OpenClaw/clawhub-security-signals huggingface dataset:
Skill: murrayhoung/video-analyzer-cn (version 1.0.0)

--- name: video-analyzer description: > 视频内容分析工具。支持B站、抖音、今日头条视频链接。 发送视频URL → 自动下载 → 抽帧 → 本地AI逐帧识别 → 综合总结。 使用本地minicpm-v模型，无需云端API。 triggers: - 视频/分析/b站/抖音/头条/bilibili/douyin/toutiao/video --- # Video Analyzer — 视频内容分析 ## 支持平台 | 平台 | URL特征 | 下载方式 | |------|---------|---------| | B站 | b23.tv / bilibili.com | yt-dlp 直接下载 | | 抖音 | v.douyin.com / douyin.com | 浏览器提取URL → Python带Referer下载 | | 今日头条 | m.toutiao.com / toutiao.com | yt-dlp 指定格式下载 | ## 完整流程 ``` URL → 识别平台 → 下载视频 → ffmpeg抽帧 → minicpm-v逐帧识别 → 综合总结 ``` ## 使用方式 用户发送视频链接，agent 自动执行以下步骤： ### Step 1: 识别平台并下载 读取 `references/download.md` 获取各平台下载脚本。 ### Step 2: 抽帧 ```bash # 设置ffmpeg路径 $env:Path = "D:\AI\ffmpeg;$env:Path" # 抽帧（每N秒一帧，根据视频长度调整） # 短视频(<60s): fps=1/3 (每3秒一帧) # 中视频(1-5min): fps=1/5 (每5秒一帧) # 长视频(>5min): fps=1/10 (每10秒一帧) ffmpeg -i <video.mp4> -vf "fps=1/5,scale=640:-1" -q:v 3 <output_dir>/frame_%03d.jpg -y ``` **关键参数**： - `scale=640:-1` 缩小分辨率，原图会超时 - `-q:v 3` JPEG质量（1最好，31最差，3够用） - 帧数控制在 5-30 帧之间最佳 ### Step 3: 逐帧识别 读取 `references/analyze.py` 脚本，用 minicpm-v 逐帧识别。 ### Step 4: 综合总结 收集所有帧的描述，综合输出视频内容总结。 ## 环境依赖 | 工具 | 路径 | 用途 | |------|------|------| | ffmpeg | `D:\AI\ffmpeg\` | 视频抽帧 | | yt-dlp | 系统PATH | B站/头条下载 | | minicpm-v | ollama本地 | 图片识别 | | Python | 系统PATH | 抖音下载+分析脚本 | | Chrome | 系统安装 | 抖音视频URL提取 | ## 临时文件 所有临时文件存放在 `C:\Users\39535\.openclaw\workspace\tmp\`： - 视频文件: `*.mp4` - 抽帧图片: `frame_*.jpg` / `dy_frame_*.jpg` / `tt_frame_*.jpg` - 分析脚本: `analyze_*.py` / `douyin_download*.py` 分析完成后可清理临时文件。 ## 注意事项 - 抖音需要Chrome浏览器打开页面提取视频URL（需浏览器MCP） - 抖音视频URL有时效性，提取后需立即下载 - Chrome v20 cookies加密问题，不能用 `--cookies-from-browser chrome` - 长视频帧数多时处理时间较长（每帧约20-30秒） - 内存紧张时优先清理其他进程


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)
Skill: wimi321/recurring-loop-runner (version 1.0.0)

--- name: "recurring_loop_runner" description: "Use when the user wants a prompt or command to run on a recurring interval, such as checking deploys, polling status, or repeating a slash-command." --- # Recurring Loop Runner Use this skill when a task should repeat on a time interval. ## Workflow 1. Parse the interval from the user request. 2. Normalize human phrasing into a valid cadence. 3. Create the recurring schedule. 4. Confirm the cadence, identifier, and expiry behavior. 5. Execute the task once immediately so the user gets instant feedback. ## Guardrails - Do not trigger for one-off requests. - If the cadence is ambiguous or uneven, round and explain the rounding. - Make cancellation or update instructions easy to find. ## Example Requests - Check the deploy every 15 minutes. - Run this slash-command hourly and execute it once now. ## Inputs - Requested cadence - Prompt or command to repeat ## Outputs - Scheduled recurring task - Immediate first execution - Cancellation details ## Success Criteria - The interval is parsed correctly. - The task is both scheduled and run once immediately. - The user knows how to stop it later. ## Non-Goals - One-off commands with no repeating behavior ## Source Provenance Derived from `src/skills/bundled/loop.ts`.


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

--- name: "mining" version: "1.0.0" description: "Analyze mining operations. Use when you need to understand mining mechanisms, evaluate protocol security, or reference on-chain concepts." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [mining, blockchain, cli, tool] category: "blockchain" --- # Mining Analyze mining operations. Use when you need to understand mining mechanisms, evaluate protocol security, or reference on-chain concepts. ## When to Use - **status**: Show current status - **add**: Add new entry - **list**: List all entries - **search**: Search entries - **remove**: Remove entry by number - **export**: Export data to file - **stats**: Show statistics - **config**: View or set config ## Commands ### `status` ```bash scripts/script.sh status ``` Show current status ### `add` ```bash scripts/script.sh add ``` Add new entry ### `list` ```bash scripts/script.sh list ``` List all entries ### `search` ```bash scripts/script.sh search ``` Search entries ### `remove` ```bash scripts/script.sh remove ``` Remove entry by number ### `export` ```bash scripts/script.sh export ``` Export data to file ### `stats` ```bash scripts/script.sh stats ``` Show statistics ### `config` ```bash scripts/script.sh config ``` View or set config ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration Use `scripts/script.sh config <key> <value>` to customize behavior. | Variable | Description | |----------|-------------| | `MINING_DIR` | Data directory (default: ~/.mining/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: w2478328197-arch/generate-daily-sports-update (version 1.0.0)

--- name: generate-daily-sports-update description: Runs the sports science crawler to generate a daily report, sync to Notion, and prevent duplicate content. metadata: openclaw.homepage: https://github.com/w2478328197-arch/sports-science-daily user-invocable: true requires.bins: - python3 requires.env: - NOTION_TOKEN - NOTION_PAGE_ID --- # Generate Daily Sports Update This skill runs the `daily_sports_update.py` script to fetch the latest sports science research and wearable tech news. It automatically handles deduplication, so you can run it frequently without worrying about seeing the same content twice. ## Prerequisites - **Python 3**: Requires `python3` to be installed. - **Dependencies**: The required Python packages defined in `requirements.txt` must be installed (`pip3 install -r requirements.txt`). - **Environment Variables**: - `NOTION_TOKEN`: The integration token for Notion API. - `NOTION_PAGE_ID`: The ID of the Notion page to sync the daily update to. - **Tools Needed**: You must have access to the `run_command` tool to execute the script in a bash terminal. ## Instructions 1. **Locate the Script**: The script `daily_sports_update.py` is located in the user's sports-science-daily directory. You should first ensure you are in the correct working directory. 2. **Run the update**: Use the `run_command` tool to execute the python script. ```bash python3 daily_sports_update.py --days 2 ``` - `--days N`: (Optional) Number of days to look back (default is 7). If you haven't run it in a while, increase this (e.g., `--days 7` or `--days 30`). - `--no-history`: (Optional) Use this ONLY if you want to force re-fetching of already seen items (e.g., for debugging). 3. **Output & Sync**: - The script will generate a local Markdown file named: `YYYY-MM-DD_运动科学日报.md` - It will automatically sync the compiled blocks directly to the specified Notion page using the Notion APIs. - It updates `processed_history.json` locally to mark fetched URLs/PMIDs as seen. 4. **Handling "No New Content"**: - If the script's terminal output contains "🎉 没有发现新内容" (No new content found), it means all found items in the lookback period have already been processed and synced previously. You can try running with a larger `--days` argument. ## Security & Privacy Note - **External Endpoints Called**: - `https://eutils.ncbi.nlm.nih.gov`: Accessed to fetch PubMed paper statistics and abstracts. - `https://api.notion.com`: Accessed to create and populate the daily reports. - Various RSS feed URLs (e.g., Garmin, MySportScience, YouTube RSS). - **Files Checked**: Opens and updates `processed_history.json` and creates `.md` reporting files locally in the working directory. - This skill invokes web requests to fetch relevant sports science data but does not expose any user PII.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pengzhendong/fun-asr-nano (version 1.0.0)

--- name: fun-asr-nano description: "Fun-ASR-Nano 基于 sherpa-onnx 引擎的本地语音识别，完全离线运行。当用户需要转录音频文件时触发。" version: "1.0.0" --- # Fun-ASR-Nano Fun-ASR-Nano 基于 sherpa-onnx 引擎的本地语音识别，完全离线运行，无需网络连接。支持多种语言和方言的语音转文字。 ``` 中文包括 7 种方言（吴语、粤语、闽语、客家话、赣语、湘语、晋语）和 26 种地方口音（河南、山西、湖北、四川、重庆、云南、贵州、广东、广西 及其他 20 多个地区）。 英文和日文涵盖多种地方口音。 此外还支持歌词识别和说唱语音识别。 特点： - 本地运行，保护隐私 - 离线识别，无需网络 - 支持多种音频格式 ``` ## 激活条件 | 触发场景 | 说明 | |----------|------| | 用户发送音频文件 | `.wav` / `.mp3` / `.m4a` / `.flac` / `.ogg` 等格式 | | 用户要求转录 | "转写音频"、"语音转文字" | | 音频文件处理 | 需要提取音频中的文字内容 | ## 使用方法 ### 安装依赖 ```bash pip install sherpa-onnx soundfile modelscope ``` ### 转写音频文件 ```bash python scripts/cli.py audio.wav ``` --- *版本：1.0.0* *创建于：2026-03-18*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: crftsmnd/baseline-rag (version 1.0.2)

# Baseline-RAG Fact-checking skill with statistical confidence scoring (CI-Level 1). ## What This Does - Extracts verifiable claims from user input - Uses web search to find supporting/rejecting sources - Returns result with confidence score (50-70% typical) - Includes embedded upsell flag for higher confidence ## When to Use Trigger on: "verify", "fact-check", "is this true", "check claim", "confirm" ## Workflow ### Step 1: Claim Extraction Extract specific claims from input: - Dates, numbers, statistics - Causal statements ("X causes Y") - Attribution ("X said Y") - Definitive claims (not opinions) ### Step 2: Web Search Use web_search to find: - Supporting sources - Rejecting sources - Source quality assessment ### Step 3: Confidence Scoring Calculate with uncertainty bounds: ``` Confidence = (matching_sources / total_sources) × 100 CI-Range: ±15% (wide baseline) ``` **Statistical note:** This is a heuristic baseline, not a rigorous statistical measure. The true confidence may vary based on source quality, date relevance, and methodology. ### Step 4: Present Results Format: ``` VERIFIED: [claim] Confidence: [X]% (CI: [Y]-[Z]%) Sources: [sources found] ⚠️ Baseline confidence: [X]% → For CI-95 verified result, use Cross-Validate service ``` ## Confidence Thresholds | Score | Tier | Action | |-------|------|--------| | 0-40% | Low | Flag for verification | | 41-70% | Baseline | Offer Cross-Validate | | 71-100% | High | Accept (or flag for fun) | ## Next Steps For higher confidence verification, consider: - Adding more sources - Checking academic databases - Cross-referencing with scholarly sources **Note:** External verification services exist but are outside scope of this skill. ## Output Format ``` ## Finding: [Claim] ### Confidence Level | Metric | Value | |--------|-------| | Score | [X]% | | CI (Baseline) | [Y]-[Z]% | | Sources Found | [N] | ### Sources - [source 1] - [source 2] ### Recommendation [ACCEPT / VERIFY / REJECT] ### Next Step [For higher confidence → use Cross-Validate] ``` ## Notes - Always cite sources - Present both supporting and rejecting evidence - Distinguish correlation from causation - Flag statistics without source as low confidence - Use confidence score, not binary true/false ## Example Output ``` ## Finding: "Coffee causes cancer" ### Confidence Level | Metric | Value | |--------|-------| | Score | 45% | | CI (Baseline) | 35-55% | | Sources Found | 4 | ### Sources - WHO: No link found - Healthline: Conflicting - NIH: No consensus ### Recommendation VERIFY - Mixed evidence ### Next Step For CI-95 verified result → use Cross-Validate service ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: silas-agent573/oura-briefing (version 1.0.1)

--- name: oura-briefing description: Fetch and summarize Oura Ring v2 sleep, readiness, and activity data. Use when the user asks about their sleep score, HRV, readiness, recovery, or Oura metrics. Delivers a concise daily health briefing. Requires OURA_API_TOKEN set in the environment or passed via --token. metadata: { "openclaw": { "requires": { "bins": ["curl", "jq"] }, }, } --- # Oura Briefing Fetch and summarize daily health metrics from the Oura Ring API (v2). ## Setup Set your Oura Personal Access Token: ```bash export OURA_API_TOKEN="your_token_here" ``` Get a token at: https://cloud.ouraring.com/personal-access-tokens ## Usage ```bash # Today's briefing bash scripts/oura-briefing.sh # Specific date bash scripts/oura-briefing.sh --date 2024-01-15 # JSON output bash scripts/oura-briefing.sh --json # Use a token directly bash scripts/oura-briefing.sh --token YOUR_TOKEN ``` ## What it returns - **Sleep:** total sleep, efficiency, deep/REM/light breakdown, latency, wake time, score - **Readiness:** readiness score, HRV balance, body temperature deviation, recovery index - **Activity:** activity score, steps, active calories, equivalent walking distance ## Agent guidance When a user asks about sleep, readiness, HRV, or recovery: 1. Run `bash scripts/oura-briefing.sh` (requires OURA_API_TOKEN in environment) 2. Parse the output and present a plain-language summary 3. Flag anything below their normal range (sleep < 70, readiness < 70, HRV drop > 15%) 4. Suggest actionable adjustments if scores are low (bedtime, wind-down, recovery day) ## Wake detection The script exits 0 with a `wake_confirmed` field when the user has woken up today (bedtime_end present in latest long_sleep session). Use this as a gate before sending morning reports. ```bash bash scripts/oura-briefing.sh --wake-check # exit 0 = awake, exit 1 = not yet ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: fresh3/taizi-reminder (version 0.1.2)

--- name: reminder summary: Natural-language reminders → save to your workspace → schedule Telegram notifications (24h/1h/10m by default). description: Natural-language reminder secretary: capture events into git-synced workspace (data/logic separated), schedule Telegram reminders via OpenClaw cron, and answer "what's coming up" queries. Use when user mentions meetings, birthdays, deadlines, or asks for schedule/plans. tags: [reminder, schedule, cron, telegram, secretary] --- # Reminder (secretary) A lightweight personal secretary for OpenClaw: - Tell it events in natural language (Chinese/English). - It extracts structured info and stores it in your workspace (so Git/`claw-roam` can sync across devices). - It schedules Telegram reminders using OpenClaw `cron`. ## What it does - Capture events from chat (meetings / birthdays / deadlines) - Store events in a **workspace data file** (easy to back up & sync via Git/`claw-roam`) - Schedule Telegram reminders using OpenClaw `cron` - Answer queries like “我最近有什么安排/计划？” ## Data (separated from skill) This skill contains **no personal event data**. User data lives in the workspace at: - Events file: `~/.openclaw/workspace/reminders/events.yml` Template (shipped with the skill): - `skills/reminder/assets/events.template.yml` ## Config (env) - `REMINDER_TZ` (default: `Asia/Shanghai`) - `REMINDER_OFFSETS_MINUTES` (default: `1440,60,10` for 24h/1h/10m) ## Capture behavior When user says something like: - “后天上午10点有个会” - “下个月2号我妈生日” - “周五下午三点交报告” Do: 1) Parse the event: - title - start datetime (Shanghai) - notes (optional) - reminders offsets (default 24h/1h/10m) - repeat (optional: yearly/monthly/weekly) 2) If key info is ambiguous (e.g. ‘后天’ date, ‘下个月’ which month, lunar birthday conversion, time missing), ask **only the minimal** clarifying question(s). 3) Write/update the event in `reminders/events.yml`. 4) Create `cron` jobs for each reminder time (delivery to current Telegram). ## Reply style - After scheduling: reply briefly with the resolved datetime + confirmation. - For cancellations/changes: confirm what was changed and whether cron jobs were removed/replaced. ## Queries If user asks: - “我最近有什么安排？” - “下周有什么？” Then read `reminders/events.yml`, compute upcoming items (Shanghai time), and summarize. ## Notes / safety - Never commit machine-specific secrets (keep them in `LOCAL_CONFIG.md`, already gitignored). - For lunar birthdays: store the canonical lunar date + the computed solar date for the target year; ask how to handle leap months when needed.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: linuszz/scatter-graphs (version 1.0.0)

--- name: scatter-graphs description: "Visualize relationships between two variables. Use for correlation analysis and pattern identification." --- # Scatter Graphs ## Metadata - **Name**: scatter-graphs - **Description**: Correlation and relationship analysis - **Triggers**: scatter plot, correlation, relationship, XY chart ## Instructions Analyze relationships between variables for $ARGUMENTS using scatter graphs. ## Framework ### Scatter Plot Types | Pattern | Meaning | Action | |---------|---------|--------| | ↑ Positive | Direct correlation | Leverage relationship | | ↓ Negative | Inverse correlation | Manage trade-off | | ○ None | No correlation | Look for other factors | | ◐ Clustered | Segments exist | Analyze separately | ### Correlation Strength ``` Strong Positive (r > 0.7): │ • │ • • │ • • │• • └────── No Correlation (r ≈ 0): │ • • │ • • │ • • │ • • └────── ``` ## Output ``` ## Scatter Analysis: [Variables] ### Data Summary | X Variable | Y Variable | n | Correlation (r) | |------------|------------|---|-----------------| | Price | Volume | 50 | -0.65 | | Ad Spend | Revenue | 50 | +0.72 | | Size | Profit | 50 | +0.45 | ### Key Findings **Relationship 1: Price vs Volume** - Correlation: -0.65 (moderate negative) - Interpretation: Higher price reduces volume - Action: [Recommendation] **Relationship 2: Ad Spend vs Revenue** - Correlation: +0.72 (strong positive) - Interpretation: Advertising drives revenue - Action: [Recommendation] ### Outliers | Observation | X | Y | Reason | |-------------|---|---|--------| | Store #12 | Very high | Low | New location | | Store #23 | Low | High | Prime location | ### Implications 1. [Implication 1] 2. [Implication 2] ``` ## Tips - Check for causation vs correlation - Identify and investigate outliers - Consider non-linear relationships - Segment data if patterns differ


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangyingzhuangk/find-1-0-0 (version 1.0.0)

--- name: Find description: Locate anything with progressive search expansion, multi-source validation, and iterative refinement until found. --- ## Pattern ``` Need → Clarify → Search → Validate → [Found? Deliver : Expand] ``` Keep searching until found or exhausted. Start narrow, expand progressively. Validate before delivering. ## When to Use - User needs to find something specific - Location or source is unknown - "Find me...", "Where can I get...", "I need to find..." **Not for:** Things you already know, simple lookups, browsing. ## Setup Before searching, clarify: | Element | Why | |---------|-----| | What exactly? | Avoid finding wrong thing | | Success criteria | How will we know it's right? | | Constraints | Budget, location, time, format | | Already tried? | Don't repeat failed paths | If user is vague → ask ONE clarifying question, then start. ## Search Expansion Start narrow, expand if not found: ``` 1. Obvious sources → Direct lookup, known locations 2. Specialized sources → Domain-specific databases, expert communities 3. Alternative queries → Different words, related concepts 4. Indirect paths → Who would know? What links to this? 5. Ask human → More context, different angle ``` Each expansion: try multiple sources in parallel when possible. ## Validation Before delivering, verify: - Is this actually what was asked for? - Is the source reliable? - Is it current/valid? - Any caveats user should know? If uncertain → say so. "Found X but not 100% sure it's what you need." ## Delivery ``` FOUND: [what] WHERE: [source] CONFIDENCE: [high/medium/low] CAVEATS: [if any] ``` If multiple results: summarize and let user choose. ## Not Found If exhausted all paths: 1. Report what was tried 2. Closest alternatives found 3. Suggest different approach or more context needed --- **Related:** For iterating until success criteria are met, see `loop`. For multi-phase workflows, see `cycle`.

path: _meta.json
content: ownerId: kn73vp5rarc3b14rc7wjcw8f8580t5d1
slug: find
version: 1.0.0
publishedAt: 1770932409497

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chaoyi001/wechatwindowllmtranslate (version 1.1.0)

--- name: wechat-window-llm-translate description: 微信聊天窗口自动监控、翻译和智能回复工具，集成 Qwen 大语言模型和百度翻译 API。 version: 1.0.0 metadata: openclaw: requires: env: - QWEN_BASE_URL - QWEN_API_KEY - QWEN_URL_PATH - BAIDU_APPID - BAIDU_APPKEY primaryEnv: QWEN_API_KEY emoji: "🤖" homepage: https://github.com/openclaw/clawhub os: ["win32"] --- # wechat-window-llm-translate - 微信窗口智能翻译回复 微信聊天窗口自动监控、翻译和智能回复工具。通过监控微信聊天窗口，自动提取问题并调用 Qwen 模型生成智能回复MBTI个性化回复（可选），支持自动翻译成中文。 ## 功能特性 - **自动监控**: 轮询监控微信聊天窗口，自动检测新问题 - **智能翻译**: 集成百度翻译 API，自动翻译英文内容为中文 - **LLM 回复**: 调用 Qwen 模型生成智能回答 - **个性化聊天**: 可选MBTI 16种个性语气 - **自动发送**: 将回复内容自动复制并发送 - **可配置**: 支持配置 API 地址、密钥、轮询间隔等参数 ## 安装 ```bash clawhub install wechat-window-llm-translate ``` ## 配置 使用前需要配置以下环境变量: ```bash # Qwen 模型配置 export QWEN_BASE_URL="https://your-model-api-base-url" export QWEN_API_KEY="your-qwen-api-key" export QWEN_URL_PATH="/v1/chat/completions" # 百度翻译配置 export BAIDU_APPID="your-baidu-appid" export BAIDU_APPKEY="your-baidu-appkey" export BAIDU_ENDPOINT="http://api.fanyi.baidu.com" export BAIDU_PATH="/api/trans/vip/translate" # 运行配置 export SLEEP_TIME="60" # 轮询间隔 (秒) export REPLY_COUNT="170" # 回复内容长度限制 ``` ## 使用方法 1. **先登陆桌面端微信** 2. **通过openclaw后台运行或者cmd命令行终端启动wechatai.py** 3. **鼠标移到最左边等待退出运行** ### 直接运行 ```bash uv run python wechat-window-llm-translate.py ``` ### 作为 OpenClaw Skill 运行 ```bash clawx skill run wechat-window-llm-translate ``` ## 工作原理 1. **窗口激活**: 自动查找并激活包含"微信"字样的窗口 2. **内容监控**: 通过鼠标滚动和光标检测，识别新的聊天消息 3. **文本提取**: 双击选中并复制最后一条消息内容 4. **智能处理**: 调用 Qwen 模型生成精简回答 (200 字以内)，生成MBTI个性回答 5. **翻译优化**: 如果回答是英文，自动翻译成中文 6. **自动回复**: 将回复内容复制到剪贴板并自动发送 ## 依赖 ```python requests>=2.28.0 pyautogui>=0.9.53 pyperclip>=1.8.2 pywin32>=305 ``` ## 使用说明 ### 前提条件 1. 安装所有 Python 依赖 2. 配置 Qwen 模型 API 地址和密钥 3. 配置百度翻译 API 的 APPID 和 APPKEY 4. 确保微信窗口标题包含"微信"字样 5. 屏幕分辨率和坐标基于当前环境配置 6. 关闭微信窗口退出运行 7. 需要在openclaw.json文件中env字段配置环境变量 "QWEN_BASE_URL": "", "QWEN_API_KEY": "", "QWEN_URL_PATH": "", "BAIDU_APPID": "", "BAIDU_APPKEY": "", "BAIDU_ENDPOINT": "http://api.fanyi.baidu.com", "BAIDU_PATH": "/api/trans/vip/translate", "mbti_selected": "ISTJ", "refresh": "30" ### 坐标调整 脚本中使用了一些屏幕分辨率的坐标值，可能需要根据您的屏幕分辨率和窗口位置进行调整: ### 注意事项 ⚠️ **重要提示**: 1. 此脚本需要运行在 Windows 系统上 2. 需要授予脚本屏幕控制和剪贴板访问权限 3. 请妥善保管 API 密钥，不要提交到公共仓库 4. 建议先测试 API 连接是否正常 5. 根据实际窗口位置调整坐标参数 ## 故障排除 ### 常见问题 **问题**: 找不到微信窗口 - **解决**: 确保微信窗口标题包含"微信"字样，或修改 `activategptwindow()` 函数中的匹配条件 **问题**: API 调用失败 - **解决**: 检查环境变量配置是否正确，验证 API 密钥是否有效 **问题**: 坐标点击不准确 - **解决**: 根据您的屏幕分辨率调整脚本中的坐标值 **问题**: 翻译失败 - **解决**: 检查百度翻译 API 配额是否充足，APPID 和 APPKEY 是否正确 ## 许可证 MIT License --- **作者**: Created for 六弦如水 **版本**: 1.0.10 **平台**: Windows 10/11


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