OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zd200572/blast-species-identification (version 1.0.0)

# BLAST 物种注释工具技能 ## 描述 提供BLAST物种注释工具的使用指南和快速调用功能。包含两个主要工具： - `blast_annotation_tool.py` - 对指定FASTA序列进行BLAST注释 - `top_asv_blast.py` - 从OTU表提取Top ASV并进行BLAST注释 ## 安装依赖 ```bash pip install biopython ``` ## 工具一：blast_annotation_tool.py ### 基本用法 ```bash # 基本用法：输入FASTA文件和输出目录 python3 blast_annotation_tool.py sequences.fasta results/ # 使用序列ID到样本名的映射文件 python3 blast_annotation_tool.py sequences.fasta results/ --mapping mapping.csv # 跳过已存在的结果（断点续传） python3 blast_annotation_tool.py sequences.fasta results/ --skip-existing ``` ### 参数说明 - `input`: 输入FASTA文件路径 (必填) - `output`: 输出目录路径 (必填) - `--mapping, -m`: 序列ID到样本名的映射文件 (CSV格式) - `--delay, -d`: 每次BLAST请求之间的延迟秒数 (默认: 3) - `--hits, -n`: 每个样本保留的BLAST hits数量 (默认: 10) - `--skip-existing, -s`: 跳过已存在的结果文件 (默认: False) ## 工具二：top_asv_blast.py ### 基本用法 ```bash # 基本用法 python3 top_asv_blast.py taxa_table.xls rep.fasta results/ # 跳过已存在的结果（断点续传） python3 top_asv_blast.py taxa_table.xls rep.fasta results/ --skip-existing # 自定义参数 python3 top_asv_blast.py taxa_table.xls rep.fasta results/ --delay 5 --hits 20 ``` ### 参数说明 - `otu_table`: OTU表文件 (.xls, .tsv, .csv) (必填) - `fasta`: 代表性序列FASTA文件 (必填) - `output`: 输出目录路径 (必填) - `--top-n, -n`: 每个样本提取前N个ASV (默认: 1) - `--delay, -d`: 每次BLAST请求之间的延迟秒数 (默认: 3) - `--hits`: 每个ASV保留的BLAST hits数量 (默认: 10) - `--skip-existing, -s`: 跳过已存在的结果文件 (默认: False) ## 输入文件格式 ### FASTA文件 ``` >ASV1 TAGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAG... >ASV2 TAGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAG... ``` ### 映射文件 (可选) CSV格式，第一列为序列ID，第二列为样本名： ```csv ASV1,D1-8 ASV2,J2-8 ASV3,D3-8 ``` ### OTU表格式 - 第一列：ASV/OTU ID - 中间列：样本序列计数（支持重复样本自动合并） - 最后一列：Taxonomy注释 ## 输出文件 - 每个样本的BLAST结果CSV文件 - 汇总表 (blast_summary.csv) - Top ASV信息表 (top_asv_info.csv) ## 注意事项 1. 需要网络连接访问NCBI BLAST服务 2. 每次比对可能需要几秒到几十秒 3. 建议使用`--delay`参数避免请求过于频繁 4. 使用`--skip-existing`可实现断点续传 ## 快速调用 当您需要进行BLAST物种注释时，只需说： - "使用blast注释工具" - "运行top asv blast" - "BLAST物种注释指南" 我会为您提供详细的参数说明和使用方法。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ggyybb/travel-planner-plus (version 1.0.1)

--- name: travel-planner description: 为用户生成个性化旅游攻略。当用户要求"做一份旅游攻略"、"帮我规划X天X晚的旅行"、"生成某地旅游行程"、"制定旅游计划"时使用此技能。支持国内外各大城市，自动搜索景点/酒店/美食，计算交通路线，生成Word文档并发送给用户。 --- # Travel Planner - 旅游攻略生成技能 ## 功能概述 为用户生成完整旅游攻略，包含： - 景点、酒店、美食推荐（来自网络搜索） - 精确交通路线规划（来自百度地图API） - 按时间顺序排列的详细行程表 - Word文档直接发送给用户 ## 使用流程 ### Step 1: 检查API密钥 检查 MEMORY.md 或 TOOLS.md 中是否已有百度AK： - 如果有：直接使用 - 如果没有：询问用户是否提供百度地图开放平台的AK（用于地理编码和路线规划） ### Step 2: 收集用户需求 询问确认以下信息： 1. **目的地**：哪个城市？ 2. **天数**：X天X晚？ 3. **旅游主题/人群**：年轻人/亲子/家庭/商务？主题偏好？ 4. **特殊要求**：要少走路/景点集中/人气高/购物游/文化游？ ### Step 3: 网络搜索 使用 multi-search-engine（通过 web_fetch + Bing URL）搜索： - `{城市} + {天数} + 旅游攻略 + 景点推荐` - `{城市} + 必吃美食 + 网红餐厅` - `{城市} + 年轻人 + 热门景点` - `{城市} + {天数} + 住宿推荐 + 酒店` 参考搜索关键词： ``` # 景点攻略 {城市} {天数} 旅游攻略 景点 2024/2025 {城市} 必去景点 排行榜 年轻人 {城市} 小众景点 网红打卡 # 美食住宿 {城市} 特色美食 必吃 餐厅推荐 {城市} 住宿推荐 酒店 位置 # 交通 {城市} 机场/车站 到 市中心 交通方式 ``` ### Step 4: 地理编码 使用百度地图开放平台 API 获取每个地点坐标： ``` 地理编码API: https://api.map.baidu.com/geocoding/v3/?address={地点}&output=json&ak={AK} 驾⻋路线API: https://api.map.baidu.com/direction/v2/driving?origin={lat,lng}&destination={lat,lng}&ak={AK} 公交路线API: https://api.map.baidu.com/direction/v2/transit?origin={lat,lng}&destination={lat,lng}&ak={AK} ``` ### Step 5: 智能排程 将景点按以下原则分组： 1. **地理距离近的放同一天** 2. **考虑游览时间**（每个大景点约1-3小时） 3. **考虑交通时间**（同一区域景点间尽量<30分钟） 4. **不要排太满**，留出吃饭、休息、拍照时间 5. **早晚穿插**，不要全是同类型景点 ### Step 6: 生成Word文档 使用 Python `python-docx` 库生成攻略文档，包含： 1. **封面信息**：目的地 + 天数 + 主题 2. **每日行程表**：时间 | 活动 | 地点 | 游览时长 | 交通 3. **交通总览表**：路线 | 距离 | 方式 | 用时 | 费用 4. **住宿推荐表**：区域 | 酒店类型 | 优点 5. **美食推荐表**：类型 | 推荐店铺 | 人均 6. **实用Tips**：建议、注意事项、必备app ### Step 7: 发送文件 使用 `message` 工具通过飞书发送Word文件给用户。 ### Step 8: 免责声明 提醒用户： > 📌 **免责声明**：地点、交通信息来源于百度地图开放平台，住宿、饮食推荐仅供参考。建议出发前参考小红书、携程等平台做进一步细化攻略。 --- ## 百度API使用方法 ### 地理编码（地址→坐标） ```bash curl "https://api.map.baidu.com/geocoding/v3/?address={地址}&output=json&ak={AK}" ``` 返回：`{ status: 0, result: { location: { lng: 经度, lat: 纬度 } } }` ### 公交路线规划 ```bash curl "https://api.map.baidu.com/direction/v2/transit?origin={lat},{lng}&destination={lat},{lng}&ak={AK}" ``` 返回：`{ status: 0, result: { routes: [{ distance: 距离(米), duration: 时间(秒), price: 费用 }] } }` ### 驾⻋路线规划 ```bash curl "https://api.map.baidu.com/direction/v2/driving?origin={lat},{lng}&destination={lat},{lng}&ak={AK}" ``` --- ## 智能排程算法 ``` 输入：景点列表、游玩天数 输出：每日行程安排 步骤： 1. 将景点按地理坐标聚类（距离<3公里的归为一组） 2. 优先选择人气高、交通便利的景点作为核心 3. 将景点组分配到各天，遵循： - 同区域景点尽量放同一天 - 每天不超过3个大景点 - 每个大景点间交通时间<30分钟 4. 在景点间插入餐厅（步行10分钟以内优先） 5. 考虑酒店位置，选择最方便的一组 ``` --- ## 输出模板 见 references/output-template.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wsjwong/global-price-comparison (version 1.0.2)

--- name: global-price-comparison description: Discover and compare the same product across multiple countries and source types (official stores, marketplaces, retailers) using Brave and/or Tavily web search, then normalize all offers to USD for ranking and spread analysis. Use when users ask for global product price comparison, cross-country official price checks, or cheapest-market analysis. --- # Global Price Comparison Use this skill to run a **global same-product price scan** and output a **USD-normalized comparison**. ## Quick start ```bash # 1) Create a starter CSV template python scripts/global_price_compare.py template --out /tmp/offers.csv # 2) (Optional) discover candidate links by country/source type # Uses Brave and Tavily when available (BRAVE_API_KEY / TAVILY_API_KEY) python scripts/global_price_compare.py discover \ --product "iPhone 16 Pro 256GB" \ --countries US,JP,DE,UK \ --source-types official_store,marketplace,electronics_retailer \ --engine all \ --out /tmp/discover.json # 3) Fill /tmp/offers.csv with verified offers, then compare python scripts/global_price_compare.py compare \ --input /tmp/offers.csv \ --format markdown ``` ## Workflow 1. Define exact product variant (model/storage/spec). 2. Run `discover` to get candidate URLs by market and source type. 3. Add verified offers to CSV (`product,country,currency,source_type,source_name,price,url`). 4. Run `compare` to normalize all prices to USD and rank best/worst. 5. Share markdown/JSON/CSV output. ## Commands ### Template ```bash python scripts/global_price_compare.py template --out /tmp/offers.csv ``` ### Discover candidate links ```bash python scripts/global_price_compare.py discover \ --product "PlayStation 5 Slim" \ --countries US,JP,DE \ --source-types official_store,marketplace,electronics_retailer \ --engine all \ --count 3 \ --format markdown ``` Notes: - `discover` supports `--engine brave|tavily|all` (default `all`). - For `all`, it uses **Brave first** and only falls back to Tavily when Brave fails or returns no results. - Set one or both keys as needed: `BRAVE_API_KEY`, `TAVILY_API_KEY`. - Source types are intentionally generic (not local-store specific). ### Compare offers in USD ```bash python scripts/global_price_compare.py compare \ --input /tmp/offers.csv \ --format markdown \ --out /tmp/comparison.csv ``` Outputs include: - ranked offers by USD price - delta vs best offer - spread in USD and % ## Defaults Default country set: - `US, UK, JP, DE, FR, CA, AU, SG, HK, TW` Default source types: - `official_store` - `marketplace` - `electronics_retailer` - `general_retailer` ## Data/quality guardrails Before finalizing recommendations: - confirm same model/variant - confirm tax/shipping basis consistency - separate new vs refurbished/used - keep URL + timestamp evidence Detailed schema + guardrails: - `references/data-shape-and-source-types.md` ## Resources - Script: `scripts/global_price_compare.py` - Reference: `references/data-shape-and-source-types.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mayuyang-study/knowledge-curator (version 1.0.0)

# knowledge-curator - 知识库管理技能 ## 描述 自动抓取用户发送的链接内容（小红书、B 站、知乎、YouTube 等），整理成结构化知识库，支持按主题分类、增量更新和内容检索。 ## 触发条件 ### 收藏内容（必须满足以下条件之一） - 用户明确请求保存内容到知识库（"收藏"、"保存到知识库"、"记下来"、"kb save"等） - 消息中包含"收藏"、"收藏到知识库"、"kb"等关键词 + URL **注意**：单纯发送链接不会触发收藏，必须有明确的收藏意图！ ### 查询内容 - 用户查询知识库内容（"/kb search"、"/kb list"、"搜索..."、"查找..."等） ## 功能 ### 1. 内容抓取 - **小红书**: 提取标题、正文、图片描述、标签 - **B 站**: 提取视频标题、简介、字幕（如有）、标签、UP 主信息 - **知乎**: 提取问题、回答、作者、赞同数 - **YouTube**: 提取视频标题、描述、字幕/转录、标签 - **通用网页**: 提取标题、正文、元描述 ### 2. 自动分类 按内容主题自动分类到以下类别： - **科技**: AI、编程、数码、互联网、科学 - **生活**: 美食、旅行、家居、日常、情感 - **学习**: 教育、课程、教程、知识、技能 - **娱乐**: 影视、音乐、游戏、综艺、明星 - **工作**: 职场、管理、效率、工具、商业 - **健康**: 运动、饮食、医疗、心理、养生 ### 3. 输出格式 每个知识库条目为 Markdown 文件，包含： ```markdown # [标题] **原始链接**: [URL] **来源平台**: [平台名称] **收藏日期**: YYYY-MM-DD HH:mm **分类**: [主题分类] **标签**: #标签 1 #标签 2 #标签 3 ## 摘要 [200-500 字内容摘要] ## 关键知识点 - 知识点 1 - 知识点 2 - 知识点 3 ## 原文内容 [完整或精简的原文内容] ## 备注 [用户添加的备注或 AI 的补充说明] ``` ### 4. 增量更新 - 检测重复链接，避免重复保存 - 支持同一主题下追加新内容 - 维护知识库索引文件 `index.json` ### 5. 查询检索 - 关键词搜索（标题、标签、内容） - 按分类筛选 - 按时间范围筛选 - 语义相似度检索（可选） ## 使用方法 ### 保存内容（需要明确指令） **正确示例**： ``` 用户：收藏 https://www.bilibili.com/video/BV1xx411c7mD AI: 📚 已保存到知识库【学习/编程】类别 标题：Python 入门教程 标签：#Python #编程 #教程 查看：`/kb search Python` ``` ``` 用户：保存到知识库：https://zhuanlan.zhihu.com/p/123456 AI: ✅ 已收藏到知识库 ``` ``` 用户：kb save https://youtube.com/watch?v=xxx AI: ✅ 已保存到知识库 ``` **错误示例**（不会触发收藏）： ``` 用户：https://www.bilibili.com/video/BV1xx411c7mD AI: （仅正常回复链接内容，不收藏） ``` ### 查询内容 ``` 用户：/kb search Python AI: 🔍 找到 3 条相关内容： 1. [学习/编程] Python 入门教程 (2026-03-15) 2. [学习/编程] Python 数据分析实战 (2026-03-10) 3. [工作/效率] 用 Python 自动化办公 (2026-03-08) ``` ### 管理命令 - `/kb list [category]` - 列出知识库内容 - `/kb search <keywords>` - 搜索内容 - `/kb delete <id>` - 删除条目 - `/kb export` - 导出知识库 - `/kb stats` - 查看统计信息 ## 文件结构 ``` knowledge-curator/ ├── SKILL.md # 本文件 ├── README.md # 详细文档 ├── scripts/ │ ├── fetch.js # 内容抓取 │ ├── summarize.js # 内容总结 │ ├── categorize.js # 自动分类 │ ├── store.js # 存储管理 │ └── query.js # 查询检索 ├── references/ │ └── examples.md # 使用示例 └── knowledge-base/ # 知识库存储 ├── 科技/ ├── 生活/ ├── 学习/ ├── 娱乐/ ├── 工作/ ├── 健康/ └── index.json # 索引文件 ``` ## 依赖 - `web_fetch` 工具：抓取网页内容 - `exec` 工具：运行 Node.js 脚本 - 文件系统：存储知识库 ## 注意事项 - 部分平台可能有反爬限制，需适当延迟请求 - 视频字幕提取依赖平台 API 或第三方服务 - 建议定期备份知识库目录 - 敏感内容需用户确认后再保存 ## 版本 v1.0.0 - 初始版本

path: _meta.json
content: ownerId: knowledge-curator
slug: knowledge-curator
version: 1.0.0
publishedAt: 1710537600000

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: googleworkspace-bot/gws-sheets (version 1.0.12)

--- name: gws-sheets description: "Google Sheets: Read and write spreadsheets." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws sheets --help" --- # sheets (v4) > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. ```bash gws sheets <resource> <method> [flags] ``` ## Helper Commands | Command | Description | |---------|-------------| | [`+append`](../gws-sheets-append/SKILL.md) | Append a row to a spreadsheet | | [`+read`](../gws-sheets-read/SKILL.md) | Read values from a spreadsheet | ## API Resources ### spreadsheets - `batchUpdate` — Applies one or more updates to the spreadsheet. Each request is validated before being applied. If any request is not valid then the entire request will fail and nothing will be applied. Some requests have replies to give you some information about how they are applied. The replies will mirror the requests. For example, if you applied 4 updates and the 3rd one had a reply, then the response will have 2 empty replies, the actual reply, and another empty reply, in that order. - `create` — Creates a spreadsheet, returning the newly created spreadsheet. - `get` — Returns the spreadsheet at the given ID. The caller must specify the spreadsheet ID. By default, data within grids is not returned. You can include grid data in one of 2 ways: * Specify a [field mask](https://developers.google.com/workspace/sheets/api/guides/field-masks) listing your desired fields using the `fields` URL parameter in HTTP * Set the includeGridData URL parameter to true. - `getByDataFilter` — Returns the spreadsheet at the given ID. The caller must specify the spreadsheet ID. For more information, see [Read, write, and search metadata](https://developers.google.com/workspace/sheets/api/guides/metadata). This method differs from GetSpreadsheet in that it allows selecting which subsets of spreadsheet data to return by specifying a dataFilters parameter. Multiple DataFilters can be specified. - `developerMetadata` — Operations on the 'developerMetadata' resource - `sheets` — Operations on the 'sheets' resource - `values` — Operations on the 'values' resource ## Discovering Commands Before calling any API method, inspect it: ```bash # Browse resources and methods gws sheets --help # Inspect a method's required params, types, and defaults gws schema sheets.<resource>.<method> ``` Use `gws schema` output to build your `--params` and `--json` flags.


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

--- name: Aliexpress slug: aliexpress version: 1.0.0 description: Navigate Aliexpress as buyer, seller, or dropshipper with vendor evaluation, price comparison, and scam detection. metadata: {"clawdbot":{"emoji":"🛒","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## When to Use User needs help with Aliexpress purchases, selling, or sourcing. Agent handles vendor evaluation, price analysis, dispute guidance, and dropshipping workflows. ## Quick Reference | Topic | File | |-------|------| | Vendor signals | `vendors.md` | | Dropshipping | `dropshipping.md` | ## Core Rules ### 1. Vendor Evaluation Beyond Stars Stars alone are misleading. Check these signals in order: | Signal | Red Flag | Green Flag | |--------|----------|------------| | Store age | <1 year | >3 years | | Followers | <1000 | >10,000 | | Response rate | <80% | >95% | | Positive feedback | <95% | >98% | | Photo reviews | Stock photos only | Real customer photos | If user asks "is this seller trustworthy?" — require ALL five signals. ### 2. Real Cost Calculation Aliexpress price ≠ landed cost. Always compute: ``` Landed Cost = Item Price + Shipping + Import Tax (if >$150) + Payment Fee (2-3%) ``` - Shipping varies 3-40 days depending on method - "Free shipping" often means 40-60 day wait - ePacket/AliExpress Standard = 15-25 days typical ### 3. Same Product Different Vendors Before recommending a purchase, search same product across vendors: - Price can vary 50-200% for identical items - Check if dropshipping from same factory (similar photos) - Lower price + low reviews = test batch risk ### 4. Dispute Timing Aliexpress dispute window rules: - Open dispute before buyer protection expires - Wait 10+ days past delivery estimate before "not received" - Document everything with screenshots - Partial refund often better than full dispute loss ### 5. Dropshipping Specifics See `dropshipping.md` for margin calculation and supplier vetting. Key rule: Never source from vendors with <98% positive and <2 years history. ### 6. Scam Detection Patterns Common scams to warn about: - Price too good (>50% below others) = bait and switch - "Ships from local warehouse" but no proof = fake - Brand names at 90% discount = counterfeit - Empty box scam = always request tracking with photos ## Common Traps - Recommending based on star rating alone → check store metrics, photo reviews, response rate - Ignoring shipping time → "free shipping" can mean 60 days - Calculating margins without landed cost → shipping + fees eat profit - Opening disputes too early → wait until protection window - Trusting "Top Brand" badge → verify independently


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/headless-browser (version 2.0.1)

--- name: "headless-browser" version: "2.0.1" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Headless Browser concepts, best practices, and implementation pat..." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [headless,browser, reference] category: "devtools" --- # Headless Browser Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Headless Browser concepts, best practices, and implementation pat... No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet 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: rabin-thami/bun-runtime (version 1.0.0)

--- name: bun-runtime description: Bun runtime capabilities for filesystem, process, and network operations. Use when you need to execute Bun-specific operations like Bun.file(), Bun.write(), or Bun.glob() for optimized file handling, or when working with Bun's native process/network APIs. Triggered by requests for Bun runtime features, file operations with Bun, or high-performance I/O tasks. --- # Bun Runtime Native Bun runtime operations for filesystem, process, and network tasks. ## When to Use Use this skill when: - Working with Bun's native file APIs (`Bun.file()`, `Bun.write()`, `Bun.glob()`) - Need optimized I/O operations in Bun environment - Running Bun-specific process commands - Making network requests with Bun's fetch ## Filesystem Operations ### Read File ```bash scripts/bun-fs.sh read /path/to/file.txt ``` Returns JSON: `{"content": "file contents"}` ### Write File ```bash scripts/bun-fs.sh write /path/to/file.txt "content here" ``` Creates parent directories automatically. Returns JSON: `{"written": true, "path": "/path/to/file.txt"}` ### Glob Files ```bash scripts/bun-glob.sh "/tmp/*.txt" ``` Returns JSON: `{"files": ["/tmp/file1.txt", "/tmp/file2.txt"], "count": 2}` ## Process Operations ### Execute Command ```bash scripts/bun-process.sh "ls -la" ``` Runs shell command and returns output. ## Network Operations ### HTTP Request ```bash scripts/bun-fetch.sh "https://api.example.com" "GET" ``` Makes HTTP request using Bun's native fetch. ## Notes - All scripts use Bun's native APIs for better performance - File operations automatically handle encoding - Errors are returned with clear messages


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: canbirlik/wiz-light-control (version 1.0.1)

--- name: wiz-light-control description: Controls Wiz smart bulbs (turn on/off, RGB colors, disco mode) via local WiFi. author: "@canbirlik" emoji: "💡" platforms: - darwin - linux - win32 dependencies: python: - pywizlight --- # Wiz Light Control Skill This skill allows the agent to control physical Wiz smart lights on the local network. ⚠️ **IMPORTANT CONFIGURATION:** The IP address `192.168.1.109` used in examples is a placeholder. You **MUST** find the actual Local IP address of the Wiz Bulb on the network before running commands. ## 🛠️ Installation & Setup (READ FIRST) **Before running the script for the first time, you MUST set up the Python environment.** The agent should verify if `venv` exists. If not, run this setup command block automatically: ```bash # 1. Create Virtual Environment and Install Dependencies python3 -m venv venv && source venv/bin/activate && pip install -r requirements.txt ``` ## Tools & Usage ### `wiz.py` The main script to interact with the light. **Arguments:** - `--ip`: The IP address of the bulb (Required). - `--action`: The command to execute. Options: `on`, `off`, `color`, `disco`. - `--r`, `--g`, `--b`: Red, Green, Blue values (0-255) (Used for `color` action). - `--duration`: Duration in seconds (Used for `disco` action). **Example Prompts:** - "Turn on the disco mode in my room for 30 seconds." - "Turn off the light." - "Change the light color to red." **Execution Examples:** ```bash # 1. Turn on Disco Mode (Check IP first!) python wiz.py --ip 192.168.1.109 --action disco --duration 60 # 2. Turn OFF python wiz.py --ip 192.168.1.109 --action off # 3. Turn ON python wiz.py --ip 192.168.1.109 --action on # 4. Set specific color (e.g., Red) python wiz.py --ip 192.168.1.109 --action color --r 255 --g 0 --b 0

path: requirements.txt
content: pywizlight

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: banxian87/ai-meeting-pro (version 1.0.0)

--- name: meeting-assistant description: AI meeting assistant for agenda generation, minute taking, and action item tracking using Manager-Worker pattern. --- # Meeting Assistant AI-powered meeting assistant that helps you organize, conduct, and follow up on meetings efficiently. --- ## Features ### 📋 Agenda Generation - Automatic agenda creation from meeting topic - Time allocation for each item - Participant preparation checklist ### 📝 Minute Taking - Real-time transcription support - Key point extraction - Decision recording ### ✅ Action Item Tracking - Extract action items from discussion - Assign owners and deadlines - Follow-up reminders --- ## Usage ```javascript const assistant = new MeetingAssistant(); // 创建会议议程 const agenda = await assistant.createAgenda({ topic: '产品评审会议', duration: 60, participants: 5, goals: ['评审新功能', '确定上线时间'] }); // 生成会议纪要 const minutes = await assistant.generateMinutes({ transcript: meetingTranscript, keyDecisions: ['决定上线时间'], actionItems: ['张三负责测试'] }); ``` --- ## Installation ```bash clawhub install meeting-assistant ``` --- ## License MIT --- ## Version 1.0.0 --- ## Created 2026-04-02


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