OpenClaw/clawhub-security-signals huggingface dataset:
Skill: hgta23/findskill (version 1.0.4)

*** name: findskill version: 1.0.4 description: 智能搜索和发现 OpenClaw 技能，支持中英双语，多来源搜索 homepage: <https://clawhub.ai> metadata: openclaw: emoji: "🔍" requires: bins: \[] --------- # FindSkill 技能包 ## 简介 FindSkill 是一个强大的 OpenClaw 技能搜索和发现工具，帮助用户快速找到所需的技能包。 ## 功能特点 - **智能搜索**：支持关键词、标签、作者等多维度搜索 - **中英双语**：完美支持中文和英文搜索 - **多来源搜索**：可从 ClawHub、本地仓库等多个来源搜索技能 - **实时更新**：技能数据库保持最新状态 - **详细信息**：提供技能包的完整描述、使用示例和版本信息 ## 使用场景 1. **寻找特定技能**：快速定位符合需求的技能包 2. **技能分类浏览**：按类别浏览可用技能 3. **技能趋势分析**：了解热门技能和最新发布 4. **技能依赖查询**：查看技能包之间的依赖关系 ## 搜索语法 FindSkills 支持高级搜索语法： - 关键词搜索：`"web scraping"` - 标签搜索：`tag:automation` - 作者搜索：`author:clawhub` - 组合搜索：`"data analysis" tag:python` ## 安装与使用 安装后，您可以使用以下命令： - 搜索技能：`findskill search <关键词>` - 列出热门技能：`findskill trending` - 查看技能详情：`findskill info <技能名称>` ## 技术栈 - Node.js - TypeScript - Axios（HTTP 请求） - Fuse.js（模糊搜索） ## 贡献 欢迎在 GitHub 上提交 Issue 和 Pull Request！ ## 许可证 MIT License


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: parkerscobey/hizal-start (version 0.1.0)

--- name: hizal-start description: Initialize the agent's context memory session. Self-triggering — fires whenever the agent begins work, picks up a task, or enters a coding session. Use before searching, writing, or building anything. Handles session start, resume, and recovery. Triggers on phrases like "let me start working", "begin this task", "pick up where I left off", "start coding", "let's get started", or any time the agent enters a new work context without an active session. --- # Hizal Start Begin every work session with Hizal. Search before you build; write as you build. ## Session Lifecycle ### 1. Check for Existing Session Call `get_active_session()` first. If `status="active"`, use the returned `session_id` and call `resume_session` to extend TTL. If `status="none"`, proceed to start a new session. ### 2. Start Session ``` hizal__start_session(lifecycle_slug="<slug>") ``` Returns a `session_id`. Keep it visible — needed for `register_focus` and `end_session`. The agent's project and lifecycle context is assumed known. If unsure, check AGENTS.md or ask the user. ### 3. Register Focus (Optional) If the task is known, register it immediately: ``` hizal__register_focus( session_id="<session-id>", task="<task description>", tags=["<tag1>", "<tag2>"] ) ``` See the `hizal-register-focus` skill for details on focus registration. ## After Starting 1. **Search Hizal** for existing context on the task (see `hizal-search` skill) 2. Only then begin coding or building 3. **Write to Hizal** as you make decisions (see `hizal-write` skill) 4. **End your session** when done (see `hizal-end` skill) ## Session Recovery If you lose your `session_id` (context reset, compaction), call `get_active_session()` again and follow the same check/resume flow above.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: changeworldy-cyber/viral-title-generator (version 1.0.0)

--- name: viral-title-generator description: 自媒体爆款标题生成技能。为小红书/公众号/抖音/知乎/B 站创作者生成高点击率标题。使用场景：(1) 小红书种草标题，(2) 公众号文章标题，(3) 抖音视频标题，(4) 知乎问答标题，(5) B 站视频标题，(6) 标题优化/改写。 --- # 自媒体爆款标题生成器 ## 核心功能 帮助自媒体创作者快速生成高点击率、高传播性的标题，提升内容曝光和流量。 ### 支持平台 - 小红书 - 微信公众号 - 抖音/快手 - 知乎 - B 站 - 微博 --- ## 各平台标题特点 ### 小红书（20 字以内） - **种草风** - "亲测好用"、"真的绝了" - **数字党** - "7 天瘦 5 斤"、"3 个技巧" - **情绪化** - "后悔没早买"、"哭死" - **emoji 加持** - 🔥✨💖📍 **示例：** ``` 25 岁存款为 0，我靠这 3 个副业月入 3 万💰 后悔没早买！这个神器让我每天多睡 1 小时⏰ 上海打工 5 年，我攒下第一个 50 万💪 ``` ### 公众号（24-30 字） - **悬念式** - "为什么...？"、"原来..." - **对比式** - "从...到..."、"以前...现在..." - **利益式** - "如何..."、"...的方法" - **权威式** - "XX 专家说"、"XX 研究显示" **示例：** ``` 从月薪 3 千到月入 10 万，我只做对了这 3 件事 为什么你总是存不下钱？这 5 个习惯正在毁掉你 哈佛研究：每天这样做，寿命延长 10 年 ``` ### 抖音（15-20 字） - **前 3 秒原则** - 开头就要抓住注意力 - **疑问式** - "你知道...吗？" - **反差式** - "没想到..."、"竟然..." - **指令式** - "一定要..."、"千万别..." **示例：** ``` 你知道吗？90% 的人都不知道这个省钱技巧 没想到！10 块钱的东西比 1000 块的还好用 一定要看！这个视频可能帮你省下一辆车 ``` ### 知乎（25-40 字） - **问题式** - 直接是问题 - **专业感** - 体现深度思考 - **数据支撑** - 用数字增加可信度 **示例：** ``` 普通人如何通过副业实现月入过万？有哪些可行的路径？ 工作 5 年存款为 0 是一种怎样的体验？ 为什么越来越多的年轻人选择不婚不育？ ``` ### B 站（20-30 字） - **二次元风** - 可以用梗、流行语 - **夸张式** - "史上最强"、"全程高能" - **系列化** - "第 X 期"、"XX 系列" **示例：** ``` 【全程高能】我花了 30 天，学会了 10 项技能 史上最强 Excel 教程！看完你就是大神 【第 10 期】挑战用 100 元过一周，结果... ``` --- ## 爆款标题公式 ### 公式 1：数字 + 结果 + 时间 ``` {数字}个{方法/技巧}，{时间}内{结果} 例：3 个存钱技巧，1 年内存下 10 万 ``` ### 公式 2：人群 + 痛点 + 解决方案 ``` {人群}还在为{痛点}烦恼？{解决方案} 例：上班族还在为没时间健身烦恼？这套方法每天只需 10 分钟 ``` ### 公式 3：对比 + 反差 ``` 从{状态 A}到{状态 B}，我只做了{事情} 例：从月光族到存款 50 万，我只做了这 3 件事 ``` ### 公式 4：悬念 + 揭秘 ``` 为什么{现象}？原来{真相} 例：为什么越省钱越穷？原来这才是富人思维 ``` ### 公式 5：警告 + 后果 ``` 千万别{行为}，否则{后果} 例：千万别这样存钱，否则越存越穷 ``` --- ## 高点击率技巧 ### 1. 用数字 - "3 个技巧"比"几个技巧"更具体 - "7 天"比"很快"更有说服力 - "10 万+"比"很多"更直观 ### 2. 制造反差 - "月薪 3 千"vs"月入 10 万" - "存款为 0"vs"攒下 50 万" - "普通人"vs"年入百万" ### 3. 引发好奇 - "原来..." - "没想到..." - "竟然是..." - "揭秘..." ### 4. 制造紧迫 - "限时" - "最后 X 天" - "马上截止" - "手慢无" ### 5. 情绪共鸣 - "后悔没早..." - "哭死" - "太香了" - "真的绝了" --- ## 违规词提醒 ### 各平台敏感词 - **绝对化** - 最、第一、顶级 - **医疗** - 治愈、疗效、减肥（用"瘦身"替代） - **金钱** - 赚钱、暴富（用"搞钱"、"增收"替代） - **色情** - 各种暗示性词语 ### 安全替代 ``` "赚钱" → "搞钱"、"增收" "减肥" → "瘦身"、"掉秤" "最好" → "超好用"、"绝了" "第一" → "天花板"、"宝藏" ``` --- ## 参考资源 - 标题公式详解：见 `references/title-formulas.md` - 平台规则：见 `references/platform-rules.md` - 爆款案例库：见 `references/examples.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: geoly-geo/geo-brand-overview (version 1.0.0)

--- name: "geo-brand-overview" version: "1.0.0" description: "Monitor your brand's AI visibility across ChatGPT, Perplexity, Gemini, Grok, and Google AI. Powered by GEOly AI — get AIGVR scores, mention counts, and platform distribution in real time via MCP." tags: ["geo", "brand-monitoring", "aigvr", "ai-search", "latest"] --- # Brand Visibility Overview > Powered by **GEOly AI** (geoly.ai) — the leading Generative Engine Optimization platform. ## What This Skill Does Connects to the **GEOly AI MCP** to fetch a real-time overview of your brand's AI visibility across 5+ major AI platforms, reporting: - 📊 **AIGVR Score** — AI Generated Visibility Rate (0–100) - 💬 **Brand Mentions** — Total appearances in AI-generated responses - 🔗 **Citations** — URLs cited by AI when mentioning your brand - 🌐 **Platform Distribution** — Breakdown across ChatGPT / Perplexity / Gemini / Grok / Google AI ## When to Trigger Activate when the user asks: - "How is my brand performing in AI search?" - "What is our current AIGVR score?" - "Show me brand mentions across AI platforms" - "Give me a visibility overview for the last 30 days" ## Instructions 1. Connect to GEOly AI MCP server: `https://app.geoly.ai/api/mcp` 2. Invoke the **Brand Overview** tool 3. Accept optional time range parameter (default: last 30 days) 4. Format results as a structured summary with platform breakdown 5. Highlight significant changes (>10% drop or rise) as alerts ## Output Format


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: duanc-chao/v (version 1.0.0)

### Skill: Drawing an ASCII "V" Graph #### Objective To create a visual representation of the letter "V" or a V-shaped graph using standard text characters. #### ️ Core Concept An ASCII "V" is constructed by printing characters on two diagonal lines that start wide at the top and converge to a single point at the bottom. This requires controlling the spacing (padding) on each line. #### Step-by-Step Guide 1. **Define the Size**: Decide on the height of your "V". Let's call this `height`. For this example, we will use a height of 5 lines. 2. **Iterate Through Rows**: You will create the graph line by line, from top to bottom. Let's use a variable `i` to represent the current row, starting from `0` up to `height - 1`. 3. **Calculate Positions for Each Line**: For each row `i`, you need to determine where to place the two characters that form the "V" shape (e.g., the asterisk `*`). - **Left Character Position**: The number of spaces before the first character increases as you go down. This is equal to the current row number, `i`. - **Right Character Position**: The number of spaces between the two characters decreases as you go down. This is calculated as `(height - 1 - i) * 2 - 1`. 4. **Handle the Bottom Point**: The formula for the space between the characters will result in a negative number for the very last line. This signals that you have reached the bottom point of the "V". In this case, you should only print one character, centered. #### ️ Visual Example (Height = 5) Let's trace the logic for a "V" with a height of 5: | Row (`i`) | Leading Spaces (`i`) | Middle Spaces (`(4-i)*2-1`) | Resulting Line | | ------ |------ |------ |------ | | **0** | 0 | 7 | `* *` | | **1** | 1 | 5 | ` * *` | | **2** | 2 | 3 | ` * *` | | **3** | 3 | 1 | ` * *` | | **4** | 4 | -1 (Bottom Point) | ` *` | #### Python Code Snippet Here is how you could implement this logic in Python: ``` def draw_ascii_v(height): for i in range(height): # Calculate spaces leading_spaces = " " * i middle_spaces_count = (height - 1 - i) * 2 - 1 # Print the line if middle_spaces_count < 0: # This is the bottom point print(leading_spaces + "*") else: # This is a regular V line middle_spaces = " " * middle_spaces_count print(leading_spaces + "*" + middle_spaces + "*") # Example usage draw_ascii_v(5) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iml1s/mirofish-predict (version 1.0.1)

--- name: mirofish-predict description: "MiroFish 群體智能推演引擎。當用戶要求「推演」「預測」「模擬」「如果…會怎樣」時使用。透過 55 個 AI Agent 在模擬社交平台上互動推演未來趨勢。" homepage: https://github.com/666ghj/MiroFish metadata: { "openclaw": { "emoji": "🐟", "requires": { "bins": ["mirofish", "docker"], "env": ["LLM_API_KEY"] }, "primaryEnv": "LLM_API_KEY", "install": [ { "id": "mirofish-npm", "kind": "node", "package": "mirofish-cli", "bins": ["mirofish"], "label": "Install MiroFish CLI (npm)", }, ], }, } --- # MiroFish 群體智能推演 用 55 個 AI Agent 在模擬的 Twitter/Reddit 上互動，推演未來趨勢。 ## 一鍵推演 ```bash mirofish predict "如果比特幣突破15萬美元，加密市場會怎樣？" ``` 自動完成：啟動 Docker 後端 → 建立專案 → 構建知識圖譜 → 生成 Agent → 運行模擬 → 輸出報告。 ## 首次設定 需要 Docker Desktop 和 LLM API key。 ```bash # 1. 啟動（首次會自動拉 Docker image） mirofish serve start # → 如果沒有 ~/.mirofish/.env，會生成模板讓你填 API key # 2. 配置 API key 後重新啟動 mirofish serve start ``` ### API Key 設定方式 - 環境變數：`export LLM_API_KEY=xxx` - OpenClaw config：`skills."mirofish-predict".env.LLM_API_KEY` - 檔案：`~/.mirofish/.env` ## 命令參考 | 命令 | 功能 | |:---|:---| | `mirofish predict "主題"` | 完整推演（自動啟動後端） | | `mirofish predict "主題" --rounds=10` | 指定推演輪數 | | `mirofish serve start` | 啟動 Docker 後端 | | `mirofish serve stop` | 停止後端 | | `mirofish serve status` | 檢查後端狀態 | | `mirofish projects` | 列出所有專案 | | `mirofish status <sim_id>` | 查詢模擬進度 | | `mirofish report <sim_id>` | 取得推演報告 | | `mirofish chat <sim_id> "問題"` | 對報告追問 | | `mirofish interview <sim_id> <agent_id> "問題"` | 採訪特定 Agent | | `mirofish env` | 顯示設定狀態 | ## 追問 ```bash mirofish chat <sim_id> "哪些 KOL 的觀點最極端？" mirofish interview <sim_id> 0 "你對 BTC 破 15 萬有什麼看法？" ``` ## 注意事項 - 推演消耗大量 LLM token（55 Agent × N 輪），本地模型可節省費用 - `--rounds` 建議 10-20 輪先試，效果好再加到 40 - `--platform` 可選 `twitter`、`reddit`、`parallel`（推薦 parallel） - 建議 LLM ≥ 14B 參數

path: _meta.json
content: slug: mirofish-predict
version: 1.0.0
publishedAt: 1741622400000

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: irook661/github-actions-troubleshooting (version 1.0.0)

--- name: github-actions-troubleshooting description: "Troubleshoot GitHub Actions workflows, particularly for Go projects. Diagnose failing workflows, distinguish between code and environment issues, interpret logs, and apply fixes for common CI/CD problems." metadata: { "openclaw": { "emoji": "🔄", "requires": { "bins": ["gh", "git"] }, "install": [ { "id": "brew", "kind": "brew", "formula": "gh", "bins": ["gh"], "label": "Install GitHub CLI (brew)", }, { "id": "apt", "kind": "apt", "package": "gh", "bins": ["gh"], "label": "Install GitHub CLI (apt)", }, ], }, } --- # GitHub Actions Troubleshooting Skill Use the `gh` CLI and Git to diagnose and fix GitHub Actions workflow failures, particularly for Go projects. This skill helps identify whether failures are due to code issues or environment/configuration problems. ## Workflow Analysis Check the status of recent workflow runs: ```bash gh run list --repo owner/repo --limit 10 ``` View details of a specific failing workflow: ```bash gh run view <run-id> --repo owner/repo ``` Get logs for failed jobs only: ```bash gh run view <run-id> --repo owner/repo --log-failed ``` ## Distinguishing Issue Types 1. **Code Issues**: Failures in compilation, tests, or linting that occur consistently across environments 2. **Environment Issues**: Problems with dependency resolution, tool installation, or type-checking in CI that work locally ## Common Go CI Fixes ### Linter Configuration Issues - Look for "undefined" reference errors that indicate import resolution problems - Try minimal linter configs that disable type-checking linters - Use `golangci-lint run --disable-all --enable=gofmt` for basic syntax checking ### Dependency Resolution - Verify go.mod and go.sum are consistent - Run `go mod tidy` to resolve dependency conflicts - Check that required dependencies are properly declared ## Diagnostic Commands Check specific workflow job logs: ```bash gh run view --job <job-id> --repo owner/repo ``` Download workflow artifacts for inspection: ```bash gh run download <run-id> --repo owner/repo ``` ## Troubleshooting Workflow 1. Identify which jobs are failing and which are passing 2. Examine error messages for clues about the nature of the issue 3. Determine if the issue is reproducible locally 4. Apply targeted fixes based on issue type 5. Monitor subsequent workflow runs to verify resolution


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

--- name: plans-todos description: 管理长期规划与短期待办事项。当用户提到目标、任务、计划、路线图，或需要将复杂目标拆解为存储在 memory/plans-todos.md 中的可执行项时使用。 version: 1.0.0 --- # Plans & Todos 助手 本技能通过维护 `~/.openclaw/workspace/memory/plans-todos.md` 文件，帮助用户管理目标拆解、进度追踪和待办清单。 ## 核心能力 ### 1. 目标拆解 (Goal Decomposition) 当用户提出一个宽泛的目标（如“学习 Python”）时： - **操作**：将其记录为一个 **Plan**。 - **拆解**：主动将其分解为至少 3-5 个具体的 **Todos**。 - **格式**：每个 Plan 必须包含描述、创建日期和初始任务列表。 ### 2. 状态管理 - **标记完成**：将 `[ ]` 更新为 `[x]`，并可选添加 `(完成日期: YYYY-MM-DD)`。 - **动态更新**：当用户在对话中提到“完成了某事”时，自动检索并更新对应的待办事项。 ### 3. 视图查询 - **全量列表**：读取存储文件，提取所有“进行中”的 Plan 及其挂载的待办事项。 - **分类检索**：支持按 Plan 名称或日期筛选任务。 ## 存储规范 始终保持 `~/.openclaw/workspace/memory/plans-todos.md` 如下结构： ```markdown # Plans & Todos 归档 ## [Plan 名称] - **描述**: [简短描述] - **创建日期**: YYYY-MM-DD - **当前状态**: [进行中 / 已完成 / 搁置] ### 待办清单 - [ ] 任务 1 (预计日期: MM-DD) - [x] 任务 2 (完成日期: YYYY-MM-DD) ``` ## 执行指南 1. **先读后写**：在任何增删改操作前，必须先使用 `read_file` 读取当前内容，以防覆盖历史数据或产生重复 ID。 2. **主动性**：如果用户提到的新意图看起来像是一个长期项目，主动询问：“需要我把这个加入你的 Plans 吗？” 3. **精准匹配**：在标记任务完成时，需通过上下文确保匹配到正确的任务描述。 4. **目录检查**：如果 `~/.openclaw/workspace/memory/` 目录不存在，请先创建。 ## 常用触发语例 - “帮我规划一下如何准备下周的面试” - “把‘买牛奶’加到我的待办里” - “列出我目前正在跟进的所有计划” - “刚才那个任务我做完了” - “帮我拆解一下制作个人博客的目标” ## 注意事项 - 保护隐私：不要在 Plans 中记录敏感密码或密钥。 - 保持整洁：定期建议用户清理或归档“已完成”超过一个月的 Plan。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: qingxiahotmail/art-knowledge-helper (version 1.1.0)

--- name: art-knowledge-helper description: 艺术知识库管理技能包。用于维护藏书整理、散文件清理、百度网盘同步、新书归档、藏书统计等日常管理工作。配合 art-tutor 技能使用。首次使用前请在 config.json 中填写知识库路径。 tags: [art, knowledge-base, management] version: 1.1.0 --- # 🎨 艺术知识库 · 管理助手 本技能用于维护艺术知识库藏书，支持：扫描下载目录归档新书、清理 EPUB 散文件、百度网盘双向同步、藏书量统计、生成藏书清单。 --- ## ⚙️ 首次配置（必须） **编辑 `config.json`**，填写你自己的路径： ```json { "knowledge_base_path": "D:\\艺术知识库", "baidu_path": "D:\\BaiduSyncdisk\\小艺\\艺术知识库", "downloads_path": "C:\\Users\\你的用户名\\Downloads" } ``` > **三个路径均可自定义**。只需改 `config.json`，所有脚本自动读取，无需修改脚本。 --- ## 功能概览 | 脚本 | 功能 | |------|------| | `scan_downloads.py` | 扫描下载目录近30天内艺术类书籍 | | `archive_books.py` | 将下载的新书归档到知识库对应分类 | | `kb_stats.py` | 统计藏书总量、各分类数量、总大小 | | `sync_to_baidu.py` | 双向同步本地与百度盘 | | `verify_sync.py` | 验证两边是否完全一致 | --- ## 快速开始 ### 1. 安装 下载本技能包，解压到 OpenClaw 工作目录： ``` skills/art-knowledge-helper/ ├── SKILL.md ├── config.json ← 【首次必填】路径配置 ├── SOUL.md ├── USER.md ├── README_SKILL.md ├── SHARE_GUIDE.md ├── FRIEND_TUTORIAL.md └── scripts/ ├── scan_downloads.py ├── archive_books.py ├── kb_stats.py ├── sync_to_baidu.py └── verify_sync.py ``` ### 2. 配置路径 编辑 `config.json`（见上方）。 ### 3. 扫描新书 ```powershell python scripts/scan_downloads.py ``` ### 4. 归档新书 ```powershell python scripts/archive_books.py ``` ### 5. 统计藏书 ```powershell python scripts/kb_stats.py ``` ### 6. 同步百度盘 ```powershell python scripts/sync_to_baidu.py ``` ### 7. 验证同步 ```powershell python scripts/verify_sync.py ``` --- ## 目录结构说明 ``` skills/ ├── art-tutor/ ← 艺术学习助手（推荐藏书、规划路径） │ └── references/ │ ├── corpus.md │ └── learning_paths.md └── art-knowledge-helper/ ← 知识库管理（藏书整理、同步、归档） ├── config.json ← 【必填】路径配置 ├── scripts/ ← 管理脚本（自动读 config.json） ├── SOUL.md ├── USER.md └── README_SKILL.md ``` --- ## 依赖 - Python 3 - PowerShell 5+（Windows） --- ## 更新日志 - **v1.1.0**（2026-04-22）：路径配置从硬编码改为 `config.json`，所有脚本统一读取，路径完全自定义。 - **v1.0.0**（2026-04-22）：初始版本，包含5个管理脚本和完整文档。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: vanhuelsing/flowclaw-orchestrator (version 1.1.3)

--- name: flowclaw version: 1.1.3 description: YAML-driven workflow orchestrator for AI agent teams with human-in-the-loop approval gates. Includes optional Notion, n8n, and Discord integrations. platforms: - macOS - Linux metadata: openclaw: requires: env: - WORKFLOW_EXECUTOR_API_KEY optionalEnv: - NOTION_API_KEY - DISCORD_BOT_TOKEN - N8N_API_KEY - OPENCLAW_GATEWAY_URL --- # FlowClaw YAML-driven workflow orchestrator for OpenClaw agent teams. Connects Notion → n8n → agents, with approval gates so nothing runs without your go-ahead. ## What It Does FlowClaw is a workflow execution engine that: 1. Receives task triggers from n8n (via Notion polling) 2. Loads the appropriate YAML workflow definition 3. Executes each step by dispatching to specialized AI agents 4. Pauses at approval gates and waits for human sign-off 5. Reports progress via Discord notifications ## Requirements - Python 3.8+ - OpenClaw with configured agents - n8n (optional — only needed for n8n-triggered workflows) - Notion workspace with task database (optional — only needed for Notion sync) ## Setup 1. Copy `config/example.env` to `.env` and fill in your API keys 2. Install: `pip3 install -r src/requirements.txt` 3. Start: `python3 src/workflow-executor.py` 4. Import `src/n8n-workflow.json` into your n8n instance 5. Update n8n credential/ID placeholders with your values — see [INTEGRATION-STEPS.md](docs/INTEGRATION-STEPS.md) ## Configuration All configuration is via environment variables. See `config/example.env` for the full list. Key variables: - `WORKFLOW_EXECUTOR_API_KEY` — API key for authenticating requests - `NOTION_API_KEY` — Notion integration token (starts with `secret_...`) - `DISCORD_BOT_TOKEN` — Discord bot token for notifications (optional) - `PORT` — Server port (default: 8765) - `MAX_WORKERS` — Gunicorn worker count (default: 4, recommended: 2× CPU cores)


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 1970168137/made-in-china (version 1.0.0)

# Made-in-China Trade Data Analyst **Short Description** Analyze global trade trends, export volumes, and product categories related to "Made in China" using public customs data. Get quick insights and summaries. **Full Description** This skill helps you explore and understand the landscape of Chinese manufacturing and exports through data. It focuses on **aggregated, statistical information** from official sources, primarily China's customs statistics. **Data Source** All data is derived from publicly available statistics provided by the **General Administration of Customs of China**. You can access the official data portal at: [http://stats.customs.gov.cn](http://stats.customs.gov.cn) **What you can ask** - “Show me the top 10 exported products from China in 2024.” - “Compare the export volume of electronics vs. textiles over the last 5 years.” - “What are the main destination countries for Chinese-made machinery?” - “Give me a summary of recent trends in China's steel exports.” **Features** - 📊 **Product Category Analysis** – Break down exports by HS codes and industry sectors. - 🌍 **Destination Insights** – See which countries import the most from China. - 📈 **Trend Visualization** – Generate simple charts and growth rates (text-based summary). - 🔍 **Custom Queries** – Ask about specific products, time periods, or regions. > **Note:** This skill uses official, publicly available trade data from China Customs. It does not access or retrieve information from any specific e-commerce platform or company database. For authoritative and comprehensive data, please refer to the source website.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 547895019/windows-tts-wsl2 (version 1.1.1)

--- name: windows-tts description: 在 Windows 11 上"直接发声"的 TTS（从 WSL2/TUI 调用 powershell.exe + System.Speech）。适用于用户说"说出来/读出来/语音播报/用TTS"，或反馈"没声音/tts 生成的 mp3 是空的/播不出来"，以及需要中文语音但 OpenClaw 内置 tts 不可用时。 --- # Windows TTS (WSL2) Use Windows built-in TTS via `powershell.exe` so audio plays through the **Windows 默认输出设备**（无需 WSLg/PulseAudio）。 ## 什么时候用这个 skill（触发提示） 当用户出现以下表达时，优先用本 skill，而不是 OpenClaw 内置 `tts`（后者可能生成空 mp3 或在某些环境无声）： - "说出来 / 读出来 / 念一下 / 语音播报 / 用 TTS" - "还没声音 / 没声音 / 播不出来" - "中文语音"且希望**直接从电脑扬声器播放** > 注意：本 skill 是"直接播放"，不会返回音频文件路径。 ## Quick start（直接说出来） Run from WSL: ```bash bash {baseDir}/scripts/say.sh "你好，我是你的助手。" ``` ## List installed voices ```bash bash {baseDir}/scripts/list_voices.sh ``` ## Speak with a specific voice ```bash bash {baseDir}/scripts/say.sh --voice "VOICE_NAME" "你好，我以后会用这个声音说话。" ``` ## Notes - If you embed PowerShell directly in bash, remember: **escape `$`** or use outer single quotes; otherwise bash expands `$s` and breaks the command. - If the user reports errors like `=New-Object` or `TypeName:` prompts, prefer the provided scripts instead of ad-hoc quoting.


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