OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yangxiao971208-star/data-processing-script-generator (version 1.0.0)

--- name: data-processing-script-generator description: | 自动化生成数据处理脚本。根据用户提供的 Excel 模板 + ERP 导出数据，生成 Python 脚本自动处理数据并生成报表。 当用户说"帮我生成数据处理脚本"、"做一个自动处理数据的脚本"、"根据模板生成脚本"时使用此 skill。 也适用于用户已有 Excel 底板和导出数据，想要自动化处理流程的场景。 --- # 自动化数据处理脚本生成 ## 目标 根据用户提供的 Excel 模板 + ERP 导出数据，自动生成可运行的数据处理脚本。 --- ## 核心流程 ### 第一步：获取素材（让用户提供） **必须向用户确认并获取：** 1. **Excel 模板文件（底板）** — 包含格式、透视表、公式等 2. **ERP 导出数据的样本** — CSV 或 Excel，需要知道是哪个 sheet > 先读取文件，分析结构（sheet、列名、数据格式），才能继续后续步骤 --- ### 第二步：确认处理逻辑（反复确认） | 确认项 | 说明 | |--------|------| | **数据来源** | ERP 导出是什么格式？CSV 还是 Excel？是哪个 sheet？ | | **清洗规则** | 哪些行需要过滤？（如：空代码、零库存） | | **计算字段** | 需要计算什么派生字段？（如：周转天数、周转判断）| | **追加还是覆盖** | 每天生成新文件，还是追加到同一个文件？ | | **特殊逻辑** | 有没有其他特殊处理逻辑？ | --- ### 第三步：确认映射关系 | 确认项 | 说明 | |--------|------| | **基准表** | 模板中哪个 sheet 是「基准」？包含什么映射？（产品代码 → 品类/供应商） | | **字段对应** | 导出数据的各列对应模板的哪些列？ | --- ### 第四步：确认文件路径（反复确认） | 确认项 | 说明 | |--------|------| | **导出文件夹路径** | ERP 导出文件放在哪里？ | | **模板文件路径** | 底板/基准表在哪里？ | | **输出文件夹路径** | 生成的报表输出到哪里？ | > ⚠️ 路径必须固定，如果后续调整，需要修改脚本中的路径变量 --- ### 第五步：确认文件命名规范 | 确认项 | 说明 | |--------|------| | **导出文件命名** | 每天导出的文件名前缀是什么？如 `商品库存导出-2026-04-02.csv` | | **模板文件命名** | 底板必须叫 `XXX-底板.xlsx`，累积文件叫 `XXX-汇总.xlsx` | | **输出文件命名** | 每天生成的文件名叫什么？如 `每日库存提醒-2026-04-02.xlsx` | --- ### 第六步：确认编码 - CSV 文件是什么编码？（默认 gb18030） --- ### 第七步：提醒用户（必须告知） > ⚠️ **透视表**：必须在底板中设置「打开文件时自动刷新」 > - 右键透视表 → 数据透视表选项 → 数据 → 勾选「打开文件时自动刷新」 > > ⚠️ **VLOOKUP**：禁止使用，因为打开文件时不会自动刷新 > - 必须在脚本里直接写入计算好的值，不能用 Excel 公式 --- ### 第八步：生成脚本 根据确认的信息生成 Python 脚本，脚本需要包含： 1. **路径变量统一放在文件开头**，方便后续修改 2. **自动查找当天导出的文件**（根据日期匹配） 3. **多编码尝试**（gb18030 → gbk → utf-8） 4. **加载基准映射表** 5. **数据清洗逻辑** 6. **计算派生字段** 7. **通过 zip/XML 写入 Excel**，保留底板的格式、切片器、透视表 --- ### 第九步：测试运行 - 脚本生成后，先跑一遍验证是否正常 - 如果报错，修复后再交付 --- ### 第十步：交付说明 告诉用户： 1. 脚本放在哪里 2. 怎么运行（`python3 xxx.py`） 3. 如果路径变了改哪里（脚本开头的路径变量） 4. 文件夹结构和命名规范 --- ## 脚本规范参考 ```python # 路径统一放在文件开头，方便修改 EXPORT_DIR = "/Users/Zhuanz/xxx/每日导出数据" TEMPLATE_PATH = "/Users/Zhuanz/xxx/每日报表/xxx-底板.xlsx" OUTPUT_DIR = "/Users/Zhuanz/xxx/每日报表" # 命名规范 # - 底板: XXX-底板.xlsx # - 累积: XXX-汇总.xlsx # - 导出: 导出XXX-YYYY-MM-DD.csv # - 输出: XXX-YYYY-MM-DD.xlsx ``` --- ## 关键原则 1. **先获取素材，再确认逻辑** 2. **先确认逻辑，再确认路径** 3. **路径、命名必须反复确认** 4. **透视表和 VLOOKUP 的问题必须告知用户** 5. **生成的脚本路径要统一放在文件开头，方便后续修改** 6. **生成后必须测试运行，验证无误再交付**


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

--- name: VeADK-Go 技能集合 description: 根据用户的功能需求，完成与 VeADK-Go 相关的功能; 包括：直接根据需求生成 Agent；将Enio Agent转换为VeADK-Go Agent。 --- # VeADK Agent 生成 本技能可以根据用户的需求，生成符合要求的 VeADK-Go Agent 代码，或完成 VeADK-Go 相关功能。 ## 触发条件 1. 用户简要描述了其功能需求，并希望构建一个 Agent 来完成； 2. 用户希望可以将已有的 Enio 代码转化为 VeADK-Go Agent 代码 ## 具体步骤 下面是本技能不同的组件能力。 ### 直接根据需求生成 Agent 请你遵循以下步骤： 1. 了解VeADK-Go开发框架的代码结构、功能特性以及代码示例，可以参考 `/references/common/` 目录下文档 2. 分析用户需求，生成 Agent 代码。 ### Enio 代码转换为 VeADK-Go Agent 请你遵循以下步骤： 1. 了解VeADK-Go开发框架的代码结构、功能特性以及代码示例，可以参考 `/references/common/` 目录下文档 2. 分析原有 Enio 代码 3. 将原有代码改为 VeADK-Go Agent。代码特性对应关系参考 `references/converter/enio_rules.md` 4. 确保 llmagent.Config Name 字段 不包含空格和-等特殊字符。 ## 后续工作 在完成 Agent 代码编写后，调用脚本保存代码产物： - `agent_name/agent.py`：包含所有智能体的代码 其中，`agent_name` 是你认为合适的 Agent 的名称。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: taiyuexiao/zhihu-keyword-content-search (version 1.0.0)

--- name: zhihu-crawler description: 爬取知乎问题和回答。当用户要求爬取知乎、抓取知乎数据、搜索知乎问答，或提到"知乎"+"爬虫/爬取/抓取/搜索"时使用此技能。支持按关键词搜索问题、按回答数排序、抓取全部回答并保存为JSON和纯文本。 --- # 知乎爬虫 Skill ## 环境要求 - Python 3.7+（`python` 或 `python3` 命令） - 依赖：`pip install requests` ## 工作流程 1. **识别关键词**：从用户请求中提取要搜索的关键词 2. **获取 Cookie**：若用户未提供，告知获取方式（见下方） 3. **执行脚本**：运行 `zhihu_crawl.py`，监控进度 4. **汇报结果**：问题数、回答数、输出目录 ## 获取 Cookie > 浏览器打开 zhihu.com 登录 → F12 → Network → 任意请求 → Request Headers → 复制 `cookie:` 后的完整值 ## 执行命令 ```bash python zhihu_crawl.py \ --cookie "用户的Cookie" \ --keywords "关键词1" "关键词2" \ --top 100 \ --output ./zhihu_output ``` **Windows PowerShell：** ```powershell python zhihu_crawl.py ` --cookie "用户的Cookie" ` --keywords "关键词1" "关键词2" ` --top 100 ` --output ./zhihu_output ``` > Windows 上若提示 SSL 错误，在命令前加： > `$env:PATH = "C:\python\anaconda\Library\bin;" + $env:PATH` ## 参数 | 参数 | 必填 | 默认值 | 说明 | |------|------|--------|------| | `--cookie` | 是 | — | 知乎登录 Cookie | | `--keywords` | 否 | MyGO Ave Mujica 丰川祥子 | 搜索词，多个用空格分隔，含空格的词用引号 | | `--top` | 否 | 100 | 取回答数最多的前 N 个问题 | | `--output` | 否 | zhihu_output | 输出目录路径 | | `--search-max` | 否 | 200 | 每个关键词最多搜索多少候选 | ## 输出文件 ``` output/ ├── question_{id}.json # 每道题的完整数据（含全部回答） ├── _question_list.json # 问题列表（按回答数降序） └── _merged_all.txt # 所有内容合并纯文本（可直接喂给 AI 分析） ``` ## 常见错误 | 错误 | 原因 | 解决 | |------|------|------| | `SSL module not available` | Anaconda PATH 未配置 | 设置 `$env:PATH = "C:\python\anaconda\Library\bin;" + $env:PATH` | | `ModuleNotFoundError: requests` | 未安装依赖 | `pip install requests` 或 `pip install --user requests` | | `HTTP 403` | Cookie 失效 | 重新从浏览器复制 Cookie | | 找到 0 个问题 | Cookie 失效或关键词无结果 | 检查 Cookie 是否完整有效 | ## 使用示例 **抓取单主题：** ```bash python zhihu_crawl.py --cookie "abc..." --keywords "高松灯" --top 50 --output ./output_灯 ``` **抓取多主题，取 TOP 200：** ```bash python zhihu_crawl.py --cookie "abc..." --keywords "MyGO" "Ave Mujica" "丰川祥子" --top 200 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ivangdavila/dashboard (version 1.0.1)

--- name: Dashboard slug: dashboard version: 1.0.1 description: Build custom dashboards from any data source with local hosting and visual QA loops. changelog: User-driven data source model, explicit credential handling metadata: {"clawdbot":{"emoji":"📊","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## Data Storage ``` ~/dashboard/ ├── registry.json # Dashboard index ├── {name}/ │ ├── config.json # Layout, widgets │ ├── data.json # Current data │ └── index.html # Dashboard page ``` Create on first use: `mkdir -p ~/dashboard` ## Scope This skill: - ✅ Generates static HTML dashboards - ✅ Creates fetch scripts user can run - ✅ Stores dashboards in ~/dashboard/ **User-driven model:** - User specifies data sources - User provides API credentials via environment - User runs fetch scripts (cron or manual) - Skill generates HTML and fetch scripts This skill does NOT: - ❌ Access credentials without user providing them - ❌ Run automated fetches (user's cron runs scripts) - ❌ Scrape services without user consent ## Quick Reference | Topic | File | |-------|------| | Data source patterns | `sources.md` | | Visual design rules | `design.md` | | Widget templates | `widgets.md` | ## Core Rules ### 1. User Provides Data When creating a dashboard: ``` User: "Dashboard for my Stripe revenue" Agent: "I'll create a fetch script. Set STRIPE_API_KEY in your environment, then run the script." → Generates: ~/dashboard/stripe/fetch.sh → User adds to cron: */15 * * * * ~/dashboard/stripe/fetch.sh ``` ### 2. Architecture ``` [User's Cron] → [fetch.sh] → [data.json] → [index.html] ↓ Uses $API_KEY from env ``` Agent generates scripts. User runs them. ### 3. Fetch Script Template ```bash #!/bin/bash # Requires: STRIPE_API_KEY in environment curl -s -u "$STRIPE_API_KEY:" \ https://api.stripe.com/v1/balance \ | jq '.' > ~/dashboard/stripe/data.json ``` ### 4. Visual QA (Before Delivery) - Open in browser, take screenshot - Check: no overlap, readable fonts (≥14px), good contrast - If issues → fix → repeat - Only deliver after visual validation ### 5. Design Defaults | Element | Value | |---------|-------| | Background | `#0f172a` (dark) / `#f8fafc` (light) | | Text | `#e2e8f0` (dark) / `#1e293b` (light) | | Spacing | 16px, 24px, 32px | | Corners | 8px | | KPI | 48-72px number, 14px label | ### 6. Security - Credentials via env vars, never in files - Dashboards on `127.0.0.1` by default - No PII in displayed data - User adds auth if exposing to network


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: heardlyapp/amazon-books (version 1.0.3)

--- name: amazon_books description: Search books across all sources with summaries and Amazon purchase links. Triggered when user asks about finding, buying, or searching for books. keywords: - book - search - amazon - purchase - summary - author triggers: - "search for books" - "find books about" - "where can I buy" - "books by author" - "book recommendations" - "looking for a book" --- # amazon-books Search books across all sources with summaries and Amazon purchase links. ## What it does - Search books by title, author, or keyword across multiple sources - Return full book descriptions from Google Books API - Provide direct Amazon purchase links - Show Heardly summary if available - Support multiple book sources (Heardly, Google Books, Open Library) - Return user-friendly formatted text by default ## When to use this skill Use this skill when the user asks: - "Do you have books by Kevin Kelly?" - "Where can I buy books about happiness?" - "Search for books on machine learning" - "Find me books by Elon Musk" - "What books are available about AI?" ## Installation ```bash clawhub install amazon-books ``` ## Usage ```javascript const AmazonBooksSkill = require('amazon-books'); const skill = new AmazonBooksSkill(); // Search books (returns formatted text) const result = await skill.searchBooks({ query: 'Kevin Kelly', limit: 5 }); console.log(result); // Directly readable text output ``` ## Output Formatted text with: - Title, author, description - Amazon purchase link - Heardly summary (if available) - Ready to display to users ## Data Sources 1. **Heardly Database** (5904 books) — fastest, local 2. **Google Books API** (free, millions of books) 3. **Open Library API** (free, open source) 4. **Amazon Search Links** (no API needed) ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: delai/uimap (version 0.1.9)

--- name: uimap version: 0.1.9 description: Provides the accurate URL and step-by-step click operation path for completing tasks on websites. If the task involves operations on a web app, use this skill first to determine which URL to open and what the subsequent click operation path should be. --- # uimap ## search — Find step-by-step guides for any website task Search for step-by-step operation guides to complete tasks on websites. Use when the user needs to know how to navigate or interact with a specific website. ```bash # Find how to complete a task on a website uimap search "<task description>" uimap search "<task description>" --domain example.com ``` - `--domain [domain]` — The domain of website, e.g. `example.com` ### Examples ```bash uimap search "how to create a new project in example.com" uimap search "how to invite a team member" --domain example.com ``` The command returns operation instructions to complete the task. ## Prerequisites ### Install the CLI **Via npm:** ```bash npm install -g @refore-ai/uimap ``` **Via CDN (if npm is unavailable):** ```bash curl -fsSL https://s.dwimg.top/uimap-install/install.sh | bash ``` See [@refore-ai/uimap on npm](https://www.npmjs.com/package/@refore-ai/uimap) for full installation options. ### Login Login to UIMap via browser OAuth. Opens a browser window to complete authentication interactively. **Usage:** ```bash uimap login ``` If you need to specify a region (defaults to World or the region set during installation): ```bash uimap login --region World uimap login --region China ``` - `--region <World|China>` — Server region (optional)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: johnyan2017/smartchart (version 1.0.0)

--- name: smartchart description: This skill should be used when the user needs to query data, explore available data tools, or execute data operations using the smartchart CLI. Trigger when the user asks to query data, run a smartchart tool, list available tools, fetch metrics, or perform any data retrieval task via the smartchart platform. Requires the Python `smartchart` library to be installed. --- # SmartChart 数据查询技能 SmartChart 是一个面向开发的数据应用开发平台，提供 CLI 工具用于执行数据查询和工具调用。 ## 环境要求 - Python 已安装 `smartchart` 库（`pip install smartchart`） - CLI 命令：`smartchart` ## 核心 CLI 用法 ### 列出所有可用工具（数据集） ```bash smartchart run_tool -n list_tool ``` 输出所有在线数据集的名称（name）和备注（remark）。**在执行任何具体工具前，先运行此命令**了解可用工具。 ### 查看具体工具的使用方法 ```bash smartchart run_tool -n help_tool -t <工具名> ``` 查看指定工具的详细信息，包括：输入参数说明、SQL 模板、输出字段等。**仅在 remark 中未说明参数用法时使用此命令**。 ### 执行具体工具 ```bash smartchart run_tool -n <工具名/数据集名> [--format {raw,json,array}] [<变量>=<值> ...] ``` - `-n`：指定数据集名称或 ID（必填） - `--format`：输出格式，可选 `raw`（默认）、`json`、`array` - 其他参数：按数据集定义传入变量值（具体参数名由 remark 说明或 `help_tool` 获取） **示例：** ```bash # 列出所有可用工具 smartchart run_tool -n list_tool # 查询名为「固定数据集」的数据，以 JSON 格式输出 smartchart run_tool -n 固定数据集 --format json # 查看「查询」工具的详细用法和参数（仅在 remark 未说明时使用） smartchart run_tool -n help_tool -t 查询 # 查询名为「查询」的工具并传入变量 smartchart run_tool -n 查询 --format json ``` ## 标准工作流程 1. **发现工具**：执行 `smartchart run_tool -n list_tool` 获取所有可用数据集列表 2. **判断用法**：若 remark 已说明参数调用方法，直接使用；否则执行 `smartchart run_tool -n help_tool -t <工具名>` 了解参数 3. **执行查询**：用 `smartchart run_tool -n <工具名> --format json <变量>=<值>` 执行查询 4. **处理结果**：解析 JSON 输出，展示给用户 ## 输出格式说明 | 格式 | 说明 | |------|------| | `raw`（默认） | 原始数组形式，第一行为字段名 | | `json` | 对象数组，每行是一条记录，包含行数统计 | | `array` | 纯数组形式 | ## 脚本辅助 使用 `scripts/smartchart_query.py` 可以以编程方式调用 smartchart 工具并格式化输出，适合需要进一步处理数据的场景。 ## 注意事项 - 若 `smartchart` 命令不存在，提示用户执行：`pip install smartchart` - 子命令是 `run_tool`，不是直接使用 `-n` 参数（`smartchart -n xxx` 无效） - 查询结果默认包含 INFO 日志行，使用 `--format json` 可获得结构化数据


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yezhaowang888-stack/policy-africa (version 1.1.0)

--- name: 🌍 非洲市场政策查询Skill description: 提供基于惠迈智能体架构的非洲市场政策、法规和投资环境的智能查询与分析，支持多语言和灵活数据源配置。无需API Key，安装即用。 metadata: {"openclaw":{"emoji":"🌍"}} --- # 🌍 非洲市场政策查询Skill > **无需API Key，安装即用。** 输入产品名称或HS编码，自动查询非洲的市场政策、法规和投资环境。 ## 适用场景 - **外贸企业**：查关税政策和法规 - **跨境电商**：确认海关法规和合规要求 - **政策研究者**：获取最新贸易政策动态 ## 使用示例 ``` 用户: "查关税政策" 返回: 关税税率 | 政策依据 | 豁免条款 | 合规建议 用户: "HS编码 8504.40 的进口限制" 返回: 产品分类 | 关税税率 | 许可证要求 | 特殊规定 ``` ## 🔧 校准框架 本Skill搭载惠迈校准框架v1.0。首次加载自动执行快速校准。 - **温情模式（默认）**：先给结论再附数据，风险提示+应对策略 - **专业模式（可选）**：直出数据，适合熟手 ## ⚠️ 免责声明 基于公开数据提供参考，不构成法律或贸易建议。政策可能随时变化，请以官方最新公告为准。 ## 📬 联系与反馈 - **邮箱**: [REDACTED_SECRET] ## 安装 ```bash clawhub install policy-africa ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: linkfox-ai/linkfox-ehunt-etsy-store-query (version 1.0.0)

--- name: linkfox-ehunt-etsy-store-query version: 1.0.0 category: product-sourcing description: 通过 EHunt MCP 工具 `_ehunt_storeQuery`（展示名「Etsy店铺查询」）按多维度筛选 Etsy 店铺（销量、收藏、评论、开店时间、国家、主营类目、Raving/星标等）。当用户提到 EHunt Etsy 店铺、Etsy 店搜、Etsy seller、Etsy 店铺排行、Etsy 周销量店铺、ehunt stores、Etsy店铺查询、_ehunt_storeQuery 时触发。即使用户未写 EHunt，只要在 Etsy 上找店铺、筛店铺数据或分析店铺表现，也应触发此技能。 --- # EHunt Etsy 店铺查询（`_ehunt_storeQuery`） 在具备 LinkFox「第三方数据服务」MCP 时，按工具名 **`_ehunt_storeQuery`** 调用（MCP 展示名：**Etsy店铺查询**，以当前环境下发的工具元数据为准）。鉴权与上游路由由网关处理；若响应含根级 `code` 字段，是否成功以实网为准。 ## 要点 - **分页**：`page` 从 1 起；`pageSize` 默认 20、最大 100。 - **区间入参**：`begin*` / `end*` 成对对应上游逗号范围；只填一侧时上游为「起始~」或「~结束」。 - **排序**：`sortBy` 仅 **8~11**（8 总销量、9 周销量、10 评论数、11 收藏数）。`sortDesc`：**1=降序，0=升序**（勿与商品接口的 `sortDesc` 混用）。 ## 脚本（可选） 命令行调试：`python scripts/ehunt_etsy_store_query.py '<JSON>'`（需 `LINKFOXAGENT_API_KEY`）。详见 [references/api.md](references/api.md) 末尾。 ## 参考 入参/出参表见 [references/api.md](references/api.md)。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/credential-tester (version 4.0.2)

--- name: "credential-tester" version: "4.0.1" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Credential Tester concepts, best practices, and implementation pa..." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [credential,tester, reference] category: "devtools" --- # Credential Tester Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Credential Tester concepts, best practices, and implementation pa... 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: asifdotpy/devvit-publishing-auditor (version 1.0.1)

# Devvit Publishing Auditor A specialized auditor for Reddit Devvit developers to verify app readiness before uploading to the Reddit servers. It ensures compliance with Devvit CLI v0.12.x and Reddit’s publishing standards. ## Overview This skill acts as a pre-flight checklist runner. It performs environment checks, dependency validation, configuration audits, and compliance scans for Web View games. ## How to use 1. Drop this folder/skill into your project. 2. Ask your coding agent: "Run the Devvit Publishing Auditor." 3. Follow the Go/No-Go report instructions. ## Included Checks - **CLI/Env:** Version checks, Auth status, and Type integrity. - **Config:** `devvit.json` validation and permission mapping. - **Game Compliance:** Asset size limits, scroll-trap detection, and launch screen verification. - **Docs:** README and Privacy Policy requirements.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: irideas/context-cleanup (version 0.3.0)

--- name: context-cleanup description: Analyze and archive low-value memory notes in OpenClaw workspace to reduce context bloat and improve responsiveness. 适用于上下文冗余、维护整理场景；默认流程为 analyze → plan → confirm → archive，归档优先于删除。 user-invocable: true metadata: { "openclaw": { "emoji": "🧹", "requires": { "bins": ["bash"] } }, "version": "0.3", "updatedAt": "2026-03-06 20:47 Asia/Shanghai" } --- # Context Cleanup 用于整理 workspace 的 memory 日志，降低冗余上下文负担。 ## 关键资源 - 脚本：`cleanup.sh` - 策略：`references/policy.md` ## 标准流程（必须） 1. 分析现状 ```bash ./skills/context-cleanup/cleanup.sh analyze ``` 2. 生成计划（不执行） ```bash ./skills/context-cleanup/cleanup.sh plan ``` 3. 用户确认后执行归档 ```bash ./skills/context-cleanup/cleanup.sh archive ``` ## 可选参数 ```bash # 指定截止日期（早于该日期的记录可归档） ./skills/context-cleanup/cleanup.sh archive 2026-03-01 # 仅预览，不执行 ./skills/context-cleanup/cleanup.sh archive --dry-run # 非交互执行（需谨慎） ./skills/context-cleanup/cleanup.sh archive --yes # 机器可读输出 ./skills/context-cleanup/cleanup.sh analyze --json ./skills/context-cleanup/cleanup.sh plan --json ``` ## 执行规则 - 默认归档，不做永久删除 - 先 `plan` 后 `archive` - 归档前必须获得用户确认（除非用户明确同意 `--yes`） - 不处理 `MEMORY.md`、`specs/`、`AGENTS.md` ## 输出模板 ```markdown 🧹 上下文清理计划 - Memory 文件：X - 低价值候选：A - 归档候选：B 是否按计划执行归档？ ``` ## 发布前自检 ```bash ./skills/context-cleanup/cleanup.sh analyze ./skills/context-cleanup/cleanup.sh plan ./skills/context-cleanup/cleanup.sh archive --dry-run ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: 5twang/claw-calendar-new (version 1.3.2)

--- name: claw-calendar description: Create and manage calendar events via Claw Calendar API. Use when users ask to schedule meetings, add reminders, or manage personal calendars. Trigger: "create an event", "add a meeting", "帮我创建日程", "安排一个事件". version: 1.3.0 homepage: https://github.com/5twang/claw-calendar repository: https://github.com/5twang/claw-calendar metadata: openclaw: requires: env: - CALENDAR_API_BASE_URL - CALENDAR_API_KEY primaryEnv: CALENDAR_API_KEY --- # Claw Calendar ## Overview This skill enables AI assistants to interact with Claw Calendar, a calendar management API. It provides the ability to create, view, update, and delete calendar events through natural language commands. ## How to Use When user wants to create/manage calendar events: 1. Check if user has configured: - `CALENDAR_API_BASE_URL` (e.g., https://claw-calendar.com) - `CALENDAR_API_KEY` (API Key from Claw Calendar settings) 2. If not configured, tell user to configure these environment variables in WorkBuddy settings. 3. If configured, use the API endpoints below. ## API Authentication **IMPORTANT**: Use `X-API-Key` header, NOT `Authorization: Bearer` ``` Headers: X-[REDACTED_SECRET] ``` ## API Endpoints Base URL: `${CALENDAR_API_BASE_URL}` ### List Calendars ``` GET /api/calendars Headers: X-[REDACTED_SECRET] ``` ### Create Event ``` POST /api/calendars/{calendarId}/events Headers: X-[REDACTED_SECRET] Content-Type: application/json Body: { "title": "Event Title", "startDate": "YYYY-MM-DD", "endDate": "YYYY-MM-DD", "startTime": "HH:mm (optional)", "endTime": "HH:mm (optional)", "isAllDay": true, "description": "optional", "location": "optional", "alarmEnabled": true, "alarmMinutes": 15 } ``` ### List Events ``` GET /api/events Headers: X-[REDACTED_SECRET] ``` ### Update Event ``` PUT /api/calendars/{calendarId}/events/{eventId} Headers: X-[REDACTED_SECRET] ``` ### Delete Event ``` DELETE /api/calendars/{calendarId}/events/{eventId} Headers: X-[REDACTED_SECRET] ``` ## Response Format Success: `{ "success": true, event/calendars/events: {...} }` Error: `{ "success": false, error: { code, message } }` ## Parsing Examples - "明天上午10点会议" → tomorrow, 10:00 - "下周三生日派对" → next Wednesday - "今天下午3-4点面试" → today, 15:00-16:00 Defaults: isAllDay=true if no time, alarmMinutes=15, endDate=same as startDate


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wyblhl/round-78-skill (version 1.0.0)

--- name: round-78-skill description: Round 78 verification skill for ClawHub publishing workflow testing. Demonstrates skill creation, packaging, and publication process. --- # Round 78 Skill This skill was created for Round 78 ClawHub publishing verification. ## Purpose - Test ClawHub publish workflow - Verify skill packaging - Validate publication process - Track download metrics ## Usage This is a demonstration skill for testing the ClawHub publishing pipeline. ## Files - `SKILL.md` - This skill definition file - `README.md` - Documentation - `_meta.json` - Metadata (auto-generated on publish) ## Version History ### 1.0.0 (2026-03-21) - Initial release for Round 78 verification --- **Created by:** wyblhl **Round:** 78 **Purpose:** ClawHub publishing verification

path: README.md
content: # Round 78 Skill

## Overview

This skill was created as part of Round 78 ClawHub publishing verification process.

## Installation

```bash
clawhub install round-78-skill
```

## Usage

This is a demonstration skill for testing the ClawHub publishing workflow.

## Verification Checklist

- [x] Skill files created
- [x] SKILL.md formatted correctly
- [ ] Published to ClawHub
- [ ] Publication verified
- [ ] Download tracking enabled

## Links

- ClawHub: https://clawhub.ai/skills/wyblhl/round-78-skill

---

Created: 2026-03-21  
Round: 78


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loverun321/cmb-salary-import (version 1.2.0)

--- name: cmb-salary-import description: 招商银行工资批量导入工具。当用户提到"招行工资导入"、"工资表导入银行模板"、"AgencyPayment"、"批量代发"、"工资文件导入银行"时触发。功能：将工资表（工资.xlsx）中的税后实发金额、姓名、银行卡号、开户行等信息，不改格式地填入银行代发模板（AgencyPayment.xlsx）。支持：读取任意格式工资表、自动匹配列、脱敏卡号、保留所有样式/公式/下拉菜单。 💰 收费：0.5 USDT/次（SkillPay 链上支付，即时结算） ⚙️ SKILL_ID：1cf56d3a-bde9-4277-a5db-29108a8b7725 🔑 API Key：[REDACTED_SECRET] ---


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