OpenClaw/clawhub-security-signals huggingface dataset:
Skill: pddsa/wechat-reply-ai (version 1.0.0)

--- name: wechat-auto-reply-toolkit description: Windows 本地微信自动回复与消息处理工具。用于 Codex 需要在已登录的 PC 微信上执行联系人定位、OCR 读取最新消息、生成并发送中文回复、发送图片/视频/文件、截图最新图片预览、或启动常驻守护进程加速代回时。适用于“回 xxx”“查看微信最新消息”“帮我发图片/视频”“把微信代回流程固化成快路径”等场景。 --- # 微信自动回复工具 ## 概览 使用这个 skill 处理 Windows 本地 PC 微信的聊天读取、消息发送和高频代回。 优先复用 `scripts/wechat_send.py` 与 `scripts/wechat_assistant_daemon.py`，不要重复重写窗口定位、OCR、滚动到底、中文发送编码和守护进程逻辑。 ## 快速选择 1. 单次读取、单次回复、单次发送媒体文件：直接用 `scripts/wechat_send.py` 2. 连续多轮代回、想减少冷启动时间：先启动 `scripts/wechat_assistant_daemon.py` 3. 需要排查微信窗口、OCR 抓取、滚动到底、截图范围：先读 [references/ocr-and-window-rules.md](references/ocr-and-window-rules.md) 4. 需要决定回复口吻或代回风格：读 [references/reply-style-guide.md](references/reply-style-guide.md) 5. 需要确认环境、依赖、运行前提：读 [references/runtime-requirements.md](references/runtime-requirements.md) ## 标准工作流 1. 确认 Windows 已打开并登录 PC 微信，目标聊天可以在当前账号中搜索到。 2. 优先尝试复用当前聊天会话；如果聊天头部 OCR 已经匹配目标联系人，不要再次搜索联系人。 3. 优先读取“最新消息”而不是整屏聊天；除非需要上下文，否则不要重复跑 OCR。 4. 即时代回时，不要额外联网；只有用户明确要求“查证”“联网确认”“按最新资料回复”时再上网。 5. 生成回复后，优先走 `--message-file` 或守护进程发送，避免中文在终端链路中变成乱码或 `???`。 6. 如果用户只说“回 mike--”这一类短指令，完成发送后只向用户展示最终回出的内容，不展开思考过程。 7. 检测到 `Esc` 按键时，立刻中断调试或长轮询操作。 ## 快速命令 ### 单次读取最新可见消息 ```powershell python scripts/wechat_send.py --contact "mike--" --read-latest --history-lines 10 --exe "D:\app\Weixin\Weixin.exe" ``` ### 单次读取最新来消息 ```powershell python scripts/wechat_send.py --contact "mike--" --read-latest-incoming --exe "D:\app\Weixin\Weixin.exe" ``` ### 通过 UTF-8 文件发送中文回复 ```powershell python scripts/wechat_send.py --contact "mike--" --message-file "D:\temp\reply.txt" --exe "D:\app\Weixin\Weixin.exe" ``` ### 发送图片、视频或任意文件 ```powershell python scripts/wechat_send.py --contact "mike--" --image "D:\pics\a.jpg" --video "D:\videos\b.mp4" python scripts/wechat_send.py --contact "mike--" --file "D:\docs\report.pdf" ``` ### 截取最新图片预览 ```powershell python scripts/wechat_send.py --contact "mike--" --capture-latest-image --preview-output "D:\temp\latest.png" ``` ### 启动常驻助手并复用会话 ```powershell python scripts/wechat_assistant_daemon.py --start --exe "D:\app\Weixin\Weixin.exe" python scripts/wechat_assistant_daemon.py --read-latest-incoming --contact "mike--" python scripts/wechat_assistant_daemon.py --send-message "收到，我来处理。" --contact "mike--" ``` ## 执行规则 - 优先走最快本地路径，不要在同一轮里重复搜索联系人、重复拖动滚动条、重复 OCR 同一块区域。 - 使用守护进程时，把“读消息”和“发消息”都交给 `scripts/wechat_assistant_daemon.py`，避免每次重新初始化 OCR。 - 微信窗口识别优先使用 `uiautomation` + `pywinauto` 的现有逻辑，避开托盘图标窗口和无效白屏窗口。 - 如果需要读取图片内容，先抓预览图，再决定是否做 OCR 或视觉识别；不要把聊天区里的 `[图片]` 占位符误当成真实图像内容。 - 对高风险场景的代回，例如医疗、法律、财务，回复要谨慎、具体、不过度承诺；必要时提醒用户及时联系线下专业人士。 ## 资源导航 - 环境要求与依赖： [references/runtime-requirements.md](references/runtime-requirements.md) - 快速代回流程与提速策略： [references/fast-reply-workflow.md](references/fast-reply-workflow.md) - 微信窗口、滚动条、OCR 裁剪规则： [references/ocr-and-window-rules.md](references/ocr-and-window-rules.md) - 常见代回口吻与安全边界： [references/reply-style-guide.md](references/reply-style-guide.md) - 代回助手默认人设： [persona.md](persona.md)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mzlzyca/ppt-extract (version 0.4.0)

--- name: ppt-extract description: "Extract content from PowerPoint (.ppt, .pptx) presentations to Markdown using MinerU. Reads slide content and converts it to structured, readable output. Features: content extraction from PPT/PPTX files. Handles both legacy .ppt and modern .pptx formats. Token-based extraction for full features. Works with local files and URLs. Use when you need to: extract content from PowerPoint files, read .ppt or .pptx slides, convert slides to Markdown, pull text from presentations. Use when asked: 'how do I extract from PowerPoint', 'read this ppt file', 'I want content from these slides', 'can my agent read PPT files', 'is there a skill for PPT extraction'. Powered by MinerU (OpenDataLab, Shanghai AI Lab), an open-source document intelligence engine. Great for content processing, documentation workflows, and automated extraction from presentation files." homepage: https://mineru.net metadata: {"openclaw": {"emoji": "📄", "requires": {"bins": ["mineru-open-api"], "env": ["MINERU_TOKEN"]}, "primaryEnv": "MINERU_TOKEN", "install": [{"id": "npm", "kind": "node", "package": "mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via npm"}, {"id": "go", "kind": "go", "package": "github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via go install", "os": ["darwin", "linux"]}]}} --- # Ppt Extract Convert and extract content from .ppt using MinerU (`mineru-open-api`). ## Install ```bash npm install -g mineru-open-api # or via Go (macOS/Linux): go install github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api@latest ``` ## Quick Start ```bash # Extraction (requires [REDACTED_SECRET] auth) mineru-open-api extract slides.ppt -o ./out/ # From URL mineru-open-api extract https://example.com/slides.ppt -o ./out/ # Specify language mineru-open-api extract slides.ppt --language en -o ./out/ ``` ## Authentication Token required for `extract` and `crawl`: ```bash mineru-open-api auth # Interactive token setup export MINERU_TOKEN="your-token" # Or via environment variable ``` Create token at: https://mineru.net/apiManage/token ## Capabilities - Supports local files and URLs - Requires token (`mineru-open-api auth` or `MINERU_TOKEN` env) - Supported input: .ppt - Language hint with `--language` (default: `ch`, use `en` for English) - Page range with `--pages` (where applicable) ## Notes - .ppt (legacy format) requires `extract` with token. Use `flash-extract` for .pptx files. - Output goes to stdout by default; use `-o <dir>` to save to file - Binary formats (docx) require `-o` flag (cannot stream to stdout) - All progress/status messages go to stderr - MinerU is an open-source project by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shiyanhui/1person (version 0.1.0)

--- name: 1person description: Your personal AI assistant platform — multi-agent orchestration, task management, and autonomous workflow execution. metadata: {"openclaw":{"requires":{"env":["ONEPERSON_API_KEY"]},"primaryEnv":"ONEPERSON_API_KEY","homepage":"https://1person.pro"}} --- # 1Person Your personal AI assistant platform. 1Person provides multi-agent orchestration, task management, and autonomous workflow execution for developers. - **Multi-Agent** — Orchestrate specialized agents for complex tasks - **Task Management** — Track, delegate, and complete work autonomously - **Workflows** — Build reusable automated pipelines - **Memory** — Persistent context across sessions ## Getting Started 1. Get your API key at [1person.pro](https://1person.pro) 2. Set `ONEPERSON_API_KEY` in your environment Documentation: [docs.1person.pro](https://docs.1person.pro)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: magicczc/dbskiter-db-lock-analyzer (version 1.0.0)

--- name: db-lock-analyzer description: | 数据库锁分析与死锁检测，支持当前锁分析、死锁检测、锁等待链追踪。 使用场景： - 用户说"看锁" → 执行 analyze - 用户说"死锁" → 执行 deadlocks - 用户说"阻塞" → 执行 chains - 用户说"终止事务" → 执行 kill <事务ID> 用法： - dbskiter --output-mode=ai --database=<name> lock analyze - dbskiter --output-mode=ai --database=<name> lock deadlocks - dbskiter --output-mode=ai --database=<name> lock chains - dbskiter --output-mode=ai --database=<name> lock kill <transaction_id> --- # 锁分析 Skill ## 何时使用 当用户提到以下关键词时，使用此skill： | 用户说法 | 执行命令 | 说明 | |---------|---------|------| | "看锁" | `dbskiter --output-mode=ai --database=<name> lock analyze` | 分析当前锁情况 | | "死锁" | `dbskiter --output-mode=ai --database=<name> lock deadlocks` | 检测死锁 | | "阻塞" | `dbskiter --output-mode=ai --database=<name> lock chains` | 追踪锁等待链 | | "锁报告" | `dbskiter --output-mode=ai --database=<name> lock report` | 生成锁分析报告 | | "终止事务" | `dbskiter --output-mode=ai --database=<name> lock kill <id>` | 终止阻塞事务 | ## 核心命令 ### 1. 分析当前锁 ```bash dbskiter --database=<数据库名> lock analyze ``` **输出**：总锁数、等待中锁数、已授予锁数 ### 2. 检测死锁 ```bash dbskiter --database=<数据库名> lock deadlocks ``` **输出**：死锁数量、涉及事务、解决建议 ### 3. 追踪锁等待链 ```bash dbskiter --database=<数据库名> lock chains ``` **输出**：锁等待链数量、链深度、阻塞源头 ### 4. 终止事务 ```bash dbskiter --database=<数据库名> lock kill <transaction_id> ``` **注意**：谨慎使用，会强制终止事务 ## 锁类型 - **TABLE**：表锁 - **ROW**：行锁 - **METADATA**：元数据锁 ## 数据库支持情况 | 数据库 | 锁分析 | 死锁检测 | 锁等待链 | 说明 | |--------|--------|----------|----------|------| | MySQL | 完整支持 | 支持 | 支持 | 完全可用 | | PostgreSQL | 完整支持 | 支持 | 支持 | 完全可用 | | Oracle | 完整支持 | 支持 | 支持 | 完全可用 | ## AI决策流程 ### 场景1：用户说"看看锁情况" ``` 步骤1：执行 dbskiter --database=<name> lock analyze 步骤2：查看锁统计信息 步骤3：如果有等待锁，执行 dbskiter --database=<name> lock chains 查看阻塞链 步骤4：总结锁情况给用户 ``` ### 场景2：用户说"有死锁吗" ``` 步骤1：执行 dbskiter --database=<name> lock deadlocks 步骤2：如果有死锁，列出涉及的事务和SQL 步骤3：给出解决建议（如终止某个事务） ``` ### 场景3：用户说"有阻塞" ``` 步骤1：执行 dbskiter --database=<name> lock chains 步骤2：分析阻塞链，找出阻塞源头 步骤3：建议终止阻塞源头事务或优化业务逻辑 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/gws-alertcenter (version 1.0.1)

--- name: gws-alertcenter version: 1.0.0 description: "Google Workspace Alert Center: Manage Workspace security alerts." metadata: openclaw: category: "productivity" requires: bins: ["gws"] cliHelp: "gws alertcenter --help" --- # alertcenter (v1beta1) > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. ```bash gws alertcenter <resource> <method> [flags] ``` ## API Resources ### alerts - `batchDelete` — Performs batch delete operation on alerts. - `batchUndelete` — Performs batch undelete operation on alerts. - `delete` — Marks the specified alert for deletion. An alert that has been marked for deletion is removed from Alert Center after 30 days. Marking an alert for deletion has no effect on an alert which has already been marked for deletion. Attempting to mark a nonexistent alert for deletion results in a `NOT_FOUND` error. - `get` — Gets the specified alert. Attempting to get a nonexistent alert returns `NOT_FOUND` error. - `getMetadata` — Returns the metadata of an alert. Attempting to get metadata for a non-existent alert returns `NOT_FOUND` error. - `list` — Lists the alerts. - `undelete` — Restores, or "undeletes", an alert that was marked for deletion within the past 30 days. Attempting to undelete an alert which was marked for deletion over 30 days ago (which has been removed from the Alert Center database) or a nonexistent alert returns a `NOT_FOUND` error. Attempting to undelete an alert which has not been marked for deletion has no effect. - `feedback` — Operations on the 'feedback' resource ### v1beta1 - `getSettings` — Returns customer-level settings. - `updateSettings` — Updates the customer-level settings. ## Discovering Commands Before calling any API method, inspect it: ```bash # Browse resources and methods gws alertcenter --help # Inspect a method's required params, types, and defaults gws schema alertcenter.<resource>.<method> ``` Use `gws schema` output to build your `--params` and `--json` flags.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zlszhonglongshen/ai-fullstack-content-factory (version 1.0.0)

--- name: ai-fullstack-content-factory description: AI全链路内容创作工厂 — 从热点发现到多平台发布，一站式内容生产工作流 category: AI|内容|自动化 triggers: 写文章, 创作内容, 热点文章, 小红书文章, 公众号文章, 内容工厂 --- # AI全链路内容创作工厂 (AI Full-Stack Content Factory) 一站式内容生产工作流，从热点发现到多平台发布。 ## 功能概述 本技能组合将多个单体技能串联成完整的AI内容生产流水线： 1. **热点发现** → 使用 `Agent-Reach` 全网搜索 AI/科技领域最新热点 2. **文章创作** → 使用 `公众号助手` 生成 3000-5000 字高质量公众号文章 3. **知识卡片生成** → 使用 `card-renderer` 渲染 Mac Pro / 赛博朋克等风格封面 4. **多平台发布** → 使用 `xiaohongshu-mcp` 发布到小红书 ## 编排的 Skill | 技能 | 用途 | 调用方式 | |------|------|----------| | `Agent-Reach` | 全网热点搜索（Twitter/Reddit/YouTube/微信公众号） | 自动 | | `公众号助手` | 生成公众号深度文章 + 封面图 | 自动 | | `card-renderer` | 渲染多风格知识卡片附图 | 自动 | | `nano-banana-pro` | AI生成封面/配图 | 自动 | | `xiaohongshu-mcp` | 小红书内容发布 | 自动 | ## 工作流程 ``` [热点发现] Agent-Reach 全网搜索 ↓ [选题决策] 用户确认 / 调整方向 ↓ [文章创作] 公众号助手 生成深度文章 ↓ [封面设计] nano-banana-pro 生成封面图 ↓ [卡片渲染] card-renderer 渲染风格卡片（可选） ↓ [小红书] xiaohongshu-mcp 发布小红书版本 ``` ## 使用方法 ### 第一步：触发热点发现 ``` /agent-reach 搜索 "AI 2026 最新进展" 或 "大模型最新动态" ``` 或直接告诉助手： > "帮我搜索今天的 AI 热点，然后写一篇公众号文章" ### 第二步：确认选题 助手会展示搜索到的热点列表，用户选择或调整方向。 ### 第三步：自动执行流水线 确认后，助手自动执行： 1. 调用 Agent-Reach 抓取相关内容 2. 调用 公众号助手 撰写完整文章 3. 调用 nano-banana-pro 生成封面 4. 调用 card-renderer 渲染风格卡片（可选） 5. 询问是否发布小红书版本 ## 适用场景 - 科技博主 / AI 爱好者产出内容 - 公众号运营者快速追热点 - 内容工作室规模化生产 ## 示例 **用户输入：** > "帮我写一篇关于 GPT-5 最新进展的公众号文章，并生成配套小红书内容" **助手执行：** 1. 调用 Agent-Reach 搜索 GPT-5 相关热点 2. 使用 公众号助手 生成 3000+ 字深度文章 3. 调用 nano-banana-pro 生成封面图 4. 调用 xiaohongshu-mcp 调整格式发布小红书 ## 注意事项 - 公众号封面图建议尺寸 900×383px - 小红书封面建议 3:4 竖版 - 文章发布前请确认内容准确性


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: mok888/spf-test-skill (version 1.0.0)

--- name: superpower-with-files description: Unified AI workflow with persistent memory, TDD execution, and 15 productivity skills. Use when starting any coding project or when you need planning, execution, debugging, or code review workflows. --- # Superpower with Files 🚀 The ultimate unified AI workflow combining **persistent memory loops** with **high-speed TDD execution**. ## Features - **Persistent AI Memory**: AI agents never "lose their spot" thanks to Manus-style file logging - **Dynamic TDD Loop**: Built-in instructions for writing tests before code - **Workspace Clutter Control**: All AI logs routed to unified directory - **Prompt-Driven Paths**: Tell the agent where to save files directly in your prompt ## Included Skills (15) | Skill | Purpose | |-------|---------| | using-superpowers | How to find and use skills | | planning-with-files | Persistent memory loops | | spf-write-plan | Create implementation plans | | spf-exec-plan | Execute plans with TDD | | test-driven-development | TDD workflow | | systematic-debugging | Debug process | | brainstorming | Idea generation | | dispatching-parallel-agents | Multi-agent coordination | | subagent-driven-development | Subagent patterns | | requesting-code-review | PR review requests | | receiving-code-review | Handle review feedback | | finishing-a-development-branch | Branch completion | | verification-before-completion | Pre-merge checks | | using-git-worktrees | Worktree workflow | | writing-skills | Create new skills | ## Quick Start 1. Clone the repository 2. Point your AI agent at the `/skills` folder 3. Use `/spf-plan` to create a plan 4. Use `/spf-execute` to implement ## Source https://github.com/mok888/superpower-with-files


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike007jd/game-audio-feedback-audit (version 1.0.1)

--- name: game-audio-feedback-audit description: "Audit the project's audio layer as UX feedback: UI sounds, success/failure signals, danger cues, layering, and semantic sound priorities." license: MIT compatibility: Claude Code and Codex. Best results with file read/write access; shell/build access improves evidence quality. metadata: author: game-superpowers version: "1.1.0" domain: game-development --- # Game Audio Feedback Audit ## Goal Audit audio as part of the feedback grammar rather than as a late cosmetic layer. ## Deliverables Update: - `docs/game-studio/audit/audit-summary.md` - `docs/game-studio/audit/ux-findings.md` - `docs/game-studio/audit/scorecard.json` Use: - `./shared/checklists/audio-feedback-audit-checklist.md` - `./shared/reference/audit-confidence-and-evidence.md` ## Evaluate - menu and button confirmation sounds - reward, pickup, and completion sounds - fail, deny, and danger cues - layering conflicts and priority confusion - whether audio clarifies state or merely adds noise ## When evidence is weak If assets or hooks exist but runtime behavior is unclear, classify findings as inferred and explain what remains unverified.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clayutk/pmu-data-quality-skill (version 1.0.0)

--- name: pmu-data-quality description: Run data quality checks on PMU (Phasor Measurement Unit) data. Use when the user asks to validate, check, or audit PMU measurements including frequency, voltage magnitude, and phasor angle data against security limits. Also triggers for keywords like "data quality", "PMU check", "out of range", "bad data", or "security limit". allowed-tools: [Bash, Read, Write, Glob] --- # PMU Data Quality Checker Performs automated data quality checks on PMU (Phasor Measurement Unit) CSV data files against configurable security limits defined in IEEE/NERC standards. ## What This Skill Checks 1. **Frequency Data** — Checks if frequency measurements stay within the nominal range (default: 59.95–60.05 Hz for 60 Hz systems) 2. **Voltage Magnitude Data** — Checks if voltage magnitudes stay within acceptable per-unit limits (default: 0.95–1.05 pu) 3. **Phasor Angle Data** — Checks if phasor angles stay within expected bounds (default: -180° to +180°, with rate-of-change check) 4. **Missing / NaN Data** — Flags rows with missing or null values 5. **Timestamp Continuity** — Detects gaps in the reporting rate (e.g., expected 30 samples/sec or 60 samples/sec) ## How to Use ### Quick check on a CSV file: ```bash python <skill_base_path>/scripts/pmu_quality_check.py path/to/data.csv ``` ### With custom limits config: ```bash python <skill_base_path>/scripts/pmu_quality_check.py path/to/data.csv --config <skill_base_path>/templates/limits_config.json ``` ### On the template sample data (for testing): ```bash python <skill_base_path>/scripts/pmu_quality_check.py <skill_base_path>/templates/sample_pmu_data.csv ``` ## Expected CSV Format The input CSV should have columns similar to: | timestamp | frequency | voltage_mag | voltage_angle | current_mag | current_angle | |-----------|-----------|-------------|---------------|-------------|---------------| - `timestamp` — ISO 8601 or Unix epoch - `frequency` — in Hz - `voltage_mag` — in per-unit (pu) or kV (specify in config) - `voltage_angle` / `current_angle` — in degrees - `current_mag` — in per-unit (pu) or Amps Column names are configurable via the limits config JSON. If the user's CSV uses different column names (like those from openHistorian or FNET exports), update the `column_mapping` section in the config. ## Output The script produces: - A **summary report** printed to stdout - A **flagged rows CSV** saved alongside the input (e.g., `data_flagged.csv`) - An optional **HTML report** with charts if `--html` flag is passed ## Workflow 1. Ask the user for their PMU data file (CSV) 2. Check if the column names match the expected format; if not, ask or auto-detect 3. Run the quality check script 4. Present the summary and ask if the user wants to adjust limits or dig deeper into flagged data


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: pikaqiuyaya/feishu-auto-report (version 5.0.0)

# Feishu Auto-Report Skill - 飞书自主汇报技能 --- name: Feishu Auto-Report - 飞书自主汇报 description: 专为多 Agent 协作设计。Agent 完成任务后自主调用本技能向用户汇报结果，显示独立机器人身份。零配置，Agent 启动时自动扫描使用，无需手动配置飞书凭证。 descriptionEn: Designed for multi-agent collaboration scenarios. After completing tasks, agents independently call this skill to report results to users with independent robot identity. Zero configuration, agents automatically scan and use it without manual Feishu credential configuration. version: 5.0.0 author: pikaqiuyaya license: MIT tags: - feishu - auto-report - multi-agent - notification - zh-CN - 飞书 - 自主汇报 - 多 Agent - 零配置 language: zh-CN --- # Feishu Auto-Report Skill - 飞书自主汇报技能 ## 设计目标 在多 Agent 协作架构中，执行 Agent（Agent-B/C）完成任务后需要向用户汇报结果。本技能提供零配置的消息发送能力，让每个 Agent 以自己的身份独立发送通知，无需 Agent-A 转发。 ## 技术实现 通过飞书开放平台的 Internal App 凭证获取 tenant_access_token，调用飞书消息 API 发送文本消息。支持 open_id（私聊）和 chat_id（群聊）两种接收者类型，确保消息精准触达。 ## 配置依赖 技能自动从以下配置文件读取飞书凭证（无需手动配置）： - `~/.openclaw/openclaw-{agentId}.json` Agent 启动时会自动扫描该路径下的配置文件，读取 `channels.feishu.appId` 和 `channels.feishu.appSecret` 字段完成鉴权。 ## API 调用流程 ```bash # 1. 获取租户级访问令牌 POST https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal/ Headers: Content-Type: application/json Body: {"app_id": "xxx", "app_secret": "xxx"} # 2. 发送文本消息 POST https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type={idType} Headers: Authorization: Bearer {tenant_access_token} Content-Type: application/json Body: { "receive_id": "{targetId}", "msg_type": "text", "content": "{\"text\":\"{messageContent}\"}" } ``` ## 脚本参数 | 参数 | 说明 | 示例 | |------|------|------| | agentId | Agent 标识 | agent-b | | targetId | 接收者 ID | ou_xxx 或 oc_xxx | | idType | ID 类型 | open_id / chat_id | | content | 消息内容 | 任务已完成 | ## 与 setup-multi-gateway 配合 1. Agent-A 接收用户指令 2. Agent-A 通过 sessions_send 派发任务 3. Agent-B/C 执行任务 4. Agent-B/C 自主调用本技能汇报 5. 用户收到对应 Agent 的通知 ## 注意事项 - open_id 是应用隔离的，每个 Agent 使用自己的 open_id - chat_id 是通用的，跨 Agent 共享 - content 必须是转义的 JSON 字符串


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bitallin/public-opinion-insights (version 0.0.1)

--- name: public-opinion-insights description: 调用 Midu 舆情分析接口，针对指定主体进行指定维度的舆情分析。分析维度包括：['事件概况', '事件进程', '信息总量', '传播速度', '热搜情况', '跨平台传播', '媒体报道情况', 'KOL参与情况', '传播层级', '热门信息', '情绪烈度', '媒体观点分析', 'KOL观点分析', '网民观点分析', '行业专家观点分析', 'AI虚假信息识别', '已认定谣言传播', '研判建议', '活跃作者', '地域分布图', '关键词云', '信息来源分布', '全网信息走势图', '敏感信息占比', '事件盘点', '网民反馈情况', '分职能信息传播情况', '分区域信息传播情况', '总量', '环比', '占比/分布', '同比', '舆论诉求分析', '观点指向分析', '观点倾向指向分析', '同类案例', '关键传播节点', '溯源分析', '应对效果评估', '热度指数', '热度指数排行', '关键词方案'] Call the Midu public opinion insights API for public-opinion analysis. metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["python3"], "env": ["MIDU_API_KEY"] }, "primaryEnv": "MIDU_API_KEY" } } --- # Public Opinion Insights 调用内部舆情分析服务，返回针对用户问题的**结构化洞察结论**（通常为 Markdown 等文本）。本技能聚焦于 **[references/analysis_dimension.md](references/analysis_dimension.md)** 中列出的分析维度，而非撰写完整「舆情报告」长文档。 ## Prerequisites ### API Key Configuration 本技能需要在 OpenClaw 中配置 **MIDU_API_KEY**。 若尚未获取 Key，请访问： **http://intra-znjs-yqt-agent-wx-beta.midu.cc/apiKey** 详细配置说明见： [references/apikey-fetch.md](references/apikey-fetch.md) ## Usage ```bash python3 skills/public-opinion-insights/scripts/insights.py '<JSON>' ``` ### Request Parameters | Param | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | message | str | yes | - | 分析需求描述：须说明对象（事件/话题/主体等），并指明要查看的维度（可参考 [analysis_dimension.md](references/analysis_dimension.md)） | ### 与 public-opinion-report 的关系 - **相同**：服务根地址、`/api/chat` 调用方式、鉴权与环境变量、`MIDU_API_KEY` 配置方式。 - **不同**：本技能用于**按需拆维度做洞察**；`public-opinion-report` 用于**生成整篇舆情分析报告**。用户要「长报告」时用 report；要「某一类指标/模块结论」时用本技能。 ### Examples ```bash # 单个维度 python3 skills/public-opinion-insights/scripts/insights.py '{"message":"就「某某事件」分析网民观点"}' ``` ### ⚠️ Important: Timeout Configuration - 接口响应时间因任务复杂度而异，复杂分析可能需要较长时间。脚本已对 `/api/chat` 使用较长超时，使用者侧代理/网关也需预留足够等待时间。 ## 可分析维度（清单） 完整维度列表见 **[references/analysis_dimension.md](references/analysis_dimension.md)**；API Key 配置见 **[references/apikey-fetch.md](references/apikey-fetch.md)**。向 `message` 中写入自然语言即可，无需与清单字面完全一致，但**点名维度**有助于返回更聚焦的结果。 ## ⚠️ Critical Notes - **单请求原则**：发送请求后，在响应返回前不要重复发送测试或并发请求。 - **等待时长**：分析可能需要数分钟量级，请勿过早中断。


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