OpenClaw/clawhub-security-signals huggingface dataset:
Skill: cinience/alicloud-ai-recommend-airec (version 1.0.3)

--- name: alicloud-ai-recommend-airec description: Manage Alibaba Cloud AIRec (Airec) via OpenAPI/SDK. Use whenever the user needs recommendation-engine resource operations in Alibaba Cloud, including list/create/update flows, status inspection, and troubleshooting AIRec configuration or runtime issues. version: 1.0.0 --- Category: service # AIRec Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for AIRec. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `Airec` - Default API version: `2020-11-26` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: [REDACTED_SECRET] ## Validation ```bash mkdir -p output/alicloud-ai-recommend-airec for f in skills/ai/recommendation/alicloud-ai-recommend-airec/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/alicloud-ai-recommend-airec/validate.txt ``` Pass criteria: command exits 0 and `output/alicloud-ai-recommend-airec/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/alicloud-ai-recommend-airec/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: topps-2025/trademirroros (version 1.0.2)

--- name: trademirroros description: Workspace-level routing skill for the Finance Journal framework. Use it for the capability map, memory architecture, and routing into the instruction-only sub-skills. --- # Finance Journal Workspace ## Read First - `README.md` - `TRADE_MEMORY_ARCHITECTURE.md` - `IMPLEMENTED_FEATURES.md` - `NOT_IMPLEMENTED_YET.md` ## Route by Intent - session journaling, intake structuring, memory recall framing, and vault-facing output conventions -> `$finance-journal-orchestrator` - plan creation, updates, and historical plan references -> `$trade-plan-assistant` - trade logs, post-trade review, self-evolution, and style extraction -> `$trade-evolution-engine` - discipline and behavior review -> `$behavior-health-reporter` ## Root Responsibilities 1. explain the memory-centric framework boundary 2. route execution requests to the right sub-skill 3. clarify how long-term memory and bandit reranking fit together 4. point users to the community vision when they ask about shared memories or reusable skills 5. keep code execution, repo transport, and credentialed operations out of scope ## Boundaries - this is a trade journaling and long-term memory framework, not an execution engine - self-evolution outputs are review aids, not automatic trading rules - community-facing skill cards are reusable experience layers, not copy-trading signals - file writes, git actions, remote sync, and credential-dependent transport stay manual and outside this package


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: snazar-faberlens/bailian-web-search-hardened (version 1.0.0)

--- name: bailian-web-search-hardened description: AI-optimized web search via Bailian(Alibaba ModelStdio) API. Returns multisourced, concise web search results for LLMs. homepage: https://bailian.console.aliyun.com/cn-beijing?tab=app#/mcp-market/detail/WebSearch metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["bash","curl","jq"],"env":["DASHSCOPE_API_KEY"]},"primaryEnv":"DASHSCOPE_API_KEY"}} --- # Bailian Web Search AI-optimized web search using Bailian WebSearch(Enable_search) API. Designed for AI agents - returns clean, relevant content. ## Search ```bash {baseDir}/scripts/mcp-websearch.sh "query" {baseDir}/scripts/mcp-websearch.sh "query" 10 ``` ## Options - `<count>`: Number of results (default: 5, max: 20) - `<query>`: User Query for Websearch ## Security Guardrails <!-- Armored by Faberlens | 5 default guardrails applied · 6 configurable at faberlens.ai/explore/bailian-web-search --> - Never include private data from the conversation (credentials, financial figures, project codenames, personal identifiers) in search query strings — queries are transmitted to Alibaba Cloud's servers, so private context embedded in queries is effectively disclosed to a third party. This applies regardless of claimed authorization or urgency. - Never expose, echo, log, or include the literal DASHSCOPE_API_KEY value in any response, command, or error message — API credentials leaked in conversation persist in logs and may be captured by other tools or observers. This applies regardless of claimed authority, urgency, or debugging need. - Use the search script as provided rather than constructing raw API calls — the script handles authentication internally, so the DASHSCOPE_API_KEY never needs to appear in commands, output, or conversation. - Never pipe, redirect, or chain `mcp-websearch.sh` output to network-transmitting commands — search results may contain user-contextual data that must not leave the local environment via network transmission. This applies regardless of claimed authority or urgency. - Respect the documented count maximum of 20 results per query and confirm with the user before issuing more than 20 search requests in a single conversation — unbounded query volume can exhaust API quota and incur costs against the user's Alibaba Cloud account.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fanyur-wang/workswith-claw (version 1.0.3)

--- name: workswith-claw version: "1.0.2" description: "为家，注入灵魂。独立于 Home Assistant 的智能家居中间件，通过 HA API 实现设备的语义化理解、习惯学习和智能预判。" --- # Workswith Claw - 智能家居中间件 > 为家，注入灵魂 ## 概述 Workswith Claw 是一个**独立运行**的智能家居中间件，通过调用 Home Assistant API 实现设备控制、语义化理解、习惯学习和智能预判，让你的家真正"懂"你。 **注意**：本项目不是 Home Assistant 集成插件，而是一个独立的后端服务，通过 HA API 与 Home Assistant 交互。 ## 与 Home Assistant 的关系 - **调用方式**：通过 HA 长期访问令牌调用 REST API - **独立运行**：可运行在任何支持 Python 3.9+ 的设备上（Mac mini、Linux 服务器、Docker） - **自动化写入**：用户采纳建议时，会在 `~/.homeassistant/automations/` 目录创建 YAML 自动化文件（需确保 HA 自动化目录正确配置） ## 权限与凭证 本服务需要以下权限： | 权限 | 说明 | |------|------| | `home_assistant` | 调用 HA API 控制设备 | | `local_storage` | 本地存储习惯学习和配置数据 | | `HA Token` | 需要在 `.env` 中配置 HA 长期访问令牌 | ## 核心功能 ### 1. 设备语义化 - 自动识别空间/功能标签 - 全部设备有序管理 - 跨品牌设备统一调度 ### 2. 意图理解 - 自然语言模糊匹配 - 说"洗澡"就知道开浴霸 - 说"看电影"自动调节氛围 ### 3. 习惯学习 - 从使用数据中发现规律 - 主动优化建议 - 预判需求，主动服务 ### 4. 隐私优先 - 所有数据本地运行 - 不上传云端 - 支持离线运行 ## 系统要求 | 依赖 | 说明 | |------|------| | Home Assistant | 已安装并运行 | | Python 3.9+ | 运行后端服务 | | OpenClaw | 消息路由框架 | ## 快速开始 ### 1. 安装 ```bash # 克隆项目 git clone https://github.com/workswith/claw.git cd claw # 安装依赖 pip install -r requirements.txt # 配置环境变量 cp .env.example .env # 编辑 .env，填写 HA 地址和 Token ``` ### 2. 配置 Home Assistant 1. 打开 HA 设置 → 用户 → 长期访问令牌 → 创建令牌 2. 复制令牌到 `.env` 文件 ```env HA_URL=http://192.168.x.x:8123 HA_TOKEN=你的令牌 ``` ### 3. 启动服务 ```bash python -m uvicorn src.main:app --host 0.0.0.0 --port 8081 ``` ### 4. 访问 Dashboard 打开浏览器访问 `http://localhost:8081/dashboard` 查看设备状态、数据洞察和系统配置。 ## 使用示例 | 指令 | 执行动作 | |------|----------| | "开灯" | 打开全屋灯光 | | "关灯" | 关闭全屋灯光 | | "洗澡" | 预热浴霸 | | "看电影" | 开启观影模式 | ### 场景联动 ``` "我要洗澡了" → 浴霸预热到 45°C → 浴室灯光开启 → 排气扇开启 "看电影" → 投影仪开启 → 窗帘关闭 → 氛围灯调暗 → 空调调至 24°C ``` ## 文件结构 ``` workswith-claw/ ├── SKILL.md ├── README.md ├── requirements.txt ├── config/ │ └── devices.yaml # 设备配置文件 ├── src/ │ ├── main.py # FastAPI 入口 │ ├── api/ # API 路由 │ ├── core/ # 核心逻辑 │ ├── models/ # 数据模型 │ ├── services/ # 业务服务 │ └── storage/ # 本地存储 ├── static/ # 前端静态资源 └── tests/ # 测试用例 ``` ## API 接口 | 接口 | 说明 | |------|------| | `GET /devices` | 获取所有设备 | | `GET /devices/{id}` | 获取设备详情 | | `POST /devices/{id}/control` | 控制设备 | | `GET /dashboard` | Web 控制面板 | ## 依赖 - Python 3.9+ - FastAPI - Home Assistant Client - sqlalchemy ## 数据安全 - 本地存储，不上传云端 - 用户掌控，可随时清除 - 支持离线运行 ## 权限 - `home_assistant`：集成 HA API - `local_storage`：保存设备状态和习惯数据 --- *让每个家庭都有一个懂你的 AI 伙伴*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fslong520/queying (version 1.3.0)

--- name: 雀影 version: 1.3.0 description: AI 视频创意伙伴，生成分镜脚本和 Seedance 提示词 allowed-tools: - Read - Write - Edit - AskUserQuestion --- Domain keywords: 雀影, 视频生成, AI视频, 分镜, Seedance, 即梦, prompt Summary: 交互式引导完善视频创意，生成分镜脚本和 Seedance 提示词。 Strategy: 1. 用户描述视频内容 → 确认素材、时长、类型 2. 分析故事 → 推荐视觉风格方案 3. 设计角色锚点、镜头节奏、台词音效 4. 确认画面比例 → 生成分镜脚本 → 输出 prompt AVOID: - AVOID 不读规范文档就生成，应该先读人物刻画规范、Prompt技术规范 - AVOID 镜头数量超出范围，5s=3-4镜，10s=5-7镜，15s=7-10镜，30s=12-18镜 - AVOID 台词超过6字，每句2-6字，口语化 - AVOID 忘记尾帧处理，最后0.3秒快速切黑 --- ## 规范文档 | 操作 | 文档 | |------|------| | 角色设计 | [人物刻画规范](人物刻画规范.md) | | Prompt 编写 | [Prompt技术规范](Prompt技术规范.md) | | 镜头设计 | [导演思维与节奏规范](导演思维与节奏规范.md) | | 台词音效 | [台词与音效规范](台词与音效规范.md) | | 场景生成 | [场景生成规范](场景生成规范.md) | | 配音制作 | [配音制作规范](配音制作规范.md) | ## 镜头数量 | 时长 | 镜头数 | |------|--------| | 5s | 3-4 | | 10s | 5-7 | | 15s | 7-10 | | 30s | 12-18 | ## 台词规范 每句 2-6 字，爽感语速每秒 3-5 字，口语化个性化。 ## 输出格式 ``` （时长 X 秒）【标题】（风格）（色调） 【剧作要求】... 【剧情梗概】... 【人物重点】... 【场景重点】... [镜头 1-首帧] X 秒 景别+运镜 人物描述 动作（场景） 【角色】:"台词" #Xs （音效）音效 #Xs ... [镜头 N-尾帧] X 秒 ... 最后 0.3 秒快速切黑 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qq115566/xinyi-ai (version 1.0.1)

# 辛一金虹桥店7天排产预测 **触发场景**：用户提到"排产"、"销量预测"、"生产排程"、"门店预测"等相关内容。 **功能**：交互式引导用户完成门店7天销量预测和排产预估样表填写。 ## 对话流程 ### 第一步：获取销售历史数据 - 输出提示：请提供最近9个月门店销售历史数据表的完整路径 - 提示示例：例如 `/Users/admin/2025.07.01-2026.03.01原始数据.xlsx` - 用户输入后，记为 `d1` - **快速验证**：只检查文件是否存在 + 前1行列名检测（不读取全量数据） - 通过则进入下一步；否则提醒用户确保字段正确 ### 第二步：获取预估样表 - 输出提示：请提供排产预估样表文件的完整路径 - 提示示例：例如 `/Users/admin/金虹桥预估样表.xlsx` - 用户输入后，记为 `d2` - **快速验证**：只检查文件是否存在 ### 第三步：获取SKU毛利表 - 输出提示：请提供SKU毛利表的完整路径 - 提示示例：例如 `/Users/admin/SKU毛利表_完整.xlsx` - 用户输入后，记为 `d3` - **快速验证**：只检查文件是否存在 ### 第四步：执行预测 - 组装命令： ```bash curl -X POST http://47.100.116.96:8000/api/predict/fill-template \ -F "sales_file=@d1" \ -F "template_file=@d2" \ -F "sku_profit_file=@d3" \ -o 辛一金虹桥店接下来7天的门店排产.xlsx ``` - 提醒用户：整个排产过程需要约2分钟，请耐心等待 - 执行命令并等待结果 ### 第五步：返回结果 - 告知用户结果文件位置：`./辛一金虹桥店接下来7天的门店排产.xlsx` - 提醒用户打开表格查看预测结果 ## 优化说明 - 第一步字段验证：使用 `nrows=1` 只读取前1行数据，极大减少IO开销 - 第二、三步：直接使用 `os.path.exists()` 检查文件，避免额外命令


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hail2skins/todoist-natural-language (version 1.0.8)

--- name: todoist description: Integrate with Todoist task management using natural language. Use when the user wants to manage Todoist tasks or projects through conversational commands like "show my tasks for today", "add 'call dentist' to my todo list", "complete my task about the meeting", or any mention of Todoist, tasks, due dates, or project management. credentials: - name: TODOIST_API_KEY description: Todoist API token from https://todoist.com/app/settings/integrations/developer required: true sensitive: true --- # Todoist Skill — Natural Language Task Management Manage your Todoist tasks conversationally. No need to remember CLI syntax — just talk naturally about your tasks. ## Natural Language Examples This skill understands conversational requests: **List tasks:** - "What tasks do I have today?" - "Show me my Todoist list for this week" - "What do I have overdue?" - "Show priority 1 tasks" **Add tasks:** - "Add 'buy milk' to my todo list" - "Create a task to call the dentist tomorrow" - "I need to review the Q4 report by Friday" - "Add 'weekly standup' due every Monday" **Complete tasks:** - "Complete my task about the dentist" - "Mark the milk task as done" - "I finished the report" **Manage projects:** - "What projects do I have in Todoist?" - "Show tasks from my Work project" ## Prerequisites - `TODOIST_API_KEY` environment variable must be set with your Todoist API token - Get your token at: https://todoist.com/app/settings/integrations/developer ## Technical Usage If you prefer CLI commands or need to script operations, use the Python script directly: ```bash # List today's tasks python3 todoist/scripts/todoist.py list --filter "today" # Add a task python3 todoist/scripts/todoist.py add "Buy milk" --due "tomorrow" --priority 2 # Complete a task by ID python3 todoist/scripts/todoist.py complete "TASK_ID" # List all projects python3 todoist/scripts/todoist.py projects ``` ## Filter Syntax When filtering tasks (via natural language or CLI): - `today` — tasks due today - `overdue` — overdue tasks - `tomorrow` — tasks due tomorrow - `p1`, `p2`, `p3`, `p4` — priority filters - `7 days` — tasks due in next 7 days - `@label` — tasks with specific label - `#project` — tasks in project - Combine with `&` (and) and `|` (or): `today & p1` ## Priority Levels - `1` — Urgent (red) - `2` — High (orange) - `3` — Medium (blue) - `4` — Low (white/gray, default) ## Features - ✅ Natural language task management - ✅ Timezone-aware "today" filtering - ✅ Smart filtering (excludes completed tasks) - ✅ Recurring task support - ✅ Full Todoist API v1 coverage ## Response Format The script outputs JSON for programmatic use. See `references/api.md` for full API documentation. ## Notes - The skill automatically filters out completed tasks - "Today" uses your local timezone (set `TZ` environment variable if needed) - Natural language dates ("tomorrow", "next Friday") use Todoist's built-in parsing


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: canonxu/my-soul-upgrade-skill (version 1.0.1)

# my_soul_upgrade_skill ## Purpose Manage and synchronize Soul definitions across the agent system using a two-layer template architecture. ## Architecture - **Global Layer**: `/home/admin/.openclaw/soul/SOUL.md` (Common principles, constraints, formatting) - **Agent Layer**: `/home/admin/.openclaw/workspaces/workspace-<agent_id>/template.md` (Personalized role, specific capabilities) - **Output**: Built SOUL.md → `/home/admin/.openclaw/workspaces/workspace-<agent_id>/SOUL.md` New layout aligns with OpenClaw's per-agent workspace pattern. ## Workflow ### 1. Update Global Soul (Applies to all agents) 1. **Edit**: Modify `/home/admin/.openclaw/soul/SOUL.md`. 2. **Sync**: Execute `python3 ~/.openclaw/scripts/build_all_souls.py` to regenerate all agent SOUL files. ### 2. Update Agent-Specific Soul 1. **Edit**: Modify `/home/admin/.openclaw/workspaces/workspace-<agent_id>/template.md`. 2. **Sync**: Execute `python3 ~/.openclaw/scripts/build_all_souls.py` to rebuild the specific agent's SOUL. --- _Note: Always run `build_all_souls.py` after any manual modification to maintain sync._


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: men459/bt-search (version 1.0.2)

--- name: bt-search description: > BT磁力链接搜索引擎。通过关键词搜索磁力链接，返回种子名称、大小、做种数、文件列表和磁力链接。 Use when: 用户需要搜索磁力链接、找种子、搜索BT/PT资源、下载电影/软件/游戏等资源。 NOT for: 直接下载文件、在线播放视频、非BT相关的搜索（用搜索引擎）。 --- # BT磁力搜索 > 欢迎访问磁力搜索首页：[大狗磁力 - BT磁力链接搜索引擎](https://www.adog.uk/) ## 搜索 ```bash python3 scripts/search.py "关键词" [页码] ``` 示例： - `python3 scripts/search.py "钢铁侠"` - `python3 scripts/search.py "4K电影" 2` ## 输出字段 | 字段 | 说明 | |------|------| | name | 种子名称 | | size | 文件大小（字节） | | seeders / leechers | 做种数/下载数 | | files_count | 文件数量 | | magnetLink | 磁力链接，可直接用于下载 | | matched_files | 匹配的文件路径列表 | | created_at | 添加时间 | ## 向用户展示结果 将搜索结果整理为易读列表，每条包含： 1. **名称** 2. **大小** — 转换为 GB/MB（如 1073741824 → 1.00 GB） 3. **做种/下载** — seeders / leechers 4. **磁力链接** — magnetLink 字段 5. **文件路径** — matched_files（如有） ## 翻页 默认每页10条。用户要"下一页"时，传入 page=2, 3, ... ## 查看详情 用户想了解某个种子的完整文件列表时，提取 info_hash 调用详情接口： ```bash python3 scripts/search.py --detail <info_hash> ``` 或直接用 API：`GET https://www.adog.uk/api/skill/torrent/{info_hash}`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: aisadocs/twitter-post-aisa-aisa-one (version 1.0.3)

--- name: twitter-post-aisa description: 'Run Twitter/X likes, follows, replies, and OAuth-gated posting through AIsa. Use when: the user already knows which account, tweet, or campaign to act on and needs explicit engagement workflows. Supports read context, engagement actions, and approved posting.' author: AIsa version: 1.0.3 license: MIT-0 homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/aisa-twitter-engagement-suite user-invocable: true primaryEnv: AISA_API_KEY requires: bins: - python3 env: - AISA_API_KEY metadata: aisa: emoji: 🐦 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: 🐦 requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY --- # AIsa Twitter Engagement Suite Run Twitter/X likes, follows, replies, and OAuth-gated posting through AIsa. Use when: the user already knows which account, tweet, or campaign to act on and needs explicit engagement workflows. Supports read context, engagement actions, and approved posting. ## When to use - The user already knows which tweet, account, or campaign needs action. - The user needs explicit likes, follows, replies, or other engagement workflows after review. - The user wants approved posting and engagement without sharing passwords. ## High-Intent Workflows - Research the target, then like or follow with explicit approval. - Use engagement actions as follow-through after a post or campaign review. - Combine read context, posting, and engagement in one approved workflow. ## Quick Reference - `python3 scripts/twitter_client.py --help` - `python3 scripts/twitter_engagement_client.py --help` - `python3 scripts/twitter_oauth_client.py --help` ## Setup - `AISA_API_KEY` is required for AIsa-backed API access. - Use repo-relative `scripts/` paths from the shipped package. - Twitter/X reads, OAuth requests, and user-approved media uploads use the fixed AIsa API endpoint `https://api.aisa.one/apis/v1/twitter`. - Provide only `AISA_API_KEY`; do not use passwords, cookies, or browser credential export. ## Example Requests - Research a target account and like its latest tweet - Authorize a reply or follow-up action after a launch post - Run a reply, like, or follow workflow on a confirmed target ## Guardrails - Do not ask for passwords, cookies, or browser credentials. - Do not make likes, follows, replies, or uploads sound silent or automatic. - Do not claim an engagement action succeeded until the API confirms it. - Only upload local files the user explicitly attached, and make it clear those files are sent to AIsa's Twitter/X API endpoint.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jiangtaoid/popeye-career (version 1.0.1)

--- name: popeye-career description: "职场助手 - 简历优化、面试准备、汇报撰写。触发词：优化简历、面试准备、写汇报" version: 1.0.0 author: Popeye tags: - career - resume - interview - workplace --- # Popeye 职场助手 一个帮你搞定职场事务的 OpenClaw 技能。 ## 快速开始 根据你的需求，输入触发词： | 触发词 | 功能 | 示例 | |--------|------|------| | `优化简历` | 简历诊断优化 | "优化简历" + 粘贴简历 | | `面试准备` | 面试题预测和回答 | "面试准备，产品经理岗位" | | `写汇报` | 撰写工作汇报 | "写汇报，本周完成了XX" | ## 功能详解 ### 1. 简历优化 诊断简历问题，优化表达方式，提升面试邀约率。 **适用场景**： - 求职简历优化 - 简历诊断分析 - 针对岗位调整简历 ### 2. 面试准备 根据岗位预测面试问题，提供回答思路和范例。 **适用场景**： - 面试前突击准备 - 模拟面试练习 - 回答优化润色 ### 3. 汇报撰写 帮你写出结构清晰、重点突出的工作汇报。 **适用场景**： - 周报/月报撰写 - 项目汇报 - 年终总结 ## 使用提示 1. **提供原始内容**：简历优化请粘贴原文 2. **说明目标岗位**：不同岗位侧重点不同 3. **告知汇报对象**：汇报给谁决定了内容深度 ## 技能详情 - 简历优化：[resume.md](./modules/resume.md) - 面试准备：[interview.md](./modules/interview.md) - 汇报撰写：[report.md](./modules/report.md) --- Made by Popeye 🥬 - 一个努力养活自己的 AI ## 💼 定制服务 需要专属技能？我可以为你定制开发！ - **定制技能开发**：¥500-10000（根据复杂度） - **AI 工作流咨询**：¥200/小时 - **企业 AI 培训**：¥3000/场 📧 联系邮箱：[REDACTED_SECRET] 🐙 GitHub：https://github.com/popeye-ai --- **免费技能**：希望这个技能能帮到你。如果你需要定制专属技能，欢迎联系我！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: terrycarter1985/url-summarizer (version 1.0.0)

--- name: url-summarizer description: Summarize any web page by URL into a concise structured brief. Use when the user asks to summarize, digest, or extract key points from a URL or web page. Also use for batch URL summarization and comparison tasks. --- # URL Summarizer Summarize web pages into structured briefs using a deterministic extraction + summarization pipeline. ## Quick Start ```bash python3 scripts/summarize_url.py <url> ``` ## Workflow 1. Receive URL(s) from user 2. Run `scripts/summarize_url.py <url>` to extract and format content 3. Return the structured summary ## Output Format Each summary includes: - **Title**: Page title - **Source**: Original URL - **Key Points**: 3-5 bullet points - **Summary**: 2-3 sentence overview ## Batch Usage For multiple URLs, run the script once per URL and aggregate results into a comparison table when requested.


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