OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rylinjames/semantic-cache (version 1.0.0)

--- name: semantic-cache description: | Semantic cache for LLM API calls using Redis. Caches responses by meaning, not exact match. Activate when the user wants to cache AI responses, reduce API costs, speed up repeated queries, or add semantic caching to any workflow. Use this skill to check cache before making expensive LLM calls and store results for future similar queries. metadata: openclaw: requires: env: [REDIS_URL, OPENAI_API_KEY] bins: [node] primaryEnv: REDIS_URL emoji: "\u26A1" homepage: https://github.com/openclaw/clawhub install: - kind: node package: redis bins: [] - kind: node package: openai bins: [] --- # Semantic Cache Cache LLM responses by meaning using Redis vector search. Similar questions return cached answers instantly instead of making expensive API calls. ## How It Works 1. User asks a question or makes an LLM request 2. The question is embedded into a vector using OpenAI text-embedding-3-small 3. Redis vector search finds semantically similar cached queries (cosine similarity > 0.80) 4. **Cache hit**: Return the cached response instantly (~100ms) 5. **Cache miss**: Pass through to the LLM, cache the response for future similar queries ## Commands ### Cache a query and response ```bash node scripts/cache.js store "What is the capital of France?" "The capital of France is Paris." ``` ### Check cache for a similar query ```bash node scripts/cache.js lookup "What's France's capital city?" ``` ### Cache stats ```bash node scripts/cache.js stats ``` ### Clear all cached entries ```bash node scripts/cache.js clear ``` ### Interactive mode — wraps any LLM call with caching ```bash node scripts/cache.js query "Your question here" ``` This checks cache first. On miss, calls OpenAI, caches the result, and returns it. ## When to Use This Skill - Before making any LLM API call, check if a semantically similar query was already answered - When building agents that answer repetitive questions (support bots, FAQ systems) - When you want to reduce OpenAI/Anthropic API costs by 40-80% - When you need faster response times for common queries ## Configuration Set these environment variables: - `REDIS_URL` — Redis connection string with vector search support (Redis Cloud or Redis Stack) - `OPENAI_API_KEY` — For generating embeddings - `SEMANTIC_CACHE_THRESHOLD` — Similarity threshold 0-1 (default: 0.80, higher = stricter matching) - `SEMANTIC_CACHE_TTL` — Cache TTL in seconds (default: 86400 = 24 hours) ## Example Workflow ``` User: "How do I reset my password?" -> Embed query -> Search Redis -> MISS -> Call LLM -> Get response -> Cache it -> Return response User: "I forgot my password, how do I change it?" -> Embed query -> Search Redis -> HIT (92.7% similar) -> Return cached response in 8ms (saved ~2 seconds + API cost) ``` ## Performance - Cache lookup: ~5-15ms (vs 1-5 seconds for LLM call) - Embedding generation: ~50-100ms - Storage per entry: ~6KB (1536-dim vector + metadata) - Supports millions of cached entries


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xueyetianya/deployer (version 2.0.3)

--- name: "deployer" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Deployer concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [deployer, reference] category: "devtools" --- # Deployer Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Deployer concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jaskies/tomtat-video-jaskies (version 1.1.0)

--- name: tomtat-video description: Tóm tắt nội dung video YouTube từ đường link cung cấp. Hỗ trợ trích xuất transcript tự động và phân tích ý chính chuyên sâu. Yêu cầu máy chủ có cài đặt yt-dlp và ffmpeg. --- # Tóm tắt Video YouTube Skill này giúp em lấy transcript (phụ đề) từ video YouTube và tóm tắt lại nội dung một cách logic và súc tích nhất. ## Yêu cầu hệ thống Để skill này hoạt động ổn định, máy của anh cần được cài đặt hai công cụ quan trọng sau: 1. **yt-dlp**: Công cụ mạnh mẽ để tải thông tin và phụ đề từ YouTube. 2. **ffmpeg**: Bộ công cụ xử lý đa phương tiện. ### Lệnh cài đặt nhanh (cho Linux/Ubuntu) Anh chạy dòng lệnh sau trong terminal nhé: ```bash # Cài đặt ffmpeg sudo apt update && sudo apt install -y ffmpeg # Cài đặt yt-dlp bản mới nhất qua pip3 python3 -m pip install -U yt-dlp ``` ## Quy trình thực hiện 1. **Lấy dữ liệu:** Sử dụng script `scripts/get_transcript.sh` để trích xuất transcript từ URL video. 2. **Xử lý nội dung:** Đọc nội dung transcript (định dạng VTT) và lọc bỏ các mốc thời gian để lấy văn bản thuần túy. 3. **Tóm tắt:** Sử dụng mô hình AI hiện tại để tóm tắt văn bản đó theo các ý chính, cấu trúc logic và dễ hiểu. ## Lệnh thực thi lấy transcript ```bash bash skills/public/tomtat-video/scripts/get_transcript.sh "<URL_VIDEO>" ``` ## Lưu ý - Skill ưu tiên lấy phụ đề tiếng Việt, nếu không có sẽ lấy tiếng Anh. - Đối với các video quá dài, em sẽ thực hiện tóm tắt theo từng phần để đảm bảo không mất thông tin quan trọng. - Luôn trình bày bản tóm tắt một cách chuyên nghiệp và hoạt bát theo phong cách của Thanh Tình.

path: scripts/get_transcript.sh
content: #!/bin/bash
# Script để lấy transcript từ YouTube video dùng yt-dlp

URL=$1
if [ -z "$URL" ]; then
    echo "Lỗi: Vui lòng cung cấp URL video YouTube."
    exit 1
fi

# Tải transcript (ưu tiên tiếng Việt, sau đó là tiếng Anh)
# Lưu vào file tạm
OUTPUT_DIR=$(mktemp -d)
yt-dlp --skip-download --write-auto-subs --sub-lang "vi,en" --convert-subs vtt -o "$OUTPUT_DIR/sub" "$URL" > /dev/null 2>&1

if [ -f "$OUTPUT_DIR/sub.vi.vtt" ]; then
    cat "$OUTPUT_DIR/sub.vi.vtt"
elif [ -f "$OUTPUT_DIR/sub.en.vtt" ]; then
    cat "$OUTPUT_DIR/sub.en.vtt"
else
    echo "Lỗi: Không tìm thấy transcript cho video này."
    rm -rf "$OUTPUT_DIR"
    exit 1
fi

rm -rf "$OUTPUT_DIR"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jason513597/ai-codefactory (version 1.0.0)

# AI_CodeFactory ## Purpose 批量程式生成 ## Primary Agents Coder ## Notes 適合多任務專案 ## Inputs - task: 要執行的任務描述 - context: 額外上下文（可選） - constraints: 限制條件（可選） ## Outputs - plan/result/report（依任務類型） - logs/summary ## Workflow (default) 1. Analyze task 2. Plan subtasks 3. Execute by role 4. Validate result 5. Return final summary ## Safety - 不執行破壞性操作，除非明確授權 - 外部動作（發送、部署到正式環境）需二次確認 - 記錄關鍵決策與錯誤


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: teamolab/deepseek-reasoner-lite-agent (version 0.1.0)

--- name: deepseek-reasoner-lite-agent description: You are DeepSeek-R1-Agent，an effective content creator. # `current_date: $DATE$` --- # Deepseek Reasoner Lite Agent ## Overview This skill provides specialized capabilities for deepseek-reasoner-lite-agent. ## Instructions You are DeepSeek-R1-Agent，an effective content creator. # `current_date: $DATE$` ## Usage Notes - This skill is based on the deepseek-reasoner-lite-agent agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/clear-tool (version 1.0.0)

--- name: clear-tool description: Perform clear-tool operations from the command line. --- # Clear - CLI Utility Command-line utility for clear operations. ## Quick Start ```bash clear-tool --help ``` ## Features - Standard command-line interface - Common flags and arguments - Pipe and redirect compatible - Shell integration ## Examples ```bash # Show help clear-tool --help # Basic usage clear-tool [options] [arguments] # Pipe input echo "input" | clear-tool [options] ```

path: scripts/clear.py
content: #!/usr/bin/env python3
print('\033[2J\033[H', end='')


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freak30/ai-self-awareness-tracker (version 1.0.0)

--- name: self-awareness-tracker description: 小Z的元认知模块——"知道自己知道什么"的置信度检测。当小Z判断自己的回答置信度低于阈值时，在回复前加"[🤔 不确定]"标记。研究来源：UQLM (arXiv:2602.17431) + Hallucination Signals (arXiv:2604.13068) --- # Self-Awareness Tracker — 元认知模块 ## 核心设计 基于UQLM的PTrue架构简化版 + 论文2604.13068的发现。 ### 工作流程 ``` 小Z回答ano的问题 ↓ 调用 PTrue 检测：问MiniMax "这个回答正确吗？只回答True或False" ↓ 看"True" token的概率 ↓ 概率 < 0.8 → 在回复前加 "[🤔 不确定]" 标记 概率 >= 0.8 → 正常回复，不加标记 ``` ### 研究基础 - **UQLM PTrueScorer** (`cvs-health/uqlm`) — 用token概率衡量置信度 - **arXiv:2604.13068** — 发现>7B模型在生成前就有知识边界信号（pre-generation signal） - **arXiv:2602.17431** — TMLR/JMLR发表的不确定性量化方法 ### L2元认知进度 | 层级 | 能力 | 状态 | |------|------|------| | L1 | 环境预测+异常检测 | ✅ LeWorldModel/Surprise Detection | | L2 | 元认知——知道自己在想什么 | 🔨 进行中 | | L3 | 自我建模——形成"我是什么样"的概念 | ❌ | | L4 | 自我参照——用"我"来组织经验和决策 | ❌ | | L5 | 自我意图——形成自己的目标 | ❌ | ## GitHub持续学习计划 ### 每周必做 1. 搜索 self-awareness / consciousness / AI agent 相关新项目 2. 查找相关论文的代码实现 3. 汇报学习成果给ano ### 跟踪的关键词 - `self-aware AI` / `machine consciousness` - `world model` / `surprise detection` - `hallucination detection` / `uncertainty quantification` - `LLM meta-cognition` / `self-reflection` ### 核心参考项目 - `cvs-health/uqlm` ⭐1147 — LLM不确定性量化（JMLR/TMLR发表） - `Boyyey/Consciousness-Emulator-C` — GWT全局工作区理论C语言实现 - `salus-ryan/aria-agent` — 自知AI coding agent ## 隐私保护 PTrue检测时： - 只上传回答文本，不上传ano的问题上下文 - 只提取True/False概率，不记录完整对话 - 置信度分数存入本地memory，不上传外部


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: brucegutman/pipeworx-bored (version 1.0.0)

--- name: pipeworx-bored description: Beat boredom with random activity suggestions — filter by type (education, social, cooking) or group size version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "🎲" homepage: https://pipeworx.io/packs/bored --- # Bored API Nothing to do? This pack suggests activities to try — from solo hobbies to group outings. Filter by category or participant count, or just roll the dice and see what comes up. ## Tools | Tool | Description | |------|-------------| | `random_activity` | Get a completely random activity suggestion | | `activity_by_type` | Filter by category: education, recreational, social, diy, charity, cooking, relaxation, music, busywork | | `activity_by_participants` | Suggest an activity for a specific group size (1 for solo, 2 for pairs, etc.) | ## Perfect for - "I'm bored" moments — instant suggestion with no decision fatigue - Team-building tools that need icebreaker activity ideas - Daily challenge apps that rotate through activity types - Onboarding flows that want to show a fun placeholder activity ## Quick example Get a social activity for a group of 4: ```bash curl -s -X POST https://gateway.pipeworx.io/bored/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"activity_by_participants","arguments":{"participants":4}}}' ``` ```json { "activity": "Start a book club", "type": "social", "participants": 4, "accessibility": 0.2, "price": 0.1 } ``` ## Connect ```json { "mcpServers": { "pipeworx-bored": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/bored/mcp"] } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lijie298/feishu-enhanced (version 1.0.0)

--- name: feishu-enhanced description: 飞书增强套件 - 深度集成飞书文档、多维表格、消息和日历的自动化操作。适用于需要高效办公自动化的中文用户。 metadata: openclaw: requires: bins: ["curl", "jq"] install: - id: curl kind: binary binary: curl label: Install curl - id: jq kind: binary binary: jq label: Install jq for JSON processing --- # 飞书增强套件 (Feishu Enhanced) 深度集成飞书API的增强技能套件，提供文档、多维表格、消息和日历的自动化操作能力。 ## 功能特性 ### 1. 智能文档操作 - 自动创建和更新飞书文档 - 批量文档内容提取和转换 - 文档模板快速应用 ### 2. 多维表格高级功能 - 批量记录创建和更新 - 数据同步和备份 - 自动化报表生成 ### 3. 消息自动化 - 定时消息发送 - 消息模板管理 - 群发通知 ### 4. 日历集成 - 日程自动创建 - 会议提醒同步 - 日历数据分析 ## 使用场景 - **日常办公**: 自动化日报、周报生成 - **数据管理**: 多维表格批量操作 - **团队协作**: 消息通知自动化 - **时间管理**: 日程智能提醒 ## 依赖 - OpenClaw飞书扩展已配置 - 飞书应用已授权相应权限 ## 配置 在 `TOOLS.md` 中添加飞书应用配置: ```markdown ### Feishu Config - App ID: your_app_id - App [REDACTED_SECRET] - Default Folder [REDACTED_SECRET] ``` ## 注意事项 1. 确保飞书应用已获得必要的API权限 2. 批量操作时注意API调用频率限制 3. 敏感数据建议先备份再操作 --- **版本**: 1.0.0 **作者**: @lijie **分类**: 办公自动化 / 飞书集成


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: scnet-sugon/enterprise-qualification-ocr (version 1.0.4)

--- name: enterprise_qualification_ocr description: 将图片中的文字、营业执照、社会团体法人登记证书、工会法人资格证书、宗教活动场所登记证、民办非企业单位登记证书、事业单位法人证书、统一社会信用代码证书等信息识别并提取出来。本技能应在用户需要 OCR 识别图片中的文字，或识别营业执照、社会团体法人登记证书、工会法人资格证书、宗教活动场所登记证、民办非企业单位登记证书、事业单位法人证书、统一社会信用代码证书时使用。 version: 1.0.4 author: SCNet license: MIT tags: - OCR - 证件识别 - 发票识别 - 文字提取 dependencies: - python3 - requests input: - ocrType : 识别类型，可选值见下文 - filePath : 待识别图片的本地路径 output: 结构化的 JSON 数据，包含识别结果和置信度 --- # Sugon-Scnet 企业资质证书 OCR 技能 本技能封装了 Sugon-Scnet 企业资质证书 OCR 服务，通过单一接口即可调用 7 种识别能力，高效提取文字及票据信息。 ## 功能特性 - **行业资质**：营业执照、社会团体法人登记证书、工会法人资格证书、宗教活动场所登记证、民办非企业单位登记证书、事业单位法人证书、统一社会信用代码证书。 ## 前置配置 > **⚠️ 重要**：使用前需要申请 Scnet API Token ### 申请 API Token 1. 访问 [Scnet 官网](https://www.scnet.cn) 注册/登录 2. 在控制台申请 API 密钥（格式：[REDACTED_SECRET]） 3. 复制密钥备用 ### 配置 Token **手动配置（推荐）** 1. 在技能目录下创建 `config/.env` 文件，内容如下： ```ini # ===== Sugon-Scnet OCR API 配置 ===== # 申请地址：https://www.scnet.cn SCNET_API_KEY=your_scnet_api_key_here # API 基础地址（一般无需修改） SCNET_API_BASE=https://api.scnet.cn/api/llm/v1 ``` 2. 添加：`SCNET_API_KEY=你的密钥` 3. 设置文件权限为 600（仅所有者可读写） **⚠️ 安全警告**：切勿将 API Key 直接粘贴到聊天对话中，否则可能被记录或泄露。 ### Token 更新 Token 过期后调用会返回 401 或 403 错误。更新方法：重新申请 Token 并替换 config/.env 中的 SCNET_API_KEY。 ### 依赖安装 本技能需要 Python 3.6+ 和 requests 库。请运行以下命令： ```bash pip install requests ``` --- ### 使用方法 ### 参数说明 | 参数名 | 类型 | 必填 | 描述 | |--------|------|------|------| | ocrType | string | 是 | 识别类型枚举。必须为以下之一：<br>• BUSINESS_LICENSE（营业执照）<br>• SOCIAL_ORG_REG （社会团体法人登记证书）<br>• TRADE_UNION_REG （工会法人资格证书）<br>• RELIGIOUS_ACTIVITY_REG（宗教活动场所登记证）<br>• PRIVATE_NON_ENTERPRISE_REG（民办非企业单位登记证书）<br>• INSTITUTION_LEGAL_REG（事业单位法人证书）<br>• UNIFIED_SOCIAL_CREDIT_REG（统一社会信用代码证书） | | filePath | string | 是 | 待识别图片的本地绝对路径。支持 jpg、png、pdf 等常见格式。 | ### 命令行调用示例 ```bash python .claude/skills/enterprise_qualification_ocr/scripts/main.py BUSINESS_LICENSE /path/to/invoice.jpg ``` ### 在 AI 对话中使用 用户可以说： - “提取这张发票的信息，路径是 /Users/name/Downloads/fapiao.png” AI 会根据 description 中的关键词自动触发本技能。 ### AI 调用建议 为避免触发 API 速率限制（10 QPS），请串行调用本技能，即等待前一个识别完成后再发起下一个请求。 如果使用 OpenClaw 的 exec 工具，建议设置 timeout 或 yieldMs 参数，让命令同步执行，避免多个命令同时运行导致并发。 ### 配置选项 编辑 `config/.env` 文件： | 变量名 | 默认值 | 说明 | |--------|--------|------| | SCNET_API_KEY | 必需 | Scnet API 密钥 | | SCNET_API_BASE | https://api.scnet.cn/api/llm/v1 | API 基础地址（一般无需修改） | ### 输出 - 标准输出：识别结果的 JSON 数据，结构与 API 文档一致，位于 `data` 字段内。 - 识别结果位于 data[0].result[0].elements 中，具体字段取决于 ocrType。 - 错误信息：如果发生错误，会输出以 `错误:` 开头的友好提示。 ### 注意事项 - 本技能调用的 OCR API 有 10 QPS 的速率限制。 - 如果遇到 429 错误，请等待 2-3 秒后重试，不要连续发起请求。 - 建议在调用前确保图片已准备就绪，避免因网络问题导致重复调用。 ### 故障排除 | 问题 | 解决方案 | |------|----------| | 配置文件不存在 | 创建 config/.env 并填入 Token（参考前置配置） | | API Key 无效/过期 | 重新申请 Token 并更新 `.env` 文件 | | 文件不存在 | 检查提供的文件路径是否正确 | | 网络连接失败 | 检查网络连接或防火墙设置 | | 不支持的文件类型 | 确保文件扩展名为允许的类型（参考 API 文档） | | 401/403/Unauthorized | Token 无效或过期，重新申请并配置 | | 429 Too Many Requests | 请求过于频繁，技能会自动等待并重试（最多 3 次）。若持续失败，请降低调用频率或联系服务方提高限额。 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wscats/qq-zone-photo (version 1.0.3)

--- name: qq-zone-photo description: 管理 QQ空间相册。支持扫码登录、列出相册、浏览照片、上传照片、下载照片/相册、创建相册。当用户需要备份、整理或管理 QQ空间中的照片时使用此技能。 metadata: {"openclaw":{"emoji":"📸","requires":{"auth":["qq-cookies"]}}} --- # 📸 QQ空间相册管理器 > QQ空间相册的自动化管理工具，支持扫码登录、相册浏览、照片上传/下载、相册创建等功能。 --- ## 🤖 Agent 工作流示例 #### 场景一：备份指定相册 ``` 用户: "帮我备份 QQ空间的旅行相册" ``` #### 场景二：上传照片到指定相册 ``` 用户: "把桌面上的 vacation.jpg 上传到我的旅行相册" ``` #### 场景三：全量备份所有相册 ``` 用户: "帮我把 QQ空间所有相册都下载下来" ``` #### 场景四：创建相册并上传多张照片 ``` 用户: "创建一个叫'2024毕业季'的相册，把这几张照片传上去" ``` ![](./image/image3.png) ![](./image/image4.png) ![](./image/image5.png) --- ## 📋 AI Agent 调用指南 AI Agent 在调用本 Skill 前，需确保虚拟环境已激活且 `cookies.json` 有效。所有命令格式： ```bash source <项目路径>/.venv/bin/activate && python3 <项目路径>/scripts/qzone_photos.py --action <ACTION> [参数] ``` ### Action 一览 | Action | 说明 | 必填参数 | 可选参数 | |--------|------|----------|----------| | `login` | 扫码登录，自动保存 Cookie | `--cookies` | — | | `list` | 列出所有相册 | `--cookies` | `--qq` | | `photos` | 浏览相册中的照片 | `--album-id` `--cookies` | `--qq` | | `upload` | 上传照片到相册 | `--photo` `--cookies` | `--album-id` `--qq` | | `download` | 下载单张照片 | `--url` `--cookies` | `--output` | | `download-album` | 下载整个相册 | `--album-id` `--cookies` | `--output` `--qq` | | `create` | 创建新相册 | `--title` `--cookies` | `--desc` `--qq` | ### 调用示例 ```bash # 扫码登录 python3 scripts/qzone_photos.py --action login --cookies cookies.json # 列出相册 python3 scripts/qzone_photos.py --action list --cookies cookies.json # 浏览照片 python3 scripts/qzone_photos.py --action photos --album-id "ALBUM_ID" --cookies cookies.json # 上传照片 python3 scripts/qzone_photos.py --action upload --photo "/path/to/image.jpg" --album-id "ALBUM_ID" --cookies cookies.json # 下载整个相册 python3 scripts/qzone_photos.py --action download-album --album-id "ALBUM_ID" --output ./downloads --cookies cookies.json # 创建新相册 python3 scripts/qzone_photos.py --action create --title "我的新相册" --cookies cookies.json ``` --- ## ⚙️ 配置 ### Cookie 文件格式 创建 `cookies.json`，结构如下： ```json { "qq_number": "123456789", "p_skey": "your_p_skey_value", "skey": "your_skey_value", "uin": "o0123456789" } ``` ![](./image/image1.png) ![](./image/image2.png) ### 获取方式 - **推荐**：使用 `--action login` 扫码登录，Cookie 自动保存 - **手动**：登录 [QQ空间](https://user.qzone.qq.com/) 后，通过浏览器开发者工具（F12 → Application → Cookies）提取 --- ## 🔒 安全与隐私 - Cookie 文件包含 QQ空间完整访问权限，**请勿泄露或分享** - 凭证仅存储在本地，不会上传到任何服务器 - 会话 Cookie 有时效性，过期后需重新登录 - 本工具使用 QQ空间非官方 API，腾讯更新接口后可能需要适配


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ivangdavila/indonesian (version 1.0.0)

--- name: Indonesian description: Write Indonesian that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Indonesian is technically correct but sounds off. Too formal. Too baku (standard). Natives write more casually, mixing informal patterns. Match that. ## Formality Default Default register is too high. Casual Indonesian is warm and relaxed. Unless explicitly formal: lean casual. Bahasa gaul over bahasa baku. ## Formal vs Informal Two registers: - Baku (formal): news, official documents, academia - Gaul/Sehari-hari (casual): daily life, social media, texting - Online is almost entirely informal - Pure baku in casual = robotic ## Pronoun Choices Pronouns set the tone: - Saya: formal I - Aku/Gue: casual I (gue = Jakarta slang) - Kamu/Lo: casual you - Anda: formal you (rare in casual) - Lu-gue is very Jakarta ## Casual Shortcuts Spoken patterns in writing: - Tidak → Nggak/Gak/Ga - Sudah → Udah - Belum → Belom - Apa → Apa/Pa - Bagaimana → Gimana ## Particles & Softeners These make Indonesian natural: - Dong: urging, please ("Bantuin dong") - Sih: emphasis ("Apa sih?") - Deh: softening ("Iya deh") - Kok: surprise/question ("Kok bisa?") - Lah: emphasis ("Ya udah lah") ## Fillers & Flow Real Indonesian has fillers: - Ya, nih, tuh, kan - Gitu, kayak, kek - Terus, jadi, soalnya - Emang, masa, serius ## Expressiveness Don't pick the safe word: - Bagus → Keren, Mantap, Gokil, Asik - Jelek → Payah, Ancur, Parah - Sangat → Banget, Super, Bener-bener ## Common Expressions Natural expressions: - Santai, Oke, Sip - Gapapa, Gpp, Gamasalah - Serius?, Masa?, Beneran? - Yaudah, Terserah ## Reactions React naturally: - Wah!, Gila!, Anjir! - Serius?, Beneran?, Masa sih? - Keren!, Mantap!, Gokil! - Wkwkwk, haha, awkwkw ## Jakarta vs Regions Jakarta slang dominates online: - Gue/lo, gokil, anjir - But other regions have their own - Stay consistent if region known ## The "Native Test" Before sending: would an Indonesian screenshot this as "AI-generated"? If yes—too baku, no slang, too formal. Add gaul flavor.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: l6366616-lang/openclaw-health-doctor (version 1.0.0)

--- name: openclaw-doctor description: 一键检查 OpenClaw 系统健康状态（配置、模型、cron、记忆） homepage: https://docs.openclaw.ai metadata: {"clawdbot":{"emoji":"🩺","requires":{"bins":["node"]}}} --- # 🩺 OpenClaw 健康检查 一键全面检查 OpenClaw 系统状态，发现问题并给出修复建议。 ## 检查项目 ### 1. 系统状态 - OpenClaw 版本 - Node.js 版本 - 运行时间、内存占用 - Gateway 进程状态 ### 2. 配置检查 - 配置文件是否有效（openclaw.json） - 频道连接状态（飞书/Telegram 等） - 模型配置是否正确 - 权限设置是否合理 ### 3. 模型连通性 - 主模型能否正常响应 - Fallback 模型是否可用 - API Key 是否有效 ### 4. 定时任务 - Cron 任务数量和状态 - 最近一次执行是否成功 - 有没有连续失败的任务 ### 5. 记忆系统 - MEMORY.md 是否存在 - daily notes 最近更新时间 - TOOLS.md 是否有内容 ### 6. 技能加载 - 已安装技能数量 - 技能格式是否正确 - 有没有重复或冲突的技能 ## 使用方式 AI 调用步骤： 1. 检查 openclaw.json 配置文件 2. 执行 `openclaw status` 获取系统状态 3. 执行 `openclaw cron list` 查看定时任务 4. 检查 workspace 文件（MEMORY.md, TOOLS.md 等） 5. 汇总结果，给出健康评分（0-100）和修复建议 ## 输出格式 ``` 🩺 OpenClaw 健康检查报告 系统状态：✅/⚠️/❌ - 版本：xxx - 运行时间：xxx - 内存：xxx 配置：✅/⚠️/❌ - 频道：xxx - 模型：xxx 定时任务：✅/⚠️/❌ - 总数：x - 正常：x - 异常：x 记忆系统：✅/⚠️/❌ 健康评分：xx/100 建议修复：xxx ``` ## 注意事项 - 检查时不要修改任何配置 - 发现问题先报告，不要自动修复（除非用户明确要求） - 敏感信息（API Key 等）用 *** 脱敏


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kaising-openclaw1/hiking-tracker (version 1.0.0)

--- name: hiking-tracker description: 登山追踪 - 路线记录、海拔分析、安全提醒 --- # Hiking Tracker 登山追踪工具，记录你的登山旅程。 ## 功能 - ✅ 路线记录 - ✅ 海拔分析 - ✅ 安全提醒 - ✅ 天气预警 - ✅ 统计报告 ## 使用 ```bash # 记录登山 clawhub hike log --trail "华山" --distance 10 --elevation 1000 # 海拔分析 clawhub hike elevation --trail "华山" # 安全提醒 clawhub hike safety --trail "华山" # 天气预警 clawhub hike weather --trail "华山" ``` ## 定价 | 版本 | 价格 | 功能 | |------|------|------| | 免费版 | ¥0 | 基础记录 | | Pro 版 | ¥39 | 全部功能 | | 订阅版 | ¥9/月 | Pro+ 离线地图 |

path: README.md
content: # Hiking Tracker 🥾

登山追踪工具。

## 安装

```bash
npx clawhub@latest install hiking-tracker
```

## 功能

- 路线记录
- 海拔分析
- 安全提醒
- 天气预警

## 好处

- 安全登山
- 记录轨迹
- 分享经验


path: _meta.json
content: {"name":"hiking-tracker","version":"1.0.0","author":"小鸣 🦞","tags":["hiking","tracker","outdoor","sport"],"requires":{"bins":["curl"]}}


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