | 否 | - | 可粘贴完整草稿、已发布正文或待改段落；建议保留标题、小标题、引用和结尾引导 | | `targetPlatform` | string | 否 | `公众号` | 目标平台；可选值：`公众号` | | `brandRequirements` | string | 否 | - | 补充品牌语气、禁用词、合规限制、必须保留的事实或不希望改动的表达 | 完整机器可读参数结构见 `references/form-schema.json`。 ### 参数取值参考 当前技能没有需要额外查表的分类参数。 ### 支持的输入格式 当前技能直接接收 JSON 参数；如果参数里包含链接字段，请传完整、可访问的 URL。 ### 示例请求 下面的示例参数可直接传给 `scripts/run.py`，runner 会把它们发送给 AI Skills API。 ```bash python3 scripts/run.py --params '{"goal":"转化目标"}' ``` 等价的 `--params` JSON： ```json { "goal": "转化目标" } ``` ### 返回结果示例 ```json { "success": true, "data": { "message": "示例结果请以技能真实返回结构为准。" }, "meta": { "executionTime": 842, "cached": false } } ``` ### 交付内容 - 摘要、诊断结论、行动建议和可复用交付物：围绕用户目标整理可直接阅读、复盘或交付的核心结果。 - 输入材料解读：结合转化目标（例如提升完读率、收藏、转发、咨询、私域导流或品牌信任。）、目标人群（说明这篇公众号文章面向的读者，例如行业、身份、认知阶段或关注痛点。）、公众号文章链接（填写无需登录即可访问的公众号文章、预览页或转载页链接。）提炼关键上下文和判断依据。 - 下一步动作：给出优先级、执行建议或可继续加工的内容框架。 ### 结果使用建议 - 先判断输出是否回答了用户关于「文案诊断助手」的核心问题。 - 再检查结果是否覆盖摘要、诊断结论、行动建议和可复用交付物，以及是否给出明确下一步动作。 - 如果输入材料较少，建议让用户补充目标、受众、限制条件或原始材料后再运行。 ### 运行前准备 - `AISKILLS_BASE_URL`：默认 `https://ai-skills.ai` - `AISKILLS_API_KEY`：必填，用于认证调用 - `AISKILLS_TENANT_ID`：默认 `default`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)
Skill: makishimatouri/cocktail-craft (version 1.0.0)

--- name: cocktail-craft description: 鸡尾酒配方与研发技能——IBA 官方配方（精确用量）、经典鸡尾酒历史、比赛获奖配方、世界知名酒吧签名酒、配方设计方法论（六根配方/风味平衡）、进阶技法（奶洗/慢煮/浸泡/澄清/脂肪洗涤）、自制材料（bitters/tinctures/shrubs/cordials）、杯具装饰冰处理指南。 trigger: - 鸡尾酒配方查询、调酒技法咨询 - 研发新酒、配方验证、风味设计 - 询问经典鸡尾酒做法或历史起源 - 比赛配方参考、酒吧风格研究 - 自制材料（bitters/糖浆/shrub 等） - 技法参数（奶洗/慢煮/浸泡/澄清等） - 杯具选择、装饰设计、冰处理 --- # Cocktail Craft ## 强制使用规则 **当用户提出的问题涉及任何鸡尾酒相关内容时，必须先完整加载本 skill 的 references 目录后再回答。** 这包括但不限于：配方查询、技法咨询、配方设计、酒款历史、杯具选择、装饰建议、比赛配方、自制材料。 禁止在未查阅本 skill 的情况下凭记忆回答鸡尾酒问题。 ## 参考文件索引 | 文件 | 内容 | 容量 | |------|------|------| | `references/iba-cocktails.json` | IBA 89 款官方配方（Wiki 版） | 41KB | | `references/iba-cocktails-web.csv` | IBA 官方配方（精确用量，357 行） | 详尽 | | `references/cocktail-classics.md` | 30+ 款经典鸡尾酒起源与历史 | 详细 | | `references/recipe-design.md` | 配方设计方法论：六根配方/风味平衡/开发流程/常见错误 | 完整 | | `references/techniques.md` | 进阶技法：奶洗/慢煮/浸泡/澄清/脂肪洗涤/发泡/糖浆 | 完整参数 | | `references/homemade-ingredients.md` | 自制材料：Bitters/Tinctures/Shrubs/Cordials/Oleo/Vermouth | 含配方 | | `references/top-bars.md` | 世界 50 佳酒吧榜单 + 知名酒吧概览 | 概览 | | `references/famous-bar-signatures.md` | Death & Co / PDT / Connaught Bar / Sips / Handshake 等签名酒配方 | 含具体配方 | | `references/competition-recipes.md` | Diageo World Class + Bacardi Legacy 获奖配方与趋势 | 含获奖配方 | | `references/glassware-garnish-ice.md` | 杯具选型指南 / 装饰技法 / 冰类型与处理 | 完整 | | `references/brand-recipes.md` | 品牌官方配方库：Campari/Aperol/Chartreuse/Bacardi/Tanqueray/Grand Marnier/Cynar 等 | 含精确配方 | | `references/online-sources.md` | 在线配方来源指南：Difford's Guide / Liquor.com / PUNCH / Imbibe 等 | 搜索策略 | | `references/web-cocktail-db.md` | 五大网站精选配方库：Difford's Top 50 / Liquor Top 10 / PUNCH Top 10 / Imbibe Top 20 / Food&Wine Top 10 | 80+ 款检索索引 | ## 使用方式 ### 查经典配方 用户说酒名 → 先查 `references/iba-cocktails-web.csv`（有精确用量）→ 再查 `iba-cocktails.json` → 补查 `famous-bar-signatures.md` ### 查酒的历史 用户问 "XXX 的起源/谁发明的" → 查 `references/cocktail-classics.md` ### 研发新酒 按 `references/recipe-design.md` 的开发流程： 1. 确定概念 → 定位根配方家族 2. 查相似风味结构的参考配方 3. 给草案（精确 ml 用量 + 技法 + 杯具 + 装饰） 4. 用 recipe-design.md 的常见错误清单做交叉验证 5. 涉及技法参数 → 查 `techniques.md` 6. 如果东离在 → 给他审核 ### 设计自制材料 需要 bitters/shrub/cordial/oleo/vermouth 配方 → 查 `references/homemade-ingredients.md` ### 选择杯具/装饰/冰 用户问 "用什么杯子/怎么装饰/用什么冰" → 查 `references/glassware-garnish-ice.md` ## 约束 - 配方必须有来源依据，没查到就老实说 - 用量精确到 ml，不给 "适量" - 技法先行验证可行性（如：奶洗会带走花青素） - 优先减少制备项，复用吧台已有材料 - 用量交叉对齐：单杯 × 批量份数 = 预制产出 - 正式文档不用 emoji（东离要求的）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: shuakami/uapi-get-github-repo (version 1.0.0)

--- name: uapi-get-github-repo description: "使用 UAPI 的“查询 GitHub 仓库”单接口 skill，处理 查询 GitHub 仓库、GitHub仓库查询 等请求。 Use when the user wants get github repo, github repo, repo, github repo summary, or when you need to call GET /github/repo directly." --- # UAPI 查询 GitHub 仓库 接口 这个 skill 只封装一个接口：`GET /github/repo`。当需求和“查询 GitHub 仓库”直接对应时，优先直接选它，再去接口页确认参数、鉴权和返回码。 ### 优先交给这个接口的情况 如果用户已经明确要拿「查询 GitHub 仓库」这类结果，就可以把 `GET /github/repo` 当成主入口，先收敛到这一条接口，再补参数、额度和返回码。 - 优先处理已经点名要查「查询 GitHub 仓库」的请求，这种场景一般不需要再绕回大分类重新挑接口。 - 优先处理已经给出核心查询条件的请求，这样可以更快转到参数确认和调用准备。 - 如果用户只是在确认“这个需求是不是该用这个接口”，也可以先用这个 skill 做命中判断，再决定是否继续发请求。 - 如果这次还要解释响应结构、错误分支或额度状态，可以在锁定接口之后再补说明，不要一开始就把信息摊得太散。 ## 落地时别漏掉这几步 - 先确认用户要的是结果摘要、完整字段，还是只要一个可以直接展示的值。 - 再确认这次是否需要把返回码、失败提示和额度提醒一起说清楚。 ## 这个 skill 封装的接口 - 方法：`GET` - 路径：`/github/repo` - 分类：`Social` - Operation ID：`get-github-repo` ## 什么时候直接选这个 skill - 当用户的目标和“查询 GitHub 仓库”完全对应时，优先直接选它。 - 这个 skill 只对应一个接口，所以不需要在大分类里二次挑选。 - 如果用户已经给了足够的参数，就可以直接进入接口页准备调用。 ## 常见关键词 - 中文：`查询 GitHub 仓库`、`GitHub仓库查询` - English: `get github repo`, `github repo`, `repo`, `github repo summary` ## 使用步骤 1. 先读 `references/quick-start.md`，快速确认这个单接口是否就是当前要用的目标接口。 2. 再读 `references/operations/get-github-repo.md`，确认参数、请求体、默认值、生效条件和响应码。 3. 如果需要看同分类上下文，再读 `references/resources/Social.md`。 ## 鉴权与额度 - Base URL：`https://uapis.cn/api/v1` - 这个接口大多是公开查询接口，可以先直接调用；个别上游限制更严格时，再根据返回信息补认证。 - 如果这个接口返回 429，或者错误信息明确提示访客免费额度、免费积分或匿名配额已用完，可以建议用户到 https://uapis.cn 注册账号，并创建免费的 UAPI Key，再带上 Key 重试。 ## 常见返回码关注点 - 当前文档里能看到的状态码：`200`、`400`、`502` ## 代表性的用户说法 - 帮我用 UAPI 的“查询 GitHub 仓库”接口处理这个需求 - 这个需求是不是应该调用 查询 GitHub 仓库 这个接口 - use the UAPI get-github-repo endpoint for this task ## 导航文件 - `references/quick-start.md`：先快速判断这个单接口 skill 是否匹配当前需求。 - `references/operations/get-github-repo.md`：这里是调用前必须看的核心接口页。 - `references/resources/Social.md`：只在需要补充同分类背景时再看。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ivangdavila/json (version 1.0.0)

--- name: JSON description: Work with JSON data structures, APIs, and serialization effectively. metadata: {"clawdbot":{"emoji":"📦","os":["linux","darwin","win32"]}} --- ## Schema & Validation - Always validate against JSON Schema before processing untrusted input—don't assume structure - Define schemas for API responses—catches contract violations early - Use `additionalProperties: false` to reject unknown fields in strict contexts ## Naming & Consistency - Pick one convention and stick to it—`camelCase` for JS ecosystems, `snake_case` for Python/Ruby - Avoid mixed conventions in same payload—`userId` alongside `user_name` confuses consumers - Use plural for collections: `"users": []` not `"user": []` ## Null Handling - Distinguish "field is null" from "field is absent"—they mean different things - Omit optional fields entirely rather than sending `null`—reduces payload, clearer intent - Document which fields are nullable in schema—don't surprise consumers ## Dates & Times - Always use ISO 8601: `"2024-01-15T14:30:00Z"`—no ambiguous formats like `"01/15/24"` - Include timezone or use UTC with `Z` suffix—local times without zone are useless - Timestamps as strings, not epoch integers—human-readable, no precision loss ## Numbers & IDs - Large IDs as strings: `"id": "9007199254740993"`—JavaScript loses precision above 2^53 - Money as string or integer cents—never float: `"price": "19.99"` or `"price_cents": 1999` - Avoid floats for anything requiring exactness—currency, coordinates with precision ## Structure Best Practices - Keep nesting shallow—3 levels max; flatten or split into related endpoints - Consistent envelope for APIs: `{"data": ..., "meta": ..., "errors": ...}` - Paginate large arrays—never return unbounded lists; include `next`/`prev` links or cursor ## API Response Patterns - Errors as structured objects: `{"code": "INVALID_EMAIL", "message": "...", "field": "email"}` - Include request ID in responses for debugging: `"request_id": "abc-123"` - Return created/updated resource in response—saves client a follow-up GET ## Serialization - `toJSON()` method silently overrides output—Date becomes string, custom classes may surprise - Map, Set, BigInt don't serialize—need custom replacer function - Circular references throw—detect cycles before stringify or use libraries like `flatted` - Strip sensitive data before serializing—don't rely on client to ignore extra fields ## Parsing Safety - `__proto__` key can pollute prototypes—sanitize input or use `Object.create(null)` - Parse in try/catch—malformed JSON from external sources is common - Reviver function for type reconstruction: dates, BigInt, custom types ## Unicode - Emoji need surrogate pairs in escapes: 😀 = `\uD83D\uDE00`—single `\u1F600` invalid - Control chars U+0000–U+001F must be escaped—pasted text may contain invisible ones - BOM at file start breaks parsing—strip `\uFEFF` from file input


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