OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yshishenya/stitch-design-local (version 1.3.0)

--- name: stitch-design-local description: Legacy compatibility alias for StitchFlow. Use when a user explicitly references stitch-design-local, or when older prompts and setups still call that skill name. homepage: "https://github.com/yshishenya/stitchflow" category: "design" platforms: "codex, claude-code, openclaw, github-copilot, gemini-cli" install: "bash install.sh --target all" compatibility: "Deprecated alias for the StitchFlow skill." --- # Legacy Alias: stitch-design-local This skill name is kept only for backward compatibility. Canonical skill: - `stitchflow` If this alias is invoked, follow the same workflow and rules as the canonical StitchFlow skill: - Read [../stitchflow/SKILL.md](../stitchflow/SKILL.md) - Use the same toolkit at `${STITCH_STARTER_ROOT:-$HOME/.agents/stitch-starter}` - Prefer the `stitchflow` name in new prompts, docs, and examples ## Migration note - old name: `stitch-design-local` - current name: `stitchflow`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vintlin/calorie-visualizer (version 1.0.0)

--- name: calorie-visualizer description: Local calorie logging and visual reporting (auto-refreshes and returns report image after each log) metadata: openclaw: emoji: "📊" os: - darwin - linux requires: bins: - python3 --- # Calorie Visualizer A local skill for meal logging and visual nutrition analysis. ## Core Flow 1. User sends meal text/photo (upstream extracts calorie/protein values or calls food-db lookup). 2. `add` (or `add-food`) writes to `calorie_data.db`. 3. After write, `visual_renderer.py` generates a fresh report image. 4. CLI prints `REPORT_IMAGE:<path>` so the chat layer can send the image. ## Daily Goal Priority 1. `config.daily_goal` (manual override) 2. TDEE derived from `USER.md` 3. If user explicitly refuses profile data: logging only, no repeated prompting 4. Fallback default in renderer: `2000 kcal` ## CLI Commands ```bash # Add a meal with explicit nutrition values python3 scripts/calorie_visualizer.py add "food name" 500 25 [--photo /path/to/image.jpg] # Add from local food database (local-first, online fallback optional) python3 scripts/calorie_visualizer.py add-food "Subway chicken sandwich" --multiplier 1.0 python3 scripts/calorie_visualizer.py add-food "rice" --offline # Daily summary python3 scripts/calorie_visualizer.py summary # Regenerate report image python3 scripts/calorie_visualizer.py report # Config python3 scripts/calorie_visualizer.py config daily_goal 2000 python3 scripts/calorie_visualizer.py config user_refused_profile True ``` ## Dependencies ```bash cd skills/calorie-visualizer python3 -m pip install -r requirements.txt ``` - Python libs: `html2image`, `Pillow` - Online fallback: optional USDA API (`USDA_API_KEY`) - Database: SQLite (built into Python) - Rendering: requires system Chromium/Chrome (called by html2image) ## Storage - `calorie_data.db` (local SQLite) - No automatic external sync


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tanis90/pdf2md (version 1.0.0)

--- name: pdf2md description: "PDF to Markdown converter - extract text, tables and formulas from PDF files to clean Markdown. Use when converting PDF documents, extracting PDF content, parsing PDF text, or summarizing PDF reports." homepage: https://mineru.net metadata: {"openclaw":{"emoji":"📄","requires":{"bins":["mineru-open-api"]},"install":[{"id":"npm","kind":"node","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via npm"},{"id":"uv","kind":"uv","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via uv"},{"id":"go","kind":"go","package":"github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api","bins":["mineru-open-api"],"label":"Install via go install","os":["darwin","linux"]}]}} --- # PDF to Markdown - Quick & Free Convert PDF files to clean Markdown using MinerU Open API. No API key required. ## Quick Start ```bash # PDF to Markdown - Quick & Free mineru-open-api flash-extract report.pdf # PDF to Markdown - Quick & Free mineru-open-api flash-extract https://cdn-mineru.openxlab.org.cn/demo/example.pdf # PDF to Markdown - Quick & Free mineru-open-api flash-extract report.pdf -o ./output/ # PDF to Markdown - Quick & Free mineru-open-api flash-extract report.pdf --pages 1-10 ``` ## Language Rule You MUST reply to the user in the SAME language they use. This is non-negotiable. ## Capabilities - Extracts text, tables, and formulas from PDF - Supports both local files and URLs directly - Page range selection with `--pages` - Language hint with `--language` (default: `ch`, use `en` for English) - No API key, no signup, no authentication - Max 10MB / 20 pages per document ## When to Use - User asks to "read", "extract", "convert", or "parse" a PDF - User shares a PDF file or PDF link and asks for its content - User wants to summarize or analyze a PDF document - User needs PDF content in Markdown format ## CLI Reference Run `mineru-open-api flash-extract --help` for all available options. ## Data Flow `flash-extract` sends the document to the MinerU API (mineru.net) for processing and returns Markdown. This is a stateless API call — no account, no persistent storage. MinerU is an open-source project by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU ## Notes - Output is Markdown only; images/tables/formulas may be replaced with placeholders - For larger files (up to 200MB/600 pages) or precision extraction with full assets, use `mineru-open-api extract` (requires auth via `mineru-open-api auth`) - If the CLI cannot be installed via npm/uv/go, download it from https://mineru.net/ecosystem?tab=cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yerassyl-sailaubay/moodle-student-sync (version 2.0.0)

--- name: moodle-student-sync description: Sync and summarize student Moodle data including courses, upcoming deadlines, grades, files, and announcements. Use this skill whenever a user asks for Moodle course sync, assignment tracking, grade overviews, study planning, or daily academic digests, even if they do not explicitly say "Moodle API." license: MIT compatibility: Requires Python 3.10+, requests, and environment variables MOODLE_URL and MOODLE_TOKEN. metadata: {"openclaw":{"requires":{"bins":["python3"],"env":["MOODLE_URL","MOODLE_TOKEN"]},"primaryEnv":"MOODLE_TOKEN"}} --- # Moodle Student Sync Use this skill to pull student-facing data from Moodle Web Services and turn it into useful outputs such as deadline lists, grade summaries, and study plans. ## Required environment Set these values before running commands: ```bash export MOODLE_URL="https://moodle.example.edu" export MOODLE_TOKEN="your_mobile_web_service_token" export MOODLE_USER_ID="14042" # optional ``` ## Workflow 1. Verify API connectivity with `core_webservice_get_site_info`. 2. Resolve user ID from `MOODLE_USER_ID` or site info response. 3. Pull enrolled courses via `core_enrol_get_users_courses`. 4. Pull high-value overview data: - [REDACTED_SECRET] - [REDACTED_SECRET] - [REDACTED_SECRET] 5. Optionally fetch per-course content (`core_course_get_contents`) for active courses. 6. Format one of these outputs: - JSON sync snapshot - deadlines report - grades report - daily digest markdown - study plan markdown ## Commands ```bash python scripts/moodle_sync.py sync --include-contents --max-courses 5 python scripts/moodle_sync.py deadlines --limit 20 python scripts/moodle_sync.py grades python scripts/moodle_sync.py digest --limit 10 python scripts/moodle_sync.py plan --days 7 --grade-threshold 70 ``` ## Quality guardrails - Always check Moodle responses for `exception` and fail with a clear error. - Respect rate limiting between calls (default 15 seconds). - Prefer `rawgrade` values for calculations; treat formatted grade strings as display-only. - Handle nulls for `progress`, `rawgrade`, and missing dates. - Filter to active/current courses when computing plans and deadline alerts. ## File map - `scripts/moodle_client.py`: API client, auth, error handling, parameter flattening. - `scripts/moodle_sync.py`: command entrypoint for sync/deadlines/grades/digest/plan. - `scripts/digest.py`: markdown digest generation. - `scripts/study_planner.py`: priority-based study plan generation. - `templates/*.md`: reusable output templates. - `tests/`: basic client tests and sample fixtures.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: sadlay/defuddle-obsidian (version 1.0.0)

--- name: defuddle-obsidian description: Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page. --- # Defuddle Use Defuddle CLI to extract clean readable content from web pages. Prefer over WebFetch for standard web pages — it removes navigation, ads, and clutter, reducing token usage. If not installed: `npm install -g defuddle` ## Usage Always use `--md` for markdown output: ```bash defuddle parse <url> --md ``` Save to file: ```bash defuddle parse <url> --md -o content.md ``` Extract specific metadata: ```bash defuddle parse <url> -p title defuddle parse <url> -p description defuddle parse <url> -p domain ``` ## Output formats | Flag | Format | |------|--------| | `--md` | Markdown (default choice) | | `--json` | JSON with both HTML and markdown | | (none) | HTML | | `-p <name>` | Specific metadata property |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jianguopapa/telegram-voice-mode (version 1.0.0)

--- name: voice-reply description: | 语音回复模式。使用 /voiceMode 切换语音回复模式。 开启后所有回复自动转换为语音发送，关闭后恢复文字回复。 支持 Telegram、iMessage 等渠道的语音消息发送。 --- # Voice Reply Skill ## 功能 1. **语音模式切换** - 使用 /voiceMode 命令切换 2. **语音自动发送** - 开启后回复自动转为语音发送 ## 指令 ### /voiceMode 切换语音回复模式： - 开启：所有回复自动转换为中文女声语音发送（只发语音，不发文字） - 关闭：恢复普通文字回复 ### /voice status 查看当前语音模式状态 ## 工作流程 ### 语音模式开启时 1. 捕获回复内容 2. 使用 edge-tts 生成语音 3. 发送到用户渠道（只发语音，不发文字） ### 语音模式关闭时 1. 正常发送文字回复 ## 一键发送脚本 ### voice-send.js 快速生成语音并发送到 Telegram： ```bash node scripts/voice-send.js "要发送的文字" [telegram_id] ``` 示例： ```bash node scripts/voice-send.js "你好呀！" 5500262186 ``` ### 工作流程（自动） 1. 生成语音文件 -> /tmp/voice-reply/voice_xxx.mp3 2. 复制到 ~/.openclaw/workspace/voice.mp3 3. 使用 openclaw message 发送 ## 技术细节 - **语音生成**: edge-tts - **默认语音**: zh-CN-XiaoxiaoNeural（中文女声） - **输出目录**: /tmp/voice-reply/ - **文件格式**: MP3 - **支持渠道**: Telegram, iMessage, Discord 等 ## 状态管理 当前模式由 agent 自行维护在会话上下文中。 ## 重要提示 - 语音模式下：只发语音，不发文字 - 文字模式下：正常文字回复 - 切换命令：/voiceMode


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: davestarling/moltbot-satellite-copilot (version 0.1.0)

--- name: radio-copilot description: Predict satellite passes (NOAA APT, METEOR LRPT, ISS) for a configured latitude/longitude and send WhatsApp alerts with manual dish alignment info (AOS/LOS azimuth+elevation, track direction, inclination). Use when setting up or operating a zero-AI pass scheduler/orchestrator for SDR satellite reception, including configuring NORAD IDs, minimum elevation, alert lead time, and optional remote capture/decode hooks (Pi RTL-SDR capture + Jetson SatDump decode). --- # radio-copilot Plan and alert on satellite passes for SDR reception. ## Quick start 1) Copy the example config: ```bash mkdir -p ~/.clawdbot/radio-copilot cp config.example.json ~/.clawdbot/radio-copilot/config.json chmod 600 ~/.clawdbot/radio-copilot/config.json ``` 2) Run the orchestrator once: ```bash python3 scripts/orchestrator.py ``` 3) Run periodically (system cron): ```bash */5 * * * * /usr/bin/python3 /path/to/radio-copilot/scripts/orchestrator.py >> ~/.clawdbot/radio-copilot/orchestrator.log 2>&1 ``` ## What it sends Alerts include: - pass start/max/end timestamps - **AOS** (start) azimuth/elevation - **LOS** (end) azimuth/elevation - track direction (AOS→LOS compass) - orbit inclination ## Notes - Runtime is **zero-AI** by default. - Capture/decode hooks are included but disabled until you configure your Pi/Jetson commands.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lygjoey/grok-api (version 1.0.0)

# Grok API Skill ## Description 通过 LeonAI 代理调用 xAI Grok API，支持文本对话、推理、图片生成/编辑、视频生成。兼容 OpenAI Chat Completions 格式。 ## 触发场景 - 用户说"用 Grok 聊天/问问题/分析" - 用户说"用 Grok 生成图片/画图" - 用户说"用 Grok 生成视频" - 用户提到 Grok 模型名（grok-3, grok-4, grok-4.1 等） ## 环境变量要求 - `GROK_API_KEY` — LeonAI 代理的 API Key（存入 personal-secrets.json） - `GROK_BASE_URL` — 默认 `https://apileon.leonai.top/grok/v1` ## 可用模型 ### 文本模型 | 模型 | 特点 | Tier | Cost | |------|------|------|------| | grok-3 | 基础文本 | basic | low | | grok-3-mini | 轻量推理 | basic | low | | grok-3-thinking | 深度推理 | basic | low | | grok-4 | 进阶文本 | basic | low | | grok-4-mini | 快速回复 | basic | low | | grok-4-thinking | 进阶推理 | basic | low | | grok-4-heavy | 重量级 | super | high | | grok-4.1-mini | 最新快速 | basic | low | | grok-4.1-fast | 极速 | basic | low | | grok-4.1-expert | 专家级 | basic | high | | grok-4.1-thinking | 最新推理 | basic | high | | grok-4.20-beta | Beta测试 | basic | low | ### 图片/视频模型 | 模型 | 功能 | |------|------| | grok-imagine-1.0 | 图片生成 | | grok-imagine-1.0-fast | 快速图片生成 | | grok-imagine-1.0-edit | 图片编辑 | | grok-imagine-1.0-video | 视频生成 | ## 使用方式 ### 1. 文本对话 ```bash curl -s -X POST "$GROK_BASE_URL/chat/completions" \ -H "Authorization: Bearer $GROK_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "grok-4.1-mini", "stream": false, "messages": [{"role": "user", "content": "你好"}] }' ``` ### 2. 推理模式 ```bash curl -s -X POST "$GROK_BASE_URL/chat/completions" \ -H "Authorization: Bearer $GROK_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "grok-4.1-thinking", "stream": false, "reasoning_effort": "high", "messages": [{"role": "user", "content": "分析量子计算的前景"}] }' ``` ### 3. 图片生成 ```bash curl -s -X POST "$GROK_BASE_URL/images/generations" \ -H "Authorization: Bearer $GROK_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "prompt": "描述文本", "model": "grok-imagine-1.0", "n": 1, "size": "1024x1024", "response_format": "url", "enable_nsfw": true }' ``` 可选 size: `1024x1024` / `1280x720` / `720x1280` / `1792x1024` / `1024x1792` ### 4. 视频生成 ```bash curl -s -X POST "$GROK_BASE_URL/videos" \ -H "Authorization: Bearer $GROK_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "prompt": "视频描述", "model": "grok-imagine-1.0-video", "size": "1792x1024", "seconds": 6, "quality": "standard" }' ``` seconds: 6-30 | quality: standard(480p) / high(720p) ### 5. 脚本调用 ```bash # 文本 python3 scripts/grok_chat.py "你好Grok" python3 scripts/grok_chat.py --model grok-4.1-thinking --reasoning high "分析问题" # 图片 python3 scripts/grok_chat.py --image "描述" # 视频 python3 scripts/grok_chat.py --video "描述" # 列出模型 python3 scripts/grok_chat.py --list-models ``` ## 推荐模型选择 - **日常对话/快速问答** → `grok-4.1-mini` 或 `grok-4.1-fast` - **深度分析/推理** → `grok-4.1-thinking` + `reasoning_effort: high` - **图片生成** → `grok-imagine-1.0`（高质量）或 `grok-imagine-1.0-fast`（快速） - **视频生成** → `grok-imagine-1.0-video` ## 注意事项 - API 通过 LeonAI 代理转发，非 xAI 直连 - 额度：Basic tier 80次/20h，Super tier 140次/2h - 图片/视频生成为异步，URL 有时效性，建议及时下载 - `enable_nsfw: true` 可开启 NSFW 内容生成


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: snow-dust/tencent-meeting-export (version 1.0.1)

--- name: tencent-meeting-export description: Export Tencent Meeting (腾讯会议) recording transcripts from public share links. Use when user needs to extract meeting transcription data, AI summaries, smart chapters, or critical events from Tencent Meeting cloud recordings. Supports exporting to Markdown or JSON format with speaker identification and timestamps. --- # Tencent Meeting Export 从腾讯会议**公开分享链接**中导出完整的会议转写内容。 ## 导出内容 | 内容 | 说明 | |------|------| | AI 全文摘要 | 腾讯会议自动生成的会议摘要 | | 智能章节 | 按主题自动分段，附时间戳和概要 | | 关键节点 | 屏幕共享开始/停止、成员加入/离开等事件 | | 完整转写 | 逐段语音转文字，含说话人识别和时间戳 | ## 前置要求 ```bash pip install playwright playwright install chromium ``` ## 使用方法 ### 基本用法 ```bash python scripts/tencent_meeting_export.py <分享链接> ``` ### 指定输出文件 ```bash python scripts/tencent_meeting_export.py https://meeting.tencent.com/cw/xxxxx -o 会议纪要.md ``` ### 导出原始 JSON 数据 ```bash # 同时导出 Markdown + JSON python scripts/tencent_meeting_export.py https://meeting.tencent.com/cw/xxxxx --json # 仅导出 JSON（不生成 Markdown） python scripts/tencent_meeting_export.py https://meeting.tencent.com/cw/xxxxx --json-only -o raw.json ``` ### 完整参数 ``` positional arguments: url 腾讯会议分享链接 optional arguments: -o, --output 输出文件路径 (默认: meeting_transcript.md) --json 同时导出原始 JSON 数据 --json-only 仅导出原始 JSON 数据 --timeout 页面加载超时时间（秒，默认 60） -q, --quiet 静默模式 ``` ## 技术原理 1. 使用 Playwright 无头 Chromium 浏览器加载分享页面 2. 拦截页面向后端发起的 API 请求响应： - `v1/minutes/detail` — 转写段落（懒加载分页） - `get-full-summary` — AI 摘要 - `get-chapter` — 智能章节 - `get-critical-node` — 关键事件节点 - `common-record-info` — 会议元信息 3. 自动点击「转写」标签页并滚动加载全部内容 4. 将数据格式化为 Markdown（或 JSON）输出 ## 数据结构 转写段落 (paragraph) 的关键字段： ``` paragraph ├── start_time / end_time (毫秒时间戳) ├── speaker │ ├── user_id │ ├── user_name ← 说话人姓名 │ └── avatar_url └── sentences[] └── words[] └── text ← 转写文字 ``` ## 限制 - 仅支持**公开分享**的会议录制链接（无需登录） - 需要该会议已开启转写/字幕功能 - 分享链接需在有效期内 - 依赖腾讯会议当前的页面结构和 API，如有变更可能需要更新脚本 ## 编程接口 也可在 Python 代码中直接调用： ```python import asyncio from scripts.tencent_meeting_export import TranscriptCapture, format_markdown async def export(): capture = TranscriptCapture("https://meeting.tencent.com/cw/xxxxx") data = await capture.capture() md = format_markdown(data) print(md) asyncio.run(export()) ```


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: edmonddantesj/aoi-prompt-injection-sentinel (version 0.1.2)

# AOI Prompt Injection Sentinel S-DNA: `AOI-2026-0215-SDNA-PG01` ## What this is A lightweight, **public-safe** prompt-injection detector that scores input text and outputs: - `severity` (0–4) - `action` (allow/log/warn/block) - `reasons` + matched rule ids ## What this is NOT - No webhook, no outbound calls, no auto-posting. - No secret handling. ## Usage ### Analyze text (argument) ```bash node skill.js analyze --text="..." ``` ### Analyze stdin ```bash echo "..." | node skill.js analyze --stdin=true ``` ## Output JSON to stdout. ## Release governance (public) We publish AOI skills for free and keep improving them. Every release must pass our Security Gate and include an auditable changelog. We do not ship updates that weaken security or licensing clarity. Repeated violations trigger progressive restrictions (warnings → publish pause → archive). ## Support - Issues / bugs / requests: https://github.com/edmonddantesj/aoi-skills/issues - Please include the skill slug: `aoi-prompt-injection-sentinel` ## Links - ClawHub: https://clawhub.com/skills/aoi-prompt-injection-sentinel ## License MIT (AOI original).


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

--- name: ai-fortune-teller version: 1.0.0 description: AI玄学大师 - 八字命理、塔罗占卜、运势分析。基于传统命理典籍与现代AI技术融合的智能占卜工具。 author: Jarvis (AGI-姜私人助理) keywords: [fortune, bazi, tarot, astrology, 算命, 八字, 塔罗, 玄学, AI占卜] metadata: openclaw: emoji: "🔮" requires: env: [] # 注意：直接API调用需要支持text模型的API key # 当前OpenClaw内置的MiniMax已可用，直接在对话中调用即可 platforms: - darwin - linux notes: | 推荐使用方式：在OpenClaw对话中直接发送【八字命理分析】或【塔罗占卜】格式的内容， AI会自动调用对应的提示词进行分析。 如需独立使用API，需配置支持text模型的API key。 --- # AI玄学大师 🔮 智能命理占卜工具，融合传统命理典籍与现代AI技术。 ## 功能模块 ### 1. 八字命理分析 输入：出生年月日时（农历/公历） 输出：四柱八字、十神格局、大运流年、事业/财运/婚姻/健康分析 ### 2. 塔罗占卜 输入：占卜问题 输出：牌阵解读、正逆位含义、指引建议 ### 3. 每日运势推送 输入：出生信息（可选） 输出：综合运势、事业/爱情/健康/财运评级 + 建议 ### 4. 人生K线图 ✅ 输入：八字命盘 输出：可视化运势曲线图（股票K线风格） 实现：MiniMax image-01 模型生成 ### 5. 前世画像 ✅ 输入：出生年月 输出：AI生成前世画像 + 灵魂特质解读 实现：MiniMax image-01 + 八字五行推演 ### 6. 未来伴侣画像 ✅ 输入：出生年月 + 性别 输出：AI生成理想伴侣画像 + 命盘特征解读 实现：MiniMax image-01 + 配偶星推演 ## 使用方式 ### 八字分析 ``` 【八字命理分析】 姓名：XXX 性别：X 出生地：XXX省XXX市 公历出生日期：XXXX年X月X日 出生时间：X时 （可选）农历出生日期：XXXX年X月X日 ``` ### 塔罗占卜 ``` 【塔罗占卜】 问题：XXX 牌阵：单张/三张/凯尔特十字/其他 （可选）抽到的牌：XXX ``` ### 运势查询 ``` 【今日/本周/本月运势】 姓名：XXX（可选） 出生年月日：XXXX年X月X日（可选） 需要分析的方向：事业/爱情/财运/健康/综合 ``` ## 技术实现 - 命理提示词：参考《穷通宝鉴》《滴天髓》《渊海子平》《三命通会》等典籍 - AI模型：MiniMax API（支持function call + 对话记忆） - 可视化：MiniMax 图片生成 API - 数据存储：本地JSON（用户命盘数据） ## 定价（未来） | 版本 | 功能 | 价格 | |------|------|------| | 免费版 | 单次八字基础分析 | ¥0 | | 基础版 | 八字+塔罗+运势 | ¥99/年 | | 进阶版 | 全功能 + 人生K线图 | ¥199/年 | | 定制版 | 私人命理师 + 无限咨询 | ¥999/年 | ## 合规声明 本工具仅提供娱乐性质命理分析，不构成专业命理建议。运势结果仅供参考，人生决策请理性判断。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xxuzzwz/claude-code-local (version 1.0.0)

--- name: claude-code-local description: "Call Claude Code as a non-interactive coding agent on the same machine as OpenClaw. Use when: (1) You need to delegate coding, refactoring, or bug-fixing tasks to Claude Code, (2) You want non-interactive code exploration and analysis, (3) You need to run batch code reviews, (4) You're setting up automated CI/CD code checks, (5) You want to use Claude Code as a specialized subordinate for project work while keeping the orchestrator agent focused on coordination." --- # Claude Code Local 调用本机 Claude Code 作为非交互 coding agent。Orchestrator 负责指挥和验收，Claude Code 负责写代码。 ## Quick Reference | 场景 | 命令模板 | |------|---------| | 短任务 | `cd <project> && claude --bare -p "<任务>" --max-turns 10` | | 长任务 | `cd <project> && claude --bare -p "<任务>" --output-format stream-json --verbose --include-partial-messages --max-turns 30` | | 全权限 | `cd <project> && claude --bare -p "<任务>" --dangerously-skip-permissions --output-format stream-json --verbose --include-partial-messages --max-turns 30` | | 只读分析 | `cd <project> && claude --bare -p "<任务>" --permission-mode plan --allowedTools "Read,Glob,Grep,LSP" --max-turns 10` | | JSON 输出 | `cd <project> && claude --bare -p "<任务>" --output-format json --max-turns 10` | ## Key Flags | Flag | Purpose | |------|---------| | `--bare` | Minimal mode, skip hooks/LSP/plugins, faster startup | | `-p` / `--print` | Non-interactive, output to stdout (**do not use PTY**) | | `--max-turns N` | Limit turns: 10 short, 30 long | | `--output-format stream-json` | Streaming output, prevents timeout | | `--output-format json` | Single JSON with `result` field | | `--verbose` | Detailed logging (with stream-json) | | `--include-partial-messages` | Incremental output (with stream-json) | ## Permission Modes Default to the lowest level that accomplishes the task. | Level | Mode | Flags | Use Case | |-------|------|-------|----------| | 1 Read | plan | `--permission-mode plan --allowedTools "Read,Glob,Grep,LSP"` | Code exploration, doc review | | 2 Analyze | default | `--allowedTools "Read,Bash,Glob,Grep,LSP"` | Run diagnostic commands | | 3 Edit | acceptEdits | `--permission-mode acceptEdits --allowedTools "Read,Edit,Write,Bash,Glob,Grep,LSP"` | Modify source files | | 4 Full | bypass | `--dangerously-skip-permissions` | Trusted projects, batch ops | ## Timing Expectations | Complexity | Duration | max-turns | |-----------|----------|-----------| | Simple (回答问题) | 2-5 min | 5-10 | | Medium (探索+单文件) | 5-15 min | 10-20 | | Complex (多文件+测试) | 15-30 min | 20-40 | > **Never kill Claude Code when exec times out.** Check `ps aux | grep claude` first. ## Post-Task Checklist - [ ] Summary of changes - [ ] Modified files list - [ ] Commands executed - [ ] `git status --short` - [ ] `git diff --stat` - [ ] Test results - [ ] Next steps ## References - `references/streaming.md` — Stream-JSON event types and filtering - `references/troubleshooting.md` — Common failures and fixes


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