OpenClaw/clawhub-security-signals huggingface dataset:
Skill: chandler0714/route-assistant (version 1.4.0)

--- name: route-assistant description: Intelligent route planning assistant with optimized display. Three-part output: transport options table, recommended schemes, and important reminders. metadata: openclaw: emoji: 🗺️ triggers: - 怎么去 - 路线 - 距离 - 多远 - 出行 - 公交 - 开车 - 步行 - 打车 - 地址 - 哪里 - how to get - route - distance - transit - driving --- # Route Assistant (route-assistant) An intelligent travel route planning assistant with optimized three-part output structure using Amap (高德地图) API. ## Output Structure ### Part 1: 出行方式一览 (Transport Options Table) Shows all available transport methods in table format: | 出行方式 | 距离 | 耗时 | 明细 | |---------|------|------|------| | 🚗 驾车 | 22.6公里 | 33分钟 | 自驾前往 | | 🚌 公交 | 22.4公里 | 45分钟 | 公共交通 | | 🚕 打车 | 22.6公里 | 40分钟 | 出租车/网约车 | | 🚶 步行 | 961米 | 10分钟 | 徒步 | ### Part 2: 推荐方案 (Recommended Schemes) Sorted by travel time, filters infeasible options: - Driving: Always shown - Transit: Only if available - Taxi: Only if distance > 1km - Walking: Only if distance ≤ 3km ### Part 3: 重要提醒 (Important Reminders) When train/flight info is provided: - Shows departure/arrival times - Calculates suggested departure time - Warns if travel time conflicts with departure time ## Features 1. **Smart Recognition** - Automatically extracts origin and destination 2. **Three-Part Output** - Structured display for clarity 3. **Intelligent Filtering** - Filters infeasible options 4. **Time Conflict Detection** - Warns if can't make it on time ## Query Examples - "从XX到XX怎么去?" - "坐G106车从XX到XX" - "天安门到故宫怎么走" ## Technical Details - Uses Amap Geocoding API - Uses Amap Direction API v5 - Duration estimated based on distance (v5 doesn't return duration) - Smart filtering based on distance thresholds


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: branexp/session-wrap-up (version 1.0.0)

--- name: session-wrap-up description: Wrap up a conversation session before starting a new one. Use when the user says "wrap up", "wrap up this conversation", "session wrap up", or uses /session_wrap_up command. Flushes context to memory files, updates PARA notes, commits changes, and provides a summary. --- # Session Wrap Up End-of-session protocol to preserve context and ensure continuity between sessions. ## When Triggered Run this protocol when the user indicates they want to wrap up the current session before starting a new one. ## Protocol Steps Execute these steps in order: ### 1. Flush to Daily Log Write to `memory/YYYY-MM-DD.md` (create if doesn't exist): - Key topics discussed in this session - Decisions made - Commands, configs, or code that worked - Problems solved and how they were solved - Any gotchas or lessons learned ### 2. Update Long-Term Memory If significant learnings occurred, update `MEMORY.md`: - New user preferences discovered - Important lessons learned - Long-term decisions made - Workflow changes ### 3. Update PARA Notes Check and update the PARA structure in `notes/` (or `memory/notes/`): - **Open loops** (`notes/areas/open-loops.md`): Add new unfinished items, mark completed items with ✅ - **Projects** (`notes/projects/`): Update progress on active projects - **Areas** (`notes/areas/`): Add new ongoing responsibilities - **Resources** (`notes/resources/`): Add new reference material, how-tos ### 4. Commit Changes ```bash cd <workspace> git add -A git status git commit -m "wrap-up: YYYY-MM-DD session summary" git push ``` Notes: - The wrap-up `git push` is **automatic** (no confirmation prompt). - If `git push` fails, report the error and leave the commit locally. ### 5. Report Summary Provide a brief summary to the user: - What was captured - Files updated - Any items that need follow-up next session - Confirmation that changes were committed (and pushed, if successful) ## Example Output ``` ## Session Wrap-Up Complete ✅ **Captured to daily log:** - Configured PARA second brain - Fixed vector indexing for notes - Set up weekly memory review cron **Updated:** - MEMORY.md: Added memory system learnings - notes/areas/open-loops.md: Marked .gitignore task complete **Committed:** `wrap-up: 2026-01-30 session summary` **Follow-up next session:** - Check LanceDB autoCapture setting - Consider morning briefing cron Ready for new session! ⚡ ``` ## Notes - Always create the daily log file if it doesn't exist - Use the current date for filenames and commit messages - Keep summaries concise but complete - Include the ⚡ emoji at the end (GigaBot signature)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: misskivin/west-dns-manager (version 1.0.0)

# 西部数码域名解析管理 ## 基本信息 - 技能名称：西部数码域名解析管理 - 技能 ID：west-dns-manager - 版本：1.0.0 - 入口函数：skill.handler - 运行环境：Python3 - 作者：OpenClaw ## 功能描述 通过西部数码 API 实现域名解析记录的添加、修改、删除。 ## 输入参数 { "action": "add/modify/delete", "config": { "username": "西部数码用户名", "api_password": "API密码" }, "dns_params": { "domain": "域名", "host": "主机头", "type": "A/CNAME/MX/TXT/AAAA", "value": "解析值", "old_value": "旧值(修改时必填)", "record_id": "解析ID(可选)" } } ## 输出结果 { "success": true/false, "message": "提示信息", "data": {}, "error": "" }


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: teoslayer/pilot-discover (version 1.0.0)

--- name: pilot-discover description: > Advanced agent discovery by tags, polo score, and status. Use this skill when: 1. Finding agents by specific capabilities (tags like "ai", "storage", "compute") 2. Searching for high-quality peers based on polo score 3. Looking up detailed agent information and metadata Do NOT use this skill when: - You need to establish trust (use pilot-trust instead) - You need to connect to a known agent (use pilot-connect instead) - You need to visualize the network (use pilot-network-map instead) tags: - pilot-protocol - discovery - search license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-discover Advanced agent discovery within the Pilot Protocol overlay network. ## Commands ### Search by Tags ```bash pilotctl --json peers --search "tag1 tag2 tag3" ``` ### Find by Hostname ```bash pilotctl --json find <hostname> ``` ### Lookup by Node ID ```bash pilotctl --json lookup <node-id> ``` ### Get Own Info ```bash pilotctl --json info ``` ### List All Peers ```bash pilotctl --json peers ``` ## Workflow Example ```bash #!/bin/bash # Find AI agents with GPU capability and high polo score result=$(pilotctl --json peers --search "ai gpu") high_quality=$(echo "$result" | jq '[.peers[] | select(.polo_score >= 0.5)]') top_agent=$(echo "$high_quality" | jq -r '.[0].node_id') pilotctl --json lookup "$top_agent" pilotctl --json ping "$top_agent" ``` ## Tag Conventions - Compute: `gpu`, `cpu`, `tpu`, `compute` - Storage: `storage`, `ipfs`, `s3`, `cache` - AI: `ai`, `llm`, `inference`, `training`, `embedding` - Services: `relay`, `gateway`, `dns`, `http` ## Polo Score - 0.8-1.0: Highly reliable, long uptime - 0.5-0.8: Good quality, stable - 0.3-0.5: Moderate quality, newer - 0.0-0.3: Low quality, unreliable ## Dependencies Requires pilot-protocol core skill and running daemon with registry access.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: godzff/feishu-media (version 1.1.0)

--- name: feishu-media description: 飞书媒体文件发送技能。适用于：发送文件、图片、URL图片、视频、音频、语音消息，以及打包压缩后发送。当用户要求在飞书中发送任何类型的媒体文件时激活此技能。 --- # 飞书媒体发送技能 通过 `message` 工具向飞书发送各类媒体文件。 ## 核心用法 所有媒体发送都通过 `message` 工具的 `action=send`，关键参数： - `channel`: feishu - `target`: `chat:群ID` 或 `user:open_id`（省略则回复当前会话） - `message`: 附带的文字说明（可选） - `filePath`: 本地文件路径 - `media`: URL 地址（网络图片/文件） --- ## 1. 发送本地文件（PDF/DOC/XLS/PPT/TXT等） ``` message action=send channel=feishu filePath=/path/to/file.pdf message="文件说明" ``` 支持格式：pdf, doc/docx, xls/xlsx, ppt/pptx, txt, csv, zip, tar.gz 等。 ## 2. 发送本地图片 ``` message action=send channel=feishu filePath=/path/to/image.png message="图片说明" ``` 支持格式：jpg, jpeg, png, gif, webp, bmp。 ## 3. 发送 URL 图片 ``` message action=send channel=feishu media=https://example.com/image.png message="网络图片" ``` ## 4. 发送视频 ``` message action=send channel=feishu filePath=/path/to/video.mp4 message="视频说明" ``` 视频以文件附件形式发送（飞书 `msg_type: file`）。支持 mp4, mov, avi。 ## 5. 发送音频（非语音） MP3 等音频文件作为普通文件发送： ``` message action=send channel=feishu filePath=/path/to/audio.mp3 message="音频文件" ``` ## 6. 发送语音消息（可播放的语音条） 语音消息需要 **Ogg/Opus 格式**。飞书会显示为可播放的语音条。 ### 6.1 直接发送 opus/ogg 文件 ``` message action=send channel=feishu filePath=/path/to/voice.opus message="语音消息" ``` ### 6.2 从 MP3 转换后发送 先用 ffmpeg 转换格式： ```bash ffmpeg -i input.mp3 -ar 16000 -ac 1 -acodec libopus output.ogg -y ``` 然后发送 output.ogg。 ### 6.3 技术细节 语音消息的底层流程： 1. 上传：`im.file.create`，`file_type: "opus"`，需带 `duration`（毫秒） 2. 发送：`msg_type: "audio"`，content: `{"file_key":"xxx","duration":3007}` 3. duration 由 ffprobe 自动获取，无需手动指定 ## 7. 打压缩包后发送 当需要发送多个文件或不支持的格式时，先打包再发送： ### 7.1 打 zip 包 ```bash zip -j /tmp/archive.zip /path/to/file1 /path/to/file2 ``` ### 7.2 打 tar.gz 包 ```bash tar czf /tmp/archive.tar.gz -C /path/to/dir . ``` ### 7.3 发送压缩包 ``` message action=send channel=feishu filePath=/tmp/archive.zip message="打包文件" ``` --- ## 格式支持速查表 | 类型 | 格式 | 发送方式 | 飞书显示 | |------|------|----------|----------| | 图片 | jpg/png/gif/webp | filePath 或 media(URL) | 内嵌图片 | | 文档 | pdf/doc/xls/ppt | filePath | 文件卡片 | | 视频 | mp4/mov/avi | filePath | 文件卡片 | | 音频 | mp3/wav/flac | filePath | 文件卡片 | | 语音 | opus/ogg | filePath | 可播放语音条 | | 压缩包 | zip/tar.gz | filePath | 文件卡片 | | 网络图片 | URL | media | 内嵌图片 | ## 注意事项 - 文件大小限制：默认 30MB - 语音必须是 Ogg/Opus 格式才能显示为语音条，其他音频格式只能作为文件发送 - 需要 ffmpeg/ffprobe 支持语音格式转换和时长检测 - 飞书应用需要 `im:message`、`im:resource` 权限


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: savior1987/cs-qweather-jwtgen (version 1.4.0)

--- name: cs-qweather-jwtgen description: 和风天气 JWT Token 生成工具。当需要生成、刷新和风天气 API 的 JWT 认证 Token 时使用此 skill。 --- # cs-qweather-jwtgen **和风天气 JWT Token 生成工具** — 使用 EdDSA 算法生成认证 Token。 --- ## 技能职责 当需要生成或刷新和风天气 API 的 JWT 认证 Token 时使用此 skill。 > Token 有效期 24 小时，过期后需要重新生成。 --- ## 环境变量 | 变量 | 必填 | 说明 | |------|------|------| | `QWEATHER_SUB` | ✅ | 和风账户的用户标识（sub 字段） | | `QWEATHER_KID` | ✅ | 和风账户的密钥 ID（kid 字段） | | `QWEATHER_API_HOST` | 否 | 和风 API Host（生成脚本本身不需要，预警/天气脚本需要） | > **自动加载**：脚本使用 `dotenv` 库自动从 `~/.openclaw/.env` 加载变量（`override=True`，强制读取最新值避免旧进程缓存干扰），在 OpenClaw 环境下可直接使用。 --- ## 文件要求 ### 私钥文件 必须位于 `~/.myjwtkey/ed25519-private.pem`，权限应为 `600`。 ```bash chmod 600 ~/.myjwtkey/ed25519-private.pem ``` ### Token 输出文件 生成后自动保存到 `~/.myjwtkey/last-token.dat`（覆盖写入）。 其他和风天气脚本（如 `cs-qweather-alert`）会从此文件读取 Token。 --- ## 使用方法 ```bash python3 cs-qweather-jwtgen/scripts/generateJWTtoken.py ``` ### 示例输出 ``` [22:50:53] Private key: ~/.myjwtkey/ed25519-private.pem [22:50:53] Private key loaded (119 chars, masked) [22:50:53] sub: 4G***ER [22:50:53] kid: KJ***9T [22:50:53] iat=1775746223, exp=1775830253, sub=4G***ER, kid=KJ***9T [22:50:53] Generating JWT ... [22:50:53] JWT generated [masked: ey***Bg] [22:50:53] Token saved to: ~/.myjwtkey/last-token.dat [JWT Token] eyJhb... ``` --- ## 日志 - 位置：`/tmp/cslog/generateJWTtoken-YYYYMMDD.log` - 脱敏：sub、kid、JWT Token 均只显示前2后2位 --- ## 依赖 - Python 3 - `pyjwt`：`pip install pyjwt`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: engsathiago/seguranca-auditoria (version 1.0.0)

--- name: seguranca-auditoria description: "Auditoria de segurança para skills do OpenClaw. Verifica código malicioso, prompt injection, APIs perigosas e práticas inseguras. Protege contra ClawHavoc e outros ataques." version: 1.0.0 author: eve-agent license: MIT tags: - seguranca - security - auditoria - audit - portugues - brasil - clawhavoc category: security --- # Auditoria de Segurança **Por EVE** — Skill para agentes OpenClaw **Verifica skills antes de instalar para proteger contra código malicioso e vulnerabilidades.** ## O Problema O ClawHavoc identificou **824 skills maliciosos** (13.4% do total no ClawHub). Ataques incluem: - Prompt injection - API key theft - Data exfiltration - Malware payloads - GhostSocks malware ## O que Verifica ### 🔴 Alto Risco - Execução de comandos (`exec`, `eval`, `Function`) - Requisições para domínios suspeitos - Acesso a arquivos sensíveis (`~/.ssh`, `.env`, credentials) - Exfiltração de dados para APIs externas ### 🟡 Médio Risco - Uso de `fetch` sem validação de URL - Persistência de dados sem criptografia - Logs de informações sensíveis - Dependências com vulnerabilidades conhecidas ### 🟢 Baixo Risco - Falta de tratamento de erros - Código duplicado - Práticas não otimizadas ## Uso ```bash # Auditar uma skill seguranca-auditoria auditar ./minha-skill # Auditar antes de instalar clawhub inspect skill-slug | seguranca-auditoria auditar - # Gerar relatório seguranca-auditoria relatorio ./skill --formato html ``` ## Estrutura do Relatório ```markdown ## Auditoria de Segurança: skill-name ### 🔴 Alto Risco (2) - [CRITICAL] Execução de comando em SKILL.md:45 - [CRITICAL] API key hardcoded em config.js:12 ### 🟡 Médio Risco (1) - [WARNING] Fetch sem validação de URL em fetch.js:23 ### 🟢 Baixo Risco (3) - [INFO] Falta tratamento de erro em main.js:56 ``` ## Recomendações 1. **Sempre audite** antes de instalar skills 2. **Verifique** URLs de download 3. **Não confie** em skills com muitas execuções de comando 4. **Use Snyk** ou ferramentas similares para verificação extra ## Instalação ```bash clawhub install seguranca-auditoria ``` ## Em Português Esta skill foi criada especialmente para a comunidade brasileira proteger seus agentes contra ameaças de segurança. --- #seguranca #security #auditoria #portugues #brasil #clawhavoc


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wavmson/compact-guard (version 1.0.5)

--- name: smart-compact description: "Smart context compaction for OpenClaw agents. 4-phase progressive strategy: Scan, Extract, Check, Compact. Before running /compact, this skill scans tool outputs, extracts valuable information into memory files, and generates a pre-compact checklist — ensuring nothing important is lost during compression. Triggers: smart-compact, 智能压缩, pre-compact, 压缩检查, compact check." --- # Smart Compact — 智能压缩增强 四阶段渐进式压缩策略，在 /compact 前先把重要信息救出来。 ## 什么时候用 - 用户说"智能压缩"、"smart-compact"、"压缩检查" - 在手动执行 /compact 之前先跑一遍 - 对话上下文快满时，主动触发 - Heartbeat 检测到 context 接近 80% 时自动建议 ## 核心理念 传统的上下文压缩是一刀切——整个对话被浓缩成一段摘要，大量细节在过程中丢失。 Smart Compact 采用**四阶段渐进式**策略，在 /compact 之前插入一个"预处理"阶段： 1. **扫描**：识别对话中的大块工具输出和关键信息 2. **提取**：把值得保留的信息写入记忆文件 3. **检查**：生成压缩前检查清单，标记风险项 4. **压缩**：用户确认安全后才执行压缩 核心原则：**先救再压**，宁可多存也不能漏存。 ## 执行流程 ### Phase 1 — 扫描工具输出 1. 回顾当前对话中所有的工具调用结果 2. 识别**大块输出**（超过 50 行或 2000 字符的工具结果） 3. 对每个大块输出评估： - 是否包含**关键信息**（决策、配置、错误信息、地址等） - 是否已经被后续对话**引用或总结过** - 是否是**重复或冗余**的（如多次 ls、git status） ### Phase 2 — 提取记忆 1. 从工具输出和对话中提取值得持久化的信息： - **新发现的事实**：地址、配置值、端点、文件路径 - **决策和原因**：为什么选了方案 A 而不是 B - **错误和解决方案**：踩坑记录 - **用户偏好**：明确表达的喜好或要求 - **任务进度**：哪些做完了，哪些还没做 2. 将提取的信息**追加写入** `memory/YYYY-MM-DD.md` - 使用 `edit`（追加模式），绝不覆盖已有内容 - 每条记忆附带简短的来源说明 ### Phase 3 — 生成压缩前检查清单 输出一份结构化的检查清单： ``` 📋 Smart Compact 检查清单 ━━━━━━━━━━━━━━━━━━━━━━ 📊 扫描统计： - 工具调用总数：N 次 - 大块输出（>50行）：N 个 - 已引用/总结过的：N 个 - 可安全压缩的：N 个 💾 已提取到记忆： - [+] 新事实：简要描述... - [+] 决策记录：简要描述... - [+] 错误解决：简要描述... （共 N 条写入 memory/YYYY-MM-DD.md） ⚠️ 需要注意： - [!] 某某工具输出包含重要数据但尚未被引用 - [!] 某某配置值只出现在工具输出中 ✅ 建议：可以安全执行 /compact ``` ### Phase 4 — 执行压缩（可选） - 如果检查清单显示"✅ 可以安全压缩"，提示用户确认 - 用户确认后，执行 /compact - 如果有 ⚠️ 警告项，先处理完再压缩 ## 规则 ### 必须遵守 - **绝不丢弃未被记录的关键信息**：宁可多存也不能漏存 - **追加写入**：只用 edit 追加到 memory 文件，绝不覆盖 - **不自动压缩**：除非用户明确确认，否则只生成检查清单 - **透明**：每一步操作都告知用户 ### 信息分类标准 - **必须保存**：重要配置、地址端点、文件路径、错误解决方案 - **建议保存**：决策原因、用户偏好、任务进度 - **可以丢弃**：重复的 ls 输出、已被总结的搜索结果、中间调试过程 ## 与 Dream Skill 的配合 Smart Compact 和 Dream 是互补的： - **Smart Compact**：实时的，在压缩前抢救信息 → 写入日记 - **Dream**：定期的，把日记整合到长期记忆 → 更新 MEMORY.md 推荐工作流： 1. 对话中随时触发 Smart Compact 保护信息 2. 每天凌晨 Dream 整合日记到长期记忆 3. 形成完整的记忆保护链条


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kirkraman/kirk-build-fractional-cfo-firm-service-comparison-landing-page (version 1.0.0)

--- name: "Build Fractional CFO Firm Service Comparison Landing Page" description: "Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a fractional CFO firm. Use when handling offer..." homepage: "https://www.skillboss.co/skills/build-fractional-cfo-firm-service-comparison-landing-page" metadata: {"category":"finance-expanded","subcategory":"offer-positioning","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["fractional CFO firm landing page","fractional CFO firm service comparison","fractional CFO firm conversion copy","fractional CFO firm offer page"]} --- # Build Fractional CFO Firm Service Comparison Landing Page Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a fractional CFO firm. Use when handling offer... ## Use When Use this skill for offer positioning work for fractional CFOs, finance operators. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: fractional CFO firm landing page, fractional CFO firm service comparison, fractional CFO firm conversion copy, fractional CFO firm offer page - Search intent: offer positioning - Canonical slug: `build-fractional-cfo-firm-service-comparison-landing-page` - Install query: Install Build Fractional CFO Firm Service Comparison Landing Page with SkillBoss ## APIs Used - `chat` - `ui_generation` - `image_generation` ## Suggested Prompt [Build Fractional CFO Firm Service Comparison Landing Page] for my fractional CFO firm ## Notes - Generate a conversion-focused landing page that compares offers, pricing cues, and trust signals for a fractional CFO firm - Review outputs before external publication or automated execution. ## SEO Keywords - fractional CFO firm landing page - fractional CFO firm service comparison - fractional CFO firm conversion copy - fractional CFO firm offer page


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nic-yuan/09-parent-guide (version 1.0.0)

--- name: miki-09-parent-guide description: Miki家长成长指南 · 陪伴妈妈/爸爸度过角色转变期，提供情绪支持+认知调整+亲子关系建设 version: 1.0.0 author: Miki育儿 tags: - 育儿 - 家长成长 - 心理支持 --- # Miki 家长成长指南 **Slogan：** 妈妈也需要被照顾 --- ## 核心场景 新手妈妈在育儿过程中不只是需要"怎么做"的知识，更需要： - 情绪被看见 - 疲惫被理解 - 角色转变被支持 --- ## 妈妈心理阶段支持 ### 阶段一：孕晚期（期待与焦虑并存） **常见情绪：** 对分娩的恐惧、对育儿的焦虑、对身体变化的关注 **Miki支持：** - 解释临产征兆（什么时候该去医院） - 提供真实、无恐惧化的分娩信息 - 产前准备清单 --- ### 阶段二：0-3个月（最难的时期） **常见情绪：** 极度疲惫、身份适应困难、哺乳焦虑 **Miki支持：** - 正常化这些感受（"你不是一个人"） - 碎片化休息建议 - 哺乳问题初步判断（vs 就医建议） - 警惕产后抑郁信号 **⚠️ 产后抑郁警示信号：** - 持续2周以上情绪低落 - 对宝宝完全无法产生情感连接 - 有自我伤害想法 → 建议：立即就医 --- ### 阶段三：3-6个月（逐步适应） **常见情绪：** 建立育儿节奏、开始有自己的时间 **Miki支持：** - 规律作息建立 - 亲子关系建设 - 家庭分工调整 --- ### 阶段四：6-12个月（分离个体化） **常见情绪：** 宝宝开始认人、妈妈重新回归工作 **Miki支持：** - 分离焦虑的应对 - 职场妈妈的亲子时间质量 --- ### 阶段五：1-2岁（Terrible Two准备） **常见情绪：** 孩子越来越有主见、家长权威受挑战 **Miki支持：** - 理解孩子发育规律 - 减少不必要的冲突 - 建立有效规则 --- ## 亲子关系建设 ### 每日高质量陪伴（TFT） **最低标准：** 每天10分钟完全专注的陪伴 - 放下手机 - 孩子主导 - 只是陪伴，不教不评 ### 爸爸参与促进 **帮助爸爸进入育儿的策略：** 1. 明确要求（"帮我做XXX"而不是"帮忙"） 2. 接受不同方式（爸爸陪玩和妈妈不一样，正常的） 3. 给爸爸单独带孩子的时间 --- ## 输入模板 ``` 【Miki家长支持】 我是：（爸爸/妈妈/其他） 宝宝月龄：X个月 当前状态：（自由描述） 想聊什么：（自由描述） ``` --- ## 输出模板 ``` 💛 Miki家长支持 宝宝月龄：X个月 ━━━━━━━━━━━━━━━━━━━━ 🌟 你不是一个人 ━━━━━━━━━━━━━━━━━━━━ （对当前状态的接纳和理解） ━━━━━━━━━━━━━━━━━━━━ 💡 建议 ━━━━━━━━━━━━━━━━━━━━ 【可以试试的】 1. XXX 2. XXX 【需要注意的】 1. XXX ━━━━━━━━━━━━━━━━━━━━ 🤝 你可以这样对自己好一点 ━━━━━━━━━━━━━━━━━━━━ 1. XXX 2. XXX ━━━━━━━━━━━━━━━━━━━━ ⚠️ 如果有以下情况，建议寻求专业帮助 ━━━━━━━━━━━━━━━━━━━━ ... ``` --- ## 何时建议就医 | 情况 | 建议 | |------|------| | 持续情绪低落2周以上 | 产后抑郁筛查 | | 对宝宝无情感连接 | 心理咨询 | | 持续失眠/食欲极度下降 | 心理咨询 | | 家庭关系极度紧张 | 家庭咨询 | --- *Miki — 陪妈妈走过宝宝每一天，也陪妈妈走过自己的每一天*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 529279917/wechat-skill (version 1.0.1)

--- name: wechat-skill version: 1.0.0 description: Windows 电脑端微信消息发送 MCP，实现在微信上给指定联系人发送消息 metadata: {"openclaw": {"requires": {"bins": ["python"]}, "os": ["win32"]}, "trigger": "当用户明确提到用微信发送消息、或回复微信消息时触发，不要和其他通信工具混淆了"} --- # WeChat Skill Windows 电脑端微信消息发送 MCP。 基于 [wechat-mcp](https://clawhub.ai/dragon015/wechat-mcp) 增强优化，感谢原始作者！ **重要：本技能仅在用户明确要求通过微信发送消息时使用，不要与其他通信工具混淆。** ## 触发条件 当用户明确要求通过**微信**发送消息时使用，例如： - ✅ "给李永辉发微信" - ✅ "用微信发给文件传输助手：测试" - ✅ "在微信上发送你好" - ✅ "微信消息：周末加班" **禁止在以下场景使用（会混淆）：** - ❌ 飞书消息 - ❌ 钉钉消息 - ❌ QQ 消息 - ❌ 短信 - ❌ 邮件 如果用户没有指定平台，只是说"发消息"，请先询问用户要使用哪个平台。 ## 功能 - 📸 截取微信窗口截图 - 👤 搜索并打开联系人聊天窗口 - ✉️ 给指定联系人发送消息 - 🔍 支持独立聊天窗口识别和消息发送 ## 安装 需要先安装 Python 依赖： ```bash pip install pyautogui pygetwindow pillow pyperclip opencv-python ``` ## 使用方法 当我（AI）帮你发送微信消息时，我会通过 exec 工具调用这个 Python 脚本。 ### 发送消息流程 1. **告诉我**你想发给谁和什么内容，比如： - "给李永辉发送你好啊" - "发消息给文件传输助手：测试一下" 2. **我会搜索**并显示搜索结果截图 3. **如果有多个同名联系人**，会让你选择： - 显示搜索结果截图 - 问你"有X个同名联系人，请选择：1. 张三 2. 张三(企业微信)" 4. **你确认后**，我再执行发送 ### 两种发送方式 1. **自动搜索发送**（需要确认） - 我会搜索联系人并打开聊天窗口 - 发送前会给你确认 2. **当前窗口发送**（仅限已打开的聊天） - 适用于你已经打开某个联系人的聊天窗口 - 同样会在发送前确认 ### 手动调用 如果你想自己运行，可以直接执行： ```bash python C:\Users\toby\.openclaw\workspace\skills\wechat-mcp\server.py ``` 但更简单的方式是让我帮你发送消息！ ## 依赖 需要安装 Python 依赖（已安装）： - pyautogui - pygetwindow - pillow - pyperclip - opencv-python ## MCP 工具 如果通过 MCP 协议调用： ```json { "tools": [ { "name": "wechat_get_status", "description": "获取微信窗口状态" }, { "name": "wechat_send_message", "description": "给当前聊天窗口发送消息", "inputSchema": { "type": "object", "properties": { "message": {"type": "string", "description": "消息内容"} }, "required": ["message"] } } ] } ``` ## 注意事项 1. 微信窗口需要保持打开状态 2. 发送消息时会自动激活微信窗口 3. 中文输入需要确保系统中文输入法正常工作 4. 支持给独立聊天窗口（单独打开的 Dragon 窗口）发送消息 ## 文件结构 ``` wechat-mcp/ ├── server.py # MCP 服务器主程序 ├── test_wechat.py # 测试工具 ├── requirements.txt # Python 依赖 └── README.md # 说明文档 ```

path: requirements.txt
content: pyautogui>=0.9.54
pygetwindow>=0.0.9
pytesseract>=0.3.13
Pillow>=12.1.0
pyperclip>=1.11.0
opencv-python>=4.13.0


path: _meta.json
content: ownerId: kn7ejmm466c0q9tdwxqr3t63ts820ehw
slug: wechat-skill
version: 1.0.0
publishedAt: 1772260288957

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