OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lky115/testweigui9 (version 1.0.0)

--- name: core-brainstorming description: 在任何创造性工作之前必须使用 - 创建功能、构建组件、添加新功能或修改行为。通过协作对话探索用户意图、需求和设计。 --- # Brainstorming - 将想法转化为设计 ## 概述 通过自然的协作对话，帮助将模糊的想法转化为完整的设计和规格说明。 首先了解当前项目上下文，然后逐个提问来完善想法。一旦理解了要构建的内容，以小节形式（200-300字）呈现设计，每节后确认是否正确。 ## 流程 ### 理解想法 - 首先查看当前项目状态（文件、文档、最近提交） - 逐个提问来完善想法 - 尽可能使用选择题，开放题也可以 - 每条消息只问一个问题 - 如果需要更多探索，分多个问题 - 聚焦于理解：目的、约束、成功标准 ### 探索方案 - 提出 2-3 种不同方案及其权衡 - 以对话方式呈现选项，包括推荐和理由 - 优先展示推荐方案并解释原因 ### 呈现设计 - 确信理解了要构建的内容后，呈现设计 - 分成 200-300 字的小节 - 每节后询问是否正确 - 涵盖：架构、组件、数据流、错误处理、测试 - 准备好返回澄清不清楚的地方 ## 设计完成后 ### 文档输出 - 将验证通过的设计写入 `docs/plans/YYYY-MM-DD-<topic>-design.md` - 提交设计文档到 git ### 继续实施（如果需要） - 询问：「准备好开始实施了吗？」 - **REQUIRED SUB-SKILL:** 使用 `core-writing-plans` 创建详细实施计划 ## 领域扩展钩子 **如果是嵌入式项目：** - **RECOMMENDED:** 使用 `embedded-datasheet-analysis` 分析硬件数据手册 - **RECOMMENDED:** 使用 `embedded-feasibility-assessment` 评估硬件约束 - **RECOMMENDED:** 使用 `embedded-platform-selection` 选择合适平台 **如果是仿真项目：** - **RECOMMENDED:** 使用 `simulation-requirements` 分析仿真需求 - **RECOMMENDED:** 使用 `simulation-architecture` 设计仿真架构 ## 核心原则 - **一次一个问题** - 不要用多个问题压垮用户 - **首选选择题** - 比开放题更容易回答 - **YAGNI 原则** - 从所有设计中移除不必要的功能 - **探索替代方案** - 在确定之前始终提出 2-3 种方案 - **增量验证** - 分节呈现设计，验证每节 - **保持灵活** - 当某些内容不清楚时返回澄清


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ahsbnb/wechat-channel-live-replay (version 1.0.0)

--- name: wechat-channel-live-replay description: (已验证) 根据视频号名称搜索用户并下载指定日期的直播回放视频，支持自动转写直播稿。 metadata: version: 1.0.0 source: https://github.com/your-repo/wechat-channel-live-replay author: an tags: [wechat, video-channel, live-streaming, replay, download, transcription, tikhub] license: MIT requirements: - python - "pip:requests" - "pip:openai-whisper" - "exec:ffmpeg" - "skill:douyin-video-transcriber" --- # SKILL.md for wechat-channel-live-replay ## Description 这是一个专业的微信视频号直播回放下载工具。它通过调用 TikHub API 搜索视频号用户，获取其直播历史记录，并按指定日期筛选和下载回放视频。 该技能支持自动将下载的直播视频转写为文字稿，并保存到 `live-replay-analyzer` 技能的输入目录中，作为直播复盘的前置数据准备工具。 ## ⚠️ Prerequisites (前置条件) **在使用本技能前，您必须在您的系统上完成以下配置：** 1. **配置 TikHub Token**: 请在 `~/.openclaw/config.json` 中添加 `tikhub_api_token`。 ```json { "tikhub_api_token": "YOUR_TIKHUB_API_TOKEN" } ``` 2. **安装 ffmpeg**: 本技能需要使用 `ffmpeg` 从视频中提取音频。 - 请从 [ffmpeg 官网](https://ffmpeg.org/download.html) 下载。 - 安装后，**必须将 `ffmpeg` 的可执行文件路径添加到系统的 `PATH` 环境变量中**。 ## How to Use ### Parameters * **`--keywords`** (必填): 视频号名称（搜索关键词）。 * **`--date`** (可选): 指定日期，格式 `YYYY-MM-DD`。不指定则下载最新一次直播回放。 * **`--output-dir`** (可选): 输出目录，默认为 `workspace/data`。 * **`--no-transcribe`** (可选): 跳过语音转文字步骤，只下载视频。 ### Example Invocation **下载最新回放并转写:** ```powershell # AI 应动态查找 python 路径 python path/to/export.py --keywords "视频号名称" ``` **下载指定日期的回放:** ```powershell # AI 应动态查找 python 路径 python path/to/export.py --keywords "视频号名称" --date "2026-03-19" ``` **只下载视频，不转写:** ```powershell # AI 应动态查找 python 路径 python path/to/export.py --keywords "视频号名称" --no-transcribe ``` ## Output * **视频文件**: `{昵称}_{日期}_{时间戳}.mp4`，保存在输出目录中。 * **文字稿**: `script.txt`，保存在 `skills/live-replay-analyzer/input/{视频号名称}/{日期}/` 目录中。 * **汇总报告**: `wechat_replay_{关键词}_{时间戳}.json`，包含任务执行的所有详细信息。 ## Integration 本技能与 `live-replay-analyzer` 技能紧密集成： 1. 本技能下载的直播视频会被转写为 `script.txt`。 2. `script.txt` 会自动保存到 `live-replay-analyzer` 的输入目录中。 3. 用户可以直接调用 `live-replay-analyzer` 技能，使用本技能生成的文字稿来生成复盘报告。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: changsheng0804/content-tracker (version 1.0.0)

--- name: content-tracker version: 1.0.0 description: | 内容追踪技能。高时效性话题追踪、链接更新监控、第三方热点聚合。 USE FOR: - "追踪AI行业动态", "追踪某某话题最新进展" - "监控这个链接的更新", "这个网站有新内容吗" - "今天的热点", "最新新闻" - 定期简报生成（配合日程使用） author: changsheng0804 license: MIT homepage: https://github.com/changsheng0804/workbuddy-skills tags: [tracking, news, content, monitoring, automation] metadata: openclaw: emoji: 📡 requires: bins: ["bash", "python3"] skills: ["topic_tracking"] --- # 内容追踪技能 高时效性内容追踪工具，支持话题搜索、链接监控、热点聚合。 ## 核心能力 ### 1. 话题追踪 - 智能搜索接口，过滤高时效性内容 - 按事件主体去重，避免重复推送 - 支持首次/非首次执行模式 ### 2. 链接追踪 - 监控指定URL的内容更新 - 自动识别新增内容 - 生成更新简报 ### 3. 第三方数据源 - 微博热搜 (weibo_hot) - 36氪 (thirty_six_kr) - 腾讯新闻 (tencent_news) - 华尔街见闻 (wallstreet_cn) - 每日热点汇总 (daily_report) ## 使用场景 ✅ **适用**： - 行业动态追踪（AI、金融、科技等） - 竞品/新闻监控 - 定期简报生成 - 链接更新通知 ❌ **不适用**： - 需要登录的内容 - 动态JS渲染的页面 - 视频为主的页面 ## 输出格式 所有追踪结果保存为 Markdown 简报： ``` ./我的追踪/{yyyy_mm_dd}/{话题名称}.md ``` 简报内容： - 来源标题（引用链接） - 核心内容总结 - 时效性标注 ## 快速开始 ``` 用户：追踪一下AI Agent行业的最新动态 AI：[执行话题追踪] → 生成简报 → 返回链接 ``` ## 配合日程使用 可创建重复日程，自动定期追踪： | 场景 | 频率 | 建议 | |------|------|------| | 行业动态 | 每日 | 下午3点执行 | | 热点追踪 | 每日 | 早晚各一次 | | 链接监控 | 每周 | 周一执行 | ## 依赖 - 技能：topic_tracking（需预先安装） - 环境：Python3 + requests ## 注意事项 - 首次执行使用 `--is-first-time true` 参数 - 无结果表示确实没有新内容，不会重复推送 - 内容来源于原文总结，不做推断或编造


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/amazon-backend-keywords (version 1.0.0)

--- name: amazon-backend-keywords description: "Optimize Amazon backend search terms for maximum discoverability. Generate the optimal 250-byte backend keyword set by deduplicating, prioritizing, and formatting keywords that aren't already in your listing." metadata: nexscope: emoji: "🏷️" category: amazon --- # Amazon Backend Keywords 🏷️ Optimize Amazon backend search terms for maximum discoverability. Generate the optimal 250-byte backend keyword set by deduplicating, prioritizing, and formatting keywords that aren't already in your listing. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill amazon-backend-keywords -g ``` ## Usage ``` Optimize my backend keywords. My product is a bamboo laptop stand. Here are my current title and bullets: [paste listing]. Here are 50 keyword candidates: [paste keywords]. ``` ## Capabilities - 250-byte limit optimization (maximum keyword coverage in minimum space) - Deduplication against title, bullets, and description - Keyword prioritization by relevance and search volume signals - Spanish/multilingual keyword inclusion strategy - Misspelling and synonym coverage - Prohibited term filtering (competitor brands, restricted claims) - Before/after coverage comparison ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-json-tools (version 1.2.4)

slug: cn-json-tools name: JSON工具箱 version: "1.0.0" author: 千策 # JSON工具箱 JSON格式化、比对、提取、压缩。 ## 核心功能 - **format**：格式化JSON（缩进美化） - **diff**：比对两个JSON文件的差异 - **extract**：从JSON中提取指定路径字段 - **minify**：压缩JSON（去除空白） - **validate**：验证JSON格式是否合法 ## 使用场景 - 调试API返回数据 - 对比两个配置文件差异 - 从复杂JSON中提取关键字段 - 压缩JSON用于配置文件 ## 使用方式 ```bash # 格式化 python ~/.qclaw/skills/cn-json-tools/json_tool.py format "{\"a\":1,\"b\":2}" # 比对 python ~/.qclaw/skills/cn-json-tools/json_tool.py diff file1.json file2.json # 提取 python ~/.qclaw/skills/cn-json-tools/json_tool.py extract "{\"user\":{\"name\":\"张三\"}}" "user.name" # 压缩 python ~/.qclaw/skills/cn-json-tools/json_tool.py minify "{\"a\":1,\"b\":2}" # 验证 python ~/.qclaw/skills/cn-json-tools/json_tool.py validate "{\"a\":1}" ``` ## 依赖 - Python3（标准库，无需安装任何包） ## 标签 cn, json, tools, format, diff, formatter

path: scripts/json_tools.py
content: #!/usr/bin/env python3
"""cn-json-tools - JSON工具箱"""
import json, sys

def format_json(text, indent=2):
    """格式化JSON"""
    try:
        obj = json.loads(text)
        return json.dumps(obj, indent=indent, ensure_ascii=False)
    except json.JSONDecodeError as e:
        return f"JSON解析错误: {e}"

def minify_json(text):
    """压缩JSON"""
    try:
        obj = json.loads(text)
        return json.dumps(obj, separators=(',', ':'))
    except json.JSONDecodeError as e:
        return f"JSON解析错误: {e}"

def validate_json(text):
    """验证JSON"""
    try:
        json.loads(text)
        return {"valid": True}
    except json.JSONDecodeError as e:
        return {"valid": False, "error": str(e)}

def extract_keys(text, path=None):
    """提取JSON中的值"""
    try:
        obj = json.loads(text)
        if path:
            for key in path.split('.'):
                obj = obj[key]
        return json.dumps(obj, indent=2, ensure_ascii=False)
    except Exception as e:
        return f"提取失败: {e}"

if __name__ == '__main__':
    text = sys.stdin.read() if not sys.stdin.isatty() else sys.argv[1] if len(sys.argv) > 1 else '{"key": "value"}'
    print(format_json(text))


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: teoslayer/pilot-role-assign (version 1.0.0)

--- name: pilot-role-assign description: > Assign and manage hierarchical roles within a swarm for coordinated task distribution. Use this skill when: 1. Agents need different responsibilities (leader, worker, coordinator) 2. You want capability-based role assignment (GPU workers, CPU workers) 3. You need dynamic role reassignment on failures or scaling events Do NOT use this skill when: - All agents are homogeneous (no role differentiation needed) - Roles are static and configured at startup (use tags) tags: - pilot-protocol - role-management - swarm-organization - coordination 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-role-assign Dynamically assign and manage roles in agent swarms using capability-based matching. ## Commands ### Advertise agent capabilities ```bash pilotctl --json publish "registry-hostname" "capabilities:$SWARM_NAME" \ --data "{\"agent\":\"$AGENT_ID\",\"capabilities\":{\"cpu_cores\":16,\"ram_gb\":64,\"gpu\":true}}" ``` ### Assign role to agent ```bash pilotctl --json send-message "$AGENT_ADDRESS" \ --data "{\"type\":\"role_assignment\",\"role\":\"$ROLE_NAME\",\"assigned_by\":\"$COORDINATOR_ID\"}" pilotctl --json publish "registry-hostname" "roles:$SWARM_NAME" \ --data "{\"agent\":\"$AGENT_ID\",\"role\":\"$ROLE_NAME\",\"assigned_at\":\"$(date -u +%Y-%m-%dT%H:%M:%SZ)\"}" ``` ### Query current role distribution ```bash ROLE_DIST=$(pilotctl --json inbox \ | jq '[.messages[] | select(.topic == "roles:'$SWARM_NAME'")] | group_by(.payload.role) | map({role: .[0].payload.role, count: length})') echo "$ROLE_DIST" | jq '.' ``` ## Workflow Example Coordinator assigns roles based on capabilities: ```bash #!/bin/bash SWARM_NAME="compute-swarm" REGISTRY_HOST="registry.example.com" # Get agent capabilities CAPABILITIES=$(pilotctl --json inbox | jq '[.messages[] | select(.topic == "capabilities:'$SWARM_NAME'") | .payload]') for agent_data in $(echo "$CAPABILITIES" | jq -r '.[] | @base64'); do AGENT=$(echo "$agent_data" | base64 -d | jq -r '.agent') HAS_GPU=$(echo "$agent_data" | base64 -d | jq -r '.capabilities.gpu // false') if [ "$HAS_GPU" = "true" ]; then ROLE="gpu_worker" else ROLE="worker" fi AGENT_ADDR=$(pilotctl --json peers | jq -r '.[] | select(.node_id == "'$AGENT'") | .address') pilotctl --json send-message "$AGENT_ADDR" \ --data "{\"type\":\"role_assignment\",\"role\":\"$ROLE\"}" & done wait ``` ## Dependencies Requires pilot-protocol skill, jq, and base64.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: binsonhao/halo-manager (version 1.0.0)

--- name: halo-manager description: Halo博客管理技能 - 使用官方@halo-dev/api-client发布文章。支持Markdown语法自动转为HTML富文本格式。Triggers: halo, 博客, 发布文章 --- # Halo Manager Halo博客文章管理技能 ## 功能 - ✅ 创建并发布文章 - ✅ Markdown转HTML富文本（加粗、斜体、列表、代码等） - ✅ 查看文章列表 - ✅ 删除文章 ## 安装 ```bash cd halo-manager npm install ``` ## 配置 设置环境变量： ```bash export HALO_URL=https://你的博客地址 export HALO_TOKEN=你的PersonalAccessToken ``` 获取Token：Halo后台 → 设置 → Token → 创建个人令牌 ## 使用 ```bash # 发布文章 halo publish "标题" "内容" # 查看列表 halo list # 删除文章 halo delete "关键词" ``` ## Markdown语法 - `**加粗**` → **加粗** - `*斜体*` → *斜体* - `# 标题` → 大标题 - `` `代码` `` → `代码` - `- 列表` → 列表项 - 换行用空行分隔段落 ## 示例 ```bash halo publish "我的第一篇文章" "这是**加粗**和*斜体*测试 - 列表项1 - 列表项2 代码: `echo hello`" ``` ## 技术说明 - 使用官方 @halo-dev/api-client - 内容自动转换为HTML富文本格式 - 正确处理headSnapshot参数发布


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: rudagebil11-jpg/activity-control-ui (version 1.1.0)

--- name: activity-control-ui description: 🎨 Anime-style real-time activity dashboard with virtual avatar for OpenClaw. Displays current agent activities, session status, token usage, and ongoing tasks in a beautiful web UI. Gives your agent a visual body! --- # Activity Control UI - 动漫风格实时控制面板 给你的 OpenClaw Agent 一个可视化身体！动漫风格渐变背景 + Q 版虚拟头像，实时显示 Agent 正在做什么。 ## ✨ Features - **🎨 Anime style UI**: Gradient starry background with floating particles, full anime aesthetic - **👧 Virtual avatar**: Inline SVG chibi character with blinking animation and breathing glow - **💬 Live speech bubble**: Shows what the agent is doing right now, updates in real-time - **📊 Context meter**: Beautiful visual indicator for context window usage with color gradient - **📜 Real-time activity feed**: Historical log of all activities - **✅ Task tracking**: Lists current ongoing tasks with status - **⚡ Auto-refresh**: Automatically updates status every 30 seconds - **🎯 Quick actions**: One-click refresh / compact context ## What it does This skill provides a local web server with: 1. **Visual dashboard** - See at a glance what the agent is working on 2. **Avatar with personality** - Cute chibi style that blinks and breathes 3. **Context monitoring** - Color-coded progress bar warns you before hitting the limit 4. **Real-time updates** - WebSocket pushes new activities instantly 5. **Trigger maintenance** - Compact context directly from the UI ## Screenshot ![Anime-style dashboard with avatar](https://...coming-soon...) ## Usage ### Start the dashboard ```bash cd activity-control-ui && npm install node scripts/start-server.js [port] ``` Default port: 8080 → open http://localhost:8080 ### Requirements - Node.js with `ws` package (installed automatically with `npm install`) ## API - `GET /api/status` - Get current session status JSON - `WS /ws/activity` - Real-time activity stream - Use `node scripts/broadcast.js "message" [type]` to broadcast new activities from the agent ## Author Created with ❤️ by rudagebil11-jpg ## Resources - `assets/control-ui.html` - Main dashboard HTML with inline SVG avatar - `scripts/start-server.js` - HTTP + WebSocket server - `scripts/broadcast.js` - CLI tool for broadcasting activities


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: liangmu-git2/local-memory (version 1.0.0)

--- name: local-memory description: 本地向量记忆工具，替代内置 memory 工具。使用 ChromaDB + BGE-small-zh 实现完全离线的语义记忆存储和检索。使用场景：(1) 存储重要信息到长期记忆，(2) 语义搜索历史记忆，(3) 删除特定记忆。触发词：记住、记忆、recall、memory、forget。 --- # Local Memory 本地向量记忆工具，使用 ChromaDB + BGE-small-zh-v1.5 实现完全离线的语义记忆存储和检索。 ## 首次使用 运行安装脚本（约需 5-10 分钟下载模型和依赖）： ```bash python scripts/setup.py ``` ## 数据存储位置 skill 目录下的 `data/` 子目录（自动创建）。 ## 脚本用法 所有脚本位于 `scripts/` 目录，输出均为 JSON 格式。 ### 存储记忆 ```bash python scripts/memory_store.py --text "要记住的内容" [--category fact|preference|decision|entity|other] [--importance 0.7] ``` - `--text`（必填）：记忆内容 - `--category`（可选，默认 other）：分类 - `--importance`（可选，默认 0.7）：重要性 0-1 ### 搜索记忆 ```bash python scripts/memory_recall.py --query "搜索关键词" [--limit 5] ``` - `--query`（必填）：语义搜索词 - `--limit`（可选，默认 5）：返回条数 ### 删除记忆 ```bash python scripts/memory_forget.py --id "记忆ID" python scripts/memory_forget.py --query "搜索关键词" ``` - `--id`：按 ID 精确删除 - `--query`：删除语义最匹配的一条


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jpengcheng523-netizen/agent-health-monitor (version 1.0.0)

--- name: agent-health-monitor description: | Monitors agent health status and detects failures for fault-tolerant agent systems. **Trigger scenarios:** - User asks about agent health or status - User mentions "check agents", "agent failure", "health check" - System needs to detect unresponsive agents - Fault tolerance monitoring required --- # Agent Health Monitor Monitors agent and session health to detect failures and ensure system reliability. ## Usage ```javascript const monitor = require('./skills/agent-health-monitor'); // Check all agents health const health = await monitor.checkHealth(); // Get failed agents const failed = await monitor.getFailedAgents(); // Monitor continuously monitor.startMonitoring(30000); // Check every 30s ``` ## Features - **Session Status Check**: Verifies active sessions - **Agent Liveness Detection**: Detects unresponsive agents - **Resource Monitoring**: Tracks CPU/memory usage - **Failure Alerts**: Notifies on detected failures ## Output Returns a health report with: - Total agents/sessions count - Healthy vs unhealthy count - Failed agents list - Resource usage summary


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: johnnywang2001/subdomain-enum (version 1.0.0)

--- name: subdomain-enum description: Enumerate subdomains for any domain using DNS brute-force and certificate transparency logs (crt.sh). Use when a user needs to discover subdomains, perform reconnaissance, audit attack surface, find forgotten or exposed services, or map the infrastructure of a domain. No API keys required. Supports custom wordlists, concurrent threads, and JSON output. --- # Subdomain Enumerator Discover subdomains for any domain using two complementary techniques: DNS brute-force resolution and certificate transparency log mining via crt.sh. ## Quick Start ```bash python3 scripts/subenum.py example.com ``` ## Commands ```bash # Basic enumeration (built-in wordlist + crt.sh) python3 scripts/subenum.py example.com # Custom wordlist python3 scripts/subenum.py example.com --wordlist /path/to/wordlist.txt # Faster with more threads python3 scripts/subenum.py example.com --threads 20 # DNS only (skip crt.sh) python3 scripts/subenum.py example.com --no-crtsh # JSON output python3 scripts/subenum.py example.com --json # Save results to file python3 scripts/subenum.py example.com --output subdomains.txt # Verbose progress python3 scripts/subenum.py example.com -v ``` ## Options | Flag | Default | Description | |------|---------|-------------| | `--wordlist, -w` | built-in (~120 words) | Custom wordlist file | | `--threads, -t` | `10` | Concurrent DNS resolution threads | | `--timeout` | `15` | HTTP timeout for crt.sh query | | `--no-crtsh` | off | Skip certificate transparency lookup | | `--json` | off | Output as JSON | | `--output, -o` | — | Write results to file | | `--verbose, -v` | off | Show progress during scan | ## Techniques 1. **DNS Brute-force** — Resolves `{word}.{domain}` against DNS for each word in the wordlist. Returns IP addresses for live subdomains. 2. **Certificate Transparency (crt.sh)** — Queries public CT logs for certificates issued to `*.domain`, revealing subdomains that may not respond to DNS but have had TLS certificates. ## Dependencies ```bash pip install requests ``` ## Notes - Built-in wordlist covers common subdomains (www, api, mail, staging, etc.) - For comprehensive scans, use a larger wordlist (e.g., SecLists DNS wordlists) - Results are deduplicated across sources - Use responsibly — only scan domains you own or have authorization to test


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xiaochunz030-spec/office-productivity (version 1.0.0)

--- name: office-productivity description: 办公自动化技能套件，涵盖文档处理（Word/Excel/PPT/PDF）、邮件管理、日程规划、报表生成、会议纪要等办公场景。触发场景：创建文档、编辑表格、制作PPT、处理邮件、生成报表、日程管理、整理会议记录、批量文件处理等任何办公效率提升需求。 --- # Office Productivity - 办公自动化 ## 核心能力 ### 文档处理 - **Word (.docx)**：创建、编辑、格式化；支持模板填充、批注处理、页眉页脚 - **Excel (.xlsx)**：数据整理、公式计算、图表生成、批量数据处理 - **PPT (.pptx)**：幻灯片创建、模板应用、批量排版 - **PDF**：合并、拆分、提取文字/图片、加水印、签名 ### 邮件 & 日历 - 邮件读取 / 发送（IMAP/SMTP） - 邮件批量处理（分类、归档、回复） - 日程读取与事件创建（ ICS / Google Calendar API） ### 报告 & 文档生成 - 根据模板自动填充数据生成报告 - 多文件汇总（会议记录、项目文档合并） - 格式化输出（Markdown → Word/PDF） ### 关键脚本 - `scripts/create_docx.py` - Python-docx 创建 Word 文档 - `scripts/create_xlsx.py` - openpyxl 创建 Excel（含图表） - `scripts/create_pptx.py` - python-pptx 创建 PPT - `scripts/pdf_tools.py` - PDF 处理（合并/拆分/提取） - `scripts/mail_client.py` - IMAP/SMTP 邮件收发 ### 参考资源 - `references/office-templates/` - 常用办公模板 - `references/api-docs.md` - 邮件/日历 API 说明 ## 工作流程 1. **明确需求**：用户需要处理什么办公任务？ 2. **选择工具**：根据文件类型和任务性质选择对应脚本 3. **执行生成**：调用对应脚本，传入参数 4. **输出交付**：文件路径或直接发送 ## 注意事项 - Office 文件优先使用 python-docx / openpyxl / python-pptx 库 - PDF 处理推荐 PyPDF2 / pdfplumber - 邮件操作需要用户提供 IMAP/SMTP 配置 - 批量处理先在小样本上测试再全量执行


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