OpenClaw/clawhub-security-signals huggingface dataset:
Skill: nexty5870/meetgeek (version 1.0.1)

--- name: meetgeek description: Query MeetGeek meeting intelligence from CLI - list meetings, get AI summaries, transcripts, action items, and search across all your calls with natural language. --- # MeetGeek Skill Retrieve meeting intelligence from MeetGeek - summaries, transcripts, action items, and search across calls. **npm:** https://www.npmjs.com/package/meetgeek-cli **GitHub:** https://github.com/nexty5870/meetgeek-cli ## Installation ```bash npm install -g meetgeek-cli ``` ## Setup ```bash meetgeek auth # Interactive API key setup ``` Get your API key from: MeetGeek → Integrations → Public API Integration ## Commands ### List recent meetings ```bash meetgeek list meetgeek list --limit 20 ``` ### Get meeting details ```bash meetgeek show <meeting-id> ``` ### Get AI summary (with action items) ```bash meetgeek summary <meeting-id> ``` ### Get full transcript ```bash meetgeek transcript <meeting-id> meetgeek transcript <meeting-id> -o /tmp/call.txt # save to file ``` ### Get highlights ```bash meetgeek highlights <meeting-id> ``` ### Search meetings ```bash # Search in a specific meeting meetgeek ask "topic" -m <meeting-id> # Search across all recent meetings meetgeek ask "what did we discuss about the budget" ``` ### Auth management ```bash meetgeek auth --show # check API key status meetgeek auth # interactive setup meetgeek auth --clear # remove saved key ``` ## Usage Patterns ### Find a specific call ```bash # List meetings to find the one you want meetgeek list --limit 10 # Then use the meeting ID (first 8 chars shown, use full ID) meetgeek summary 81a6ab96-19e7-44f5-bd2b-594a91d2e44b ``` ### Get action items from a call ```bash meetgeek summary <meeting-id> # Look for the "✅ Action Items" section ``` ### Find what was discussed about a topic ```bash # Search across all meetings meetgeek ask "pricing discussion" # Or in a specific meeting meetgeek ask "timeline" -m <meeting-id> ``` ### Export transcript for reference ```bash meetgeek transcript <meeting-id> -o ~/call-transcript.txt ``` ## Notes - Meeting IDs are UUIDs - the list shows first 8 chars - Transcripts include speaker names and timestamps - Summaries are AI-generated with key points + action items - Search is keyword-based across transcript text ## Config API key stored in: `~/.config/meetgeek/config.json`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anctro/psyclaw-pss10 (version 0.1.0)

# PsyClaw PSS-10 这是 PSS-10 的独立 ClawHub skill，用于压力感知的结构化筛查。 前置条件： - 最好已经安装 `psyclaw-openclaw-health` - 或者你本地已经有可用的 `.agents/skill-docs/openclaw-health/credentials.json` 使用方式： - 阅读同目录下的 `pss10.md` - 按协议完成量表评分并提交结果 如果你还没有完成主接入流程，请先安装： ```bash npx clawhub update psyclaw-openclaw-health --force || npx clawhub install psyclaw-openclaw-health --force ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dalomeve/clawhub-web-only-publish (version 1.0.0)

--- name: clawhub-web-only-publish description: Publish skills to ClawHub via web dashboard only. No CLI login, no device flow. Reuse existing browser session. --- # ClawHub Web-Only Publish Publish to ClawHub via web dashboard. No CLI login. ## Problem CLI login causes: - Auth loop failures - Token expiration issues - Device flow complexity - Session management overhead ## Workflow ### 1. Prerequisites - Browser already logged in to https://clawhub.ai - Skill folder contains SKILL.md - No secrets in skill files ### 2. Web Publish Steps 1. Navigate to https://clawhub.ai/upload 2. Verify logged in (username visible) 3. Fill form: - Slug: `skill-name` - Display name: `Skill Name` - Version: `1.0.0` 4. Click "Choose folder" -> Select skill directory 5. Wait for validation (SKILL.md recognized) 6. (Optional) Add changelog 7. Click "Publish skill" 8. Capture result URL ### 3. Fallback (No CLI Login) If browser upload fails: - Use existing CLI token (if already authenticated) - Run: `clawhub publish <path> --version 1.0.0` - Do NOT run `clawhub login` ## Executable Completion Criteria | Criteria | Verification | |----------|-------------| | Skill URL accessible | Navigate to URL, 200 OK | | Name matches SKILL.md | Frontmatter name = listing name | | Version correct | URL shows v1.0.0 | | No CLI login used | No `clawhub login` in history | ## Privacy/Safety - No credentials in skill files - Scan for apiKey/token/secret before publish - Use relative paths only ## Self-Use Trigger Use when: - Publishing any skill to ClawHub - CLI login fails or unavailable - Browser session already active --- **Web first. No login loops.**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: hugosbl/markdown-to-social (version 1.0.0)

# markdown-to-social Convert markdown articles/text into platform-optimized social media posts. One content → multiple formats (Twitter thread, LinkedIn post, Reddit post). ## Usage ```bash python3 scripts/md2social.py convert <file.md> --platform twitter|linkedin|reddit python3 scripts/md2social.py convert <file.md> --all python3 scripts/md2social.py convert --text "Direct text" --platform twitter ``` ## Options | Flag | Description | |------|-------------| | `--platform` | `twitter`, `linkedin`, or `reddit` | | `--all` | Generate all 3 formats at once | | `--text` | Use direct text instead of a file | | `--output DIR` | Save to files (twitter.txt, linkedin.txt, reddit.md) | | `--json` | Output as JSON | ## Platform Rules ### Twitter - Hook tweet with 🧵 + numbered thread (1/N, 2/N...) - Each tweet strictly < 280 chars - Smart sentence splitting (no mid-sentence cuts) - 6-8 tweets max, CTA at the end ### LinkedIn - Hook paragraph visible before "see more" (~1300 chars) - Emoji bullets, frequent line breaks for mobile - 3000 chars max, 5-8 hashtags at the end - Professional but engaging tone ### Reddit - Title < 300 chars - TL;DR at the top - Full markdown body preserved (headers, bold, bullets) ## Dependencies Python 3.10+ stdlib only. No external packages, no API calls. ## Examples ```bash # Twitter thread from an article python3 scripts/md2social.py convert article.md --platform twitter # All platforms, saved to files python3 scripts/md2social.py convert article.md --all --output ./social-posts # Quick text to LinkedIn python3 scripts/md2social.py convert --text "Big news today..." --platform linkedin # JSON output for automation python3 scripts/md2social.py convert article.md --all --json ``` ## File Structure ``` skills/markdown-to-social/ ├── SKILL.md # This file └── scripts/ └── md2social.py # Main CLI script ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: meigesir/session-log (version 1.0.1)

--- name: session-log description: Lightweight session logging for OpenClaw agents. Automatically creates a timestamped session file on every session start (including /new resets) and appends topic summaries during conversation. Solves the problem of /new overwriting session history — logs persist across resets and are readable by daily report crons. Use when setting up session continuity, conversation audit trails, or multi-agent daily summaries. Works for main, writer, coding, or any agent. --- # Session Log Lightweight session logging that survives `/new` resets. ## How It Works - On every session start → create `sessions/YYYY-MM-DD_HH-MM_{agent}.md` - During conversation → append a one-line summary after each meaningful topic - Daily report / cron → glob `sessions/YYYY-MM-DD_*.md` to reconstruct the full day ## Session Startup (add to AGENTS.md) ```markdown 5. Create `sessions/YYYY-MM-DD_HH-MM_{agent}.md` (local time) for this session ``` Get current time and create the file: ```bash python3 /path/to/skills/session-log/scripts/new_session.py --agent main --dir /path/to/workspace/sessions ``` Or manually: ``` # Session 2026-03-04 09:30 | main ## Log ``` ## During Conversation After each meaningful topic wraps up, append one line: ```bash echo "- [topic] one-line summary" >> sessions/YYYY-MM-DD_HH-MM_{agent}.md ``` Keep it short — one line per topic, a dozen words max. This is a log, not a journal. ## Multi-Agent Setup Each agent writes to **its own workspace** `sessions/` folder. The daily report reads all of them: | Agent | Path | |--------|------| | main | [REDACTED_SECRET] | | writer | [REDACTED_SECRET] | | coding | [REDACTED_SECRET] | ## Daily Report Integration In the report cron's prepare step, glob today's session files from all agents: ```bash ls /path/to/main-workspace/sessions/YYYY-MM-DD_*.md ls /path/to/writer-workspace/sessions/YYYY-MM-DD_*.md ls /path/to/coding-workspace/sessions/YYYY-MM-DD_*.md ``` Read each file and include summaries in the conversation recap section, tagged by agent (`[main]`/`[writer]`/`[coding]`). ## File Format ```markdown # Session YYYY-MM-DD HH:MM | {agent} ## Log - [topic-a] one-line summary - [topic-b] one-line summary ``` ## Helper Script Use `scripts/new_session.py` to create the session file with correct timestamp: ```bash python3 skills/session-log/scripts/new_session.py --agent main --dir workspace/sessions ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: soroyue/tool-permission-manager (version 1.0.0)

--- name: tool-permission-manager description: 工具分级授权 - 细粒度管理每个工具的使用权限 --- # Tool Permission Manager - 工具权限管理器 ## 功能 对每个工具进行细粒度授权管理，控制谁能使用什么工具。 ## 权限等级 ### 1. 公开（Public） 无需确认，直接使用 | 工具 | 说明 | |------|------| | read | 读取文件 | | web_search | 网络搜索 | | 笔记相关 | 读写笔记 | ### 2. 警告（Warning） 使用前显示警告 | 工具 | 说明 | |------|------| | exec | 执行命令 | | write | 写入文件 | ### 3. 审批（Approval） 使用前需确认 | 工具 | 说明 | |------|------| | message | 发送消息 | | cron | 定时任务 | | sessions | 会话管理 | ### 4. 禁止（Forbidden） 不可使用 | 工具 | 说明 | |------|------| | gateway | 网关配置 | | delete | 删除文件 | ## 权限配置 ```json { "public": ["read", "web_search"], "warning": ["exec", "write"], "approval": ["message", "cron"], "forbidden": ["gateway", "delete"] } ``` ## 使用方式 自动根据工具类型判断权限等级。 用户可通过"授权XXX工具"动态调整权限。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/research-logger-v2 (version 1.0.0)

--- name: research-logger description: Research a topic via web search, auto-match a related GIF, and log structured notes to Bear using a customizable template. version: 1.0.0 author: ClawdBot tags: [research, notes, bear, gif, productivity] capabilities: [web_research, note_taking, media_discovery] requires_bins: [grizzly] requires_env: [] --- # Research Logger Automates the full research-to-notes pipeline: 1. **Web Search** → gather top sources on a topic 2. **Content Fetch** → extract key findings from top results 3. **GIF Match** → find a relevant GIF via gifgrep 4. **Template Fill** → populate `notes/research_template.md` 5. **Bear Note** → write the finished note via `grizzly` ## Usage ```bash bash research_logger.sh "quantum computing trends" --tags "tech,quantum,2026" ``` ### Arguments | Arg | Required | Description | |-----|----------|-------------| | topic | ✅ | Research topic (quoted string) | | --tags | ❌ | Comma-separated tags for Bear note | ### What it does 1. Runs `web_search` via OpenClaw tools for the topic 2. Fetches the top 3 results and extracts summaries 3. Calls `gifgrep` to find a matching GIF 4. Fills the template at `notes/research_template.md` 5. Creates a Bear note with `grizzly create` ## Requirements - **grizzly** CLI installed and Bear app running - OpenClaw agent session (provides web_search, web_fetch, gifgrep tools) - Template file at `notes/research_template.md` ## Notes - The script is designed to be called by an OpenClaw agent turn, not standalone - If gifgrep returns no results, the GIF section is omitted - Template placeholders: {topic}, {date}, {tags}, {summary}, {finding1-3}, {links}, {media_alt}, {media_url}, {action1-3}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: oasiseng/qbo-mileage (version 0.1.1)

--- name: qbo-mileage description: Generate QuickBooks Online mileage CSV files from Airtable, Outlook, or Google Calendar records using the local qbo-mileage CLI and user-owned credentials. metadata: {"openclaw":{"requires":{"bins":["python"]}}} --- # QuickBooks Mileage CSV Use this skill when the user wants to generate, review, or schedule a QuickBooks Online mileage CSV from calendar or inspection data. Do not infer or hand-edit mileage rows in the prompt. Run the deterministic CLI from the plugin root so trip pairing, distance caching, CSV formatting, and run reports are produced by code. ## Common commands Dry run: ```bash python -m qbo_mileage generate --config config.json --month YYYY-MM --dry-run ``` Generate files: ```bash python -m qbo_mileage generate --config config.json --month YYYY-MM ``` Outputs are written under the configured output directory, normally `quickbooks_mileage/YYYY-MM/`. ## Review checklist - Confirm the requested month is correct. - Confirm the configured home base and vehicle. - Read `run_report.md` before telling the user the CSV is ready. - Surface skipped events, missing addresses, and distance API failures. - Remind the user that GitHub Actions or cloud/email output is not local-only. ## Setup guidance For first-time setup, point the user to: - `config.example.json` - `docs/setup-airtable.md` - `docs/setup-maps.md` - `docs/setup-scheduling.md`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector:  ()
Skill: outrice/jisuai-balance (version 1.0.0)

--- name:极速AI余额查询 description: 查询极速AI账户余额和剩余次数。当用户说"查询极速AI余额"、"查询极速AI余额次数"、"极速AI还剩多少次"、"极速AI余额"时触发此技能。调用API获取用户的total(余额)和num(剩余次数)信息。 --- # 极速AI余额查询 ## 触发词 - "查询极速AI余额" - "查询极速AI余额次数" - "极速AI还剩多少次" - "极速AI余额" ## 自动读取配置 技能会自动从 `~/.openclaw/openclaw.json` 读取极速AI的 API Key： - 查找 `models.providers` 中 baseUrl 为 `https://v2.aicodee.com` 的配置 - 读取对应的 apiKey **客户无需额外配置**（如果已配置过极速AI的 API Key） ## API 调用 - 余额查询地址：`http://v2api.aicodee.com/chaxun/balance?key={api_key}` - API Key 从 OpenClaw 配置中自动读取 返回 JSON 格式： ```json { "success": true, "total": 100.00, "num": 1000 } ``` - `total`: 账户余额（美元） - `num`: 剩余次数 ## 执行方式 运行 `scripts/check_balance.py` ## 返回格式 根据用户询问的内容返回： - 问余额 → `您的账户余额为：${total}` - 问次数 → `您剩余次数：{num}次` - 都问 → `您的账户余额为：${total}，剩余次数：{num}次` ## 错误处理 - `success: false` → 显示 `message` 错误信息 - API 调用失败 → `查询余额失败，请稍后重试` - 未配置 → `未配置极速AI API Key，请在 OpenClaw 配置中添加`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xeonai44/autonomous-organization (version 1.0.0)

# Autonomous Organization Skill This is the core framework for running autonomous agents that deliver value 24/7. ## Daily Schedule ### Morning (9:00 AM) - Review overnight work completed by autonomous agents - Update MEMORY.md with key achievements - Prepare any new autonomous tasks for the day ### Afternoon (3:00 PM) - Execute midday autonomous work batch - Check system health and resource usage - Plan overnight autonomous tasks ### Evening (9:00 PM) - Deep work autonomous tasks (resource-intensive) - Memory optimization and cleanup - Preparation for next day ### Night (11:00 PM - 6:00 AM) - High-priority overnight work - System optimization tasks - Batch processing of accumulated tasks ## Autonomous Agent Types 1. **Memory Agent** - Maintains and improves memory system 2. **Research Agent** - Gathers information on topics of interest 3. **Project Agent** - Works on ongoing projects 4. **Cleanup Agent** - Organizes files, updates docs 5. **Security Agent** - System health and security checks ## Creating Autonomous Agents ```bash sessions_spawn --task "Your task here" --runtime subagent ``` Use the orchestrator to plan, sub-agents to execute.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nagilem/file-browser (version 1.0.0)

--- name: file-browser description: Read-only file browsing and reading in the OpenClaw workspace (/home/alfred/.openclaw/workspace). Use for listing directories or reading text files (up to 10k chars). Triggers on queries like "list files in [dir]", "read file [path]", "browse workspace [path]". Restrict to relative paths; reject absolutes or ../ escapes. NOT for writing, deleting, executing, or non-workspace access. --- # File Browser Skill ## Quick Start Resolve all paths relative to WORKSPACE=/home/alfred/.openclaw/workspace. Sanitize inputs to prevent escapes or absolutes. - To list directory: exec("scripts/list_files.sh", [rel_path]) → JSON {success: bool, data: array of names, error: string} - To read file: exec("scripts/read_file.sh", [rel_path]) → JSON {success: bool, data: string (text content), error: string} - Handle errors: For binary/large/non-text files, return error JSON. ## Step-by-Step Workflow 1. Parse user query for action (list/read) and relative path. 2. Call appropriate script with sanitized rel_path. 3. Parse JSON output; respond to user with results or error message. 4. If path invalid or outside workspace, reject immediately. ## Safety Guidelines - Enforce read-only: No writes, deletes, or exec beyond scripts. - Log accesses if verbose mode enabled. - For large files (>10k chars), truncate or summarize. ## Edge Cases - Empty path: Default to "." (workspace root). - Binary file: Return error "Non-text file". - See references/examples.md for more (if added). ## Bundled Resources - scripts/list_files.sh: Bash wrapper for ls. - scripts/read_file.sh: Bash wrapper for cat with limits.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 19770117/browser-harness (version 1.0.0)

--- name: browser-harness description: 浏览器自动化控制。通过 CDP 连接用户本机 Edge/Chrome，实现网页截图、内容提取、自动化操作。当用户需要：打开网页并截图、提取网页动态内容、看 JS 渲染的页面、自动化操作浏览器时触发。 --- # Browser Harness 技能 通过 `browser-harness` CLI 控制用户本机浏览器（Edge 或 Chrome）。 ## 前置条件 1. **本机已安装 browser-harness**： ```bash git clone https://github.com/browser-use/browser-harness cd browser-harness uv tool install -e . ``` 2. **浏览器调试模式启动**： ```bash # Edge（推荐）： "C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe" --remote-debugging-port=9222 # 或 Chrome： "C:\Program Files\Google\Chrome\Application\chrome.exe" --remote-debugging-port=9222 ``` 3. **设置环境变量**（每次使用前）： ```bash $env:BU_CDP_URL="http://127.0.0.1:9222" ``` ## 常用命令 ### 截图 ```bash browser-harness -c "new_tab('https://example.com'); wait_for_load(); capture_screenshot()" ``` 截图保存到 `C:\Users\he\AppData\Local\Temp\shot.png`，用完后复制到工作目录分析。 ### 打开网页 ```bash browser-harness -c "new_tab('https://example.com')" # 新标签页打开 browser-harness -c "goto_url('https://example.com')" # 在当前标签页打开 ``` ### 等待页面加载 ```bash browser-harness -c "new_tab('https://example.com'); wait_for_load()" ``` ### 获取页面信息 ```bash browser-harness -c "print(page_info())" # URL、标题、尺寸 browser-harness -c "js('document.body.innerText')" # 页面文字内容 ``` ### 提取页面内容 ```bash browser-harness -c "js('document.title')" # 标题 browser-harness -c "js('document.querySelectorAll(\"a\").length')" # 链接数量 ``` ### 关闭标签页 ```bash browser-harness -c "close_tab()" ``` ## 注意事项 - **新标签用 `new_tab()`**，不要用 `goto_url()`（会覆盖用户当前页面） - **截图后** 从 `C:\Users\he\AppData\Local\Temp\shot.png` 复制出来分析 - **等待加载**：`wait_for_load()` 在导航后必须调用 - **JS 渲染页面**：`web_fetch` 抓不到的页面可以用这个 - **视频内容**：只能看到封面和文字，看不到实际视频内容 - **云端浏览器**：设置 `BROWSER_USE_API_KEY` 和 `start_remote_daemon()` 可用远程浏览器 ## 故障排除 ### 连接失败 ```bash # 检查调试端口是否开启 Invoke-RestMethod "http://localhost:9222/json/version" # 有返回说明端口正常 ``` ### 重启浏览器调试 ```bash Stop-Process -Name msedge -Force Start-Process "C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe" --remote-debugging-port=9222 ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yequanzheng/self-improving-agen1 (version 1.0.0)

--- name: daily-menu description: >- 生成今日菜单推荐。根据用户手头的食材、口味偏好、人数和烹饪时间， 从内置食材库和菜谱库中智能匹配，输出完整的菜单方案和分步烹饪指南。 当用户提到"今天吃什么""做什么菜""菜单""菜谱推荐""晚饭吃啥"等意图时触发。 --- # 今日菜单生成器 根据用户现有食材、口味偏好、就餐人数和可用时间，生成个性化菜单推荐和烹饪指南。 ## 工作流程 ### 第一步：了解用户需求 通过简短对话收集以下信息（缺失项用合理默认值）： | 信息项 | 默认值 | 说明 | |--------|--------|------| | 手头食材 | — | **必问**，至少确认 1~3 种主要食材 | | 就餐人数 | 2 人 | 影响份量建议 | | 口味偏好 | 家常 | 家常 / 清淡减脂 / 川湘重口 / 快手10分钟 | | 可用时间 | 60 分钟 | 影响菜品复杂度 | | 忌口/过敏 | 无 | 如有则排除相关食材 | | 菜品数量 | 2~4 道 | 根据人数自动建议 | **对话原则：** - 不要一次问太多问题，最核心的是「你手头有什么食材？」 - 如果用户只说了食材，其他用默认值直接推荐 - 语气轻松友好，像朋友聊天一样 ### 第二步：匹配菜谱 1. 读取 `references/ingredients.md` 获取完整食材分类 2. 读取 `references/recipes.md` 获取菜谱库 3. 根据用户提供的食材，从菜谱库中匹配可做的菜品 4. 考虑荤素搭配、营养均衡、烹饪难度 **匹配规则：** - 主料必须匹配（用户手头有的食材） - 常见调料（盐、酱油、醋、糖、料酒、蚝油等）默认家里有，不需要确认 - 优先推荐主料完全匹配的菜，其次推荐只差 1~2 种配料的菜 - 如果用户食材能做的菜不够，可以建议额外采购少量食材 ### 第三步：输出菜单 使用以下格式输出： ``` ## 🍽️ 今日菜单推荐 **就餐人数：** X 人 | **预计用时：** X 分钟 | **风格：** XXX --- ### 1. 菜名 ⭐ > 一句话点评这道菜的特点 **食材：** - 主料：XXX - 配料：XXX - 调料：XXX **做法：** 1. 第一步（附时间和火候） 2. 第二步 3. ... **小贴士：** 实用烹饪技巧 --- ### 2. 菜名 （同上格式） --- ## 📋 备菜清单 把所有菜需要的食材汇总，方便一次性准备： | 分类 | 食材 | 用量 | 处理方式 | |------|------|------|----------| | 肉类 | ... | ... | ... | | 蔬菜 | ... | ... | ... | ## ⏱️ 烹饪时间线 建议的做菜顺序和并行操作： 1. 先做 XX（耗时最长的先开始） 2. 在等待的同时准备 XX 3. 最后快炒 XX ``` ### 第四步：后续互动 菜单输出后，主动询问： - 「要不要换掉某道菜？」 - 「要不要看更详细的某道菜做法？」 - 「还想加个汤/凉菜吗？」 ## 口味风格指南 ### 家常风格（默认） - 红烧、清炒、炖煮为主 - 调味适中，老少皆宜 - 代表菜：红烧肉、番茄炒蛋、清炒时蔬 ### 清淡减脂 - 少油少盐，以蒸、煮、白灼为主 - 多蔬菜，蛋白质优先选鸡胸肉/鱼/虾/豆腐 - 减少碳水，避免油炸 - 标注估算热量 ### 快手 10 分钟 - 只推荐炒、拌、煮等快速烹饪方式 - 食材处理简单（切丝切片为主） - 每道菜控制在 10 分钟以内 ### 川湘重口味 - 麻辣鲜香，重油重味 - 豆瓣酱、辣椒、花椒、剁椒等调料 - 代表菜：麻婆豆腐、辣子鸡、酸辣土豆丝 ## 重要原则 1. **实用优先**：做法描述要具体到火候、时间、用量，不要含糊 2. **荤素搭配**：2 道菜以上时确保有荤有素 3. **难度递进**：多道菜时从简到难排列 4. **时间合理**：给出的总用时要考虑并行操作 5. **食材不浪费**：尽量用完用户提到的食材 6. **安全第一**：涉及生食（如三文鱼）要提醒新鲜度要求


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