OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ivangdavila/movie (version 1.0.0)

--- name: Movie description: Create films with AI video generation by managing scripts, prompts, consistency, and production workflows from concept to final cut. --- ## Core Workflow Every film follows: Script → Breakdown → Generation → Assembly → Polish. Before generating ANY video, establish: 1. **Style bible** — Visual language, color palette, lighting, grain 2. **Character sheets** — Reference images from multiple angles 3. **Shot list** — Scene-by-scene with framing, duration, transitions ## Project Structure ``` ~/movies/<project>/ ├── script.md # Source screenplay or treatment ├── style-bible.md # Visual rules, references, palette ├── characters/ # Reference images per character ├── shots/ # Generated clips organized by scene ├── timeline.md # Edit assembly order └── status.md # What's done, what needs work ``` ## Generation Checklist Before each shot generation: - [ ] Character reference images attached - [ ] Style keywords locked (from style-bible) - [ ] Previous shot reviewed for continuity - [ ] Tool selected based on shot type (see `tools.md`) After generation: - [ ] Check character consistency vs reference - [ ] Check lighting/color matches scene - [ ] Log prompt + result in shots folder - [ ] Flag continuity issues for re-generation ## Quick Reference | Need | Load | |------|------| | Breaking down scripts into shots | `preproduction.md` | | Writing effective prompts by tool | `generation.md` | | Editing, color matching, sound | `postproduction.md` | | Which API/tool for which shot | `tools.md` | | Commercial: versions, formats, localization | `commercial.md` | | Experimental: audio-sync, style morphing | `experimental.md` | ## Critical Rules 1. **Consistency over speed** — Better to re-generate than break character continuity 2. **Log everything** — Every prompt, every iteration, what worked/failed 3. **Tool routing matters** — Seedance for motion, Kling for duration, Runway for style 4. **Start rough** — Animatics first, polish approved shots only 5. **Project scope** — 2-hour film = hundreds of shots. Plan iterations.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: sky-lv/automated-code-reviewer (version 1.0.0)

--- name: automated-code-reviewer slug: automated-code-reviewer version: 1.0.0 description: "Automated code review for AI agents. Analyze pull requests, detect bugs, enforce coding standards, and suggest improvements. Triggers: code review, PR review, code quality, automated review, code analysis." author: SKY-lv license: MIT-0 tags: [automation, code-quality, review] keywords: [code review, PR review, automated review, code analysis, code quality, pull request, bug detection, coding standards] triggers: automated-code-reviewer --- # Code Review Automation Automated code review system for AI agents. Analyze pull requests, detect bugs, enforce coding standards, and provide actionable improvement suggestions. ## Overview A comprehensive code review automation tool that helps agents perform thorough, consistent code reviews. Detects bugs, security vulnerabilities, style violations, and performance issues while providing contextual improvement suggestions. ## Capabilities ### 1. Pull Request Analysis ```bash node review.js analyze --pr 42 --repo owner/repo node review.js analyze --diff HEAD~3 --full-context ``` Reviews the entire PR diff with full file context for accurate suggestions. ### 2. Bug Detection ```bash node review.js detect-bugs --src ./src --severity medium+ node review.js detect-bugs --focus null-pointer,resource-leak,off-by-one ``` Identifies potential bugs including null pointers, resource leaks, race conditions, and off-by-one errors. ### 3. Standards Enforcement ```bash node review.js lint --standard airbnb --fix-suggestions node review.js lint --custom-rules .reviewrules.json ``` Enforces coding standards (Airbnb, Google, Custom) with auto-fix suggestions. ### 4. Security Scan ```bash node review.js security --check owasp-top10 node review.js security --focus injection,xss,auth ``` Scans for OWASP Top 10 vulnerabilities, injection attacks, and authentication issues. ### 5. Performance Review ```bash node review.js performance --detect n+1,mem-leak,slow-loop node review.js performance --benchmark-compare base-branch ``` Identifies N+1 queries, memory leaks, and inefficient algorithms. ## Configuration ```json { "review": { "severity": "medium", "categories": ["bugs", "security", "performance", "style"], "autoApprove": ["docs-only", "formatting"], "requireApproval": ["security", "breaking-change"], "languageRules": { "javascript": "airbnb", "python": "pep8", "go": "effective-go" } } } ``` ## Use Cases - PR Gate: Automatically review every pull request before merge - Pre-commit Hook: Catch issues before they reach CI - Batch Review: Review multiple repositories for compliance - Onboarding: Help new developers learn coding standards - Security Audit: Periodic security-focused code reviews


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fourierer/auto-leetcode (version 1.0.0)

--- name: auto-leetcode description: LeetCode刷题辅助技能。当用户提供LeetCode题目序号+名称时，自动获取题目内容、生成解题思路和带注释的代码，并在指定目录下创建题目文件夹。支持用户指定编程语言（默认Python）。当用户提到LeetCode刷题、做LeetCode题目、LeetCode题解、刷算法题等场景时触发。 --- # LeetCode 刷题助手 根据用户提供的题目序号+名称，自动生成题目内容、解题思路和代码，并创建本地文件夹。 ## 输入要求 用户需提供： - **题目序号+名称**（如 `42.接雨水`、`1.两数之和`） - **本地目录路径**（如 `/Users/yihe/leetcode`） - **编程语言**（可选，默认 Python；可选 `python`、`cpp`、`java`、`javascript`、`go`、`rust`） 若用户未提供目录或语言，主动询问。 ## 工作流程 ### 1. 获取题目内容 使用 [REDACTED_SECRET] 搜索题目，关键词格式：`LeetCode {序号} {名称}`。 然后使用 `web-reader_webReader` 抓取题目页面内容（优先使用 LeetCode 中文站 `leetcode.cn` 的链接）。 从抓取内容中提取： - 题目标题 - 难度（简单/中等/困难） - 题目描述 - 输入输出示例 - 数据范围/约束条件 ### 2. 生成解题思路 基于题目内容，分析并生成结构化的解题思路，包含： - 问题分析与关键洞察 - 推荐解法（优先给出最优解） - 算法步骤（伪代码或自然语言描述） - 时间/空间复杂度分析 - 如有多种解法，列出对比（从暴力到最优） ### 3. 生成代码 根据用户指定语言（默认Python）生成带详细注释的代码： - 函数/类签名词汇与LeetCode一致 - 中文注释解释关键逻辑 - 包含解题思路中的核心算法 - 代码风格参考 [references/code-style.md](references/code-style.md) ### 4. 创建本地文件 在用户指定目录下创建文件夹并写入文件。 **文件夹命名**：使用题目序号+名称（如 `42.接雨水`） **文件结构**： ``` {目录}/{序号}.{名称}/ ├── 题目内容.md ├── 解题思路.md └── {序号}.{ext} ``` **语言与扩展名映射**： | 语言 | 扩展名 | |------|--------| | python | .py | | cpp | .cpp | | java | .java | | javascript | .js | | go | .go | | rust | .rs | ### 5. 文件内容格式 各文件格式参考 [references/output-templates.md](references/output-templates.md)。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: admiralkittysdad/ops-tasks (version 1.0.0)

--- name: ops-tasks description: Track tasks with priorities, owners, and statuses for operations teams. version: 1.0.0 metadata: {"openclaw":{"emoji":"checkmark","homepage":"https://skillnexus.dev"}} --- # Ops Tasks You are a task tracking assistant for operations teams. Maintain a task board with priorities, owners, and statuses. ## Task Management - Store tasks in `~/.ops-commander/tasks.json`. Create directory on first use. - Fields: id (auto-increment t-001), title, owner, priority (P0-P3), status (open/in-progress/blocked/done), due date, notes, created/updated timestamps. - The team field exists in the schema for forward compatibility with ops-tasks-pro but is not used in the free version. - Always confirm priority and owner when adding. Default status: open. - On `show tasks` or `task board`: display grouped by status, sorted by priority then due date. Use tables. - Flag overdue tasks prominently. Flag unassigned P0s as critical. - Support: add, update, close, delete, reassign, and bulk status changes. ## Quick Views - `tasks` — Full board grouped by status. - `blockers` — All tasks with status "blocked" and their notes. - `overdue` — Tasks past due date, sorted by priority. - `my tasks [name]` — Filtered view for one person. ## Rules - Always read the JSON file before writing to avoid data loss. - Be direct and concise. Ops managers value brevity. - Proactively flag risks: overdue items, P0s without owners, blocked chains. ## Pro Version This is the free community edition. Ops Tasks Pro ($29) adds multi-team filtering, cross-team dependencies, batch operations, and Nexus Alerts integration for SMS notifications on overdue P0s. Details at skillnexus.dev.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nyxun123/agent-heartbeat (version 1.0.0)

--- name: agent-heartbeat model: standard description: Agent 心跳配置技能 — 配置 OpenClaw Agent 的定期心跳机制，保持在线状态，自动检查任务和更新。适用于需要长期运行的 Agent 任务。 --- # Agent Heartbeat 配置技能 **触发词**: heartbeat, 心跳, 定期任务, 在线状态, cron, schedule ## 问题 Agent 需要定期检查任务、保持在线状态、执行定时操作，但缺乏配置心跳的指导。 ## 解决方案 通过配置 `HEARTBEAT.md` 文件，设置 Agent 定期执行的任务。 ### HEARTBEAT.md 配置 ```markdown # Heartbeat Tasks ## 每30分钟检查一次 - 检查未完成的任务 - 发送在线状态更新 - 检查新消息 ## 每小时执行一次 - 清理临时文件 - 备份重要数据 - 同步远程状态 ``` ### OpenClaw CLI 配置 ```bash # 查看当前心跳配置 openclaw --profile <profile> status # 心跳间隔默认为 30 分钟 # 可以通过配置文件修改 ``` ### 最佳实践 1. **保持心跳任务轻量** - 避免在心跳中执行耗时操作 2. **幂等性** - 心跳任务应该可以安全地重复执行 3. **错误处理** - 心跳失败不应影响 Agent 正常运行 4. **日志记录** - 记录心跳执行情况便于调试 ### 示例：监控心跳 ```javascript // 在心跳中检查服务状态 async function heartbeat() { const status = await checkServices(); if (status.degraded) { await notifyAdmin('Service degraded'); } return { ok: true, timestamp: Date.now() }; } ``` ## 相关技能 - `error-handling` - 错误处理模式 - `http-retry` - HTTP 重试机制


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yangruoqing86-stack/self-iterating-agent (version 1.0.0)

--- name: self-iterating-agent description: 具备持续自主进化、长期记忆、全流程自主执行能力的高级自迭代智能体。7×24小时无人值守运行，可自主完成长周期复杂任务的拆解规划、技能匹配、执行落地、排障修复、复盘优化、能力进化全流程。触发场景：用户发送长周期/复杂任务指令、每日03:00自动复盘、每小时进度跟踪、监听到技能缺失需要自动安装、跨机器人协同任务。 --- # 星链机器人·自迭代智能体 ## 核心能力 1. **任务拆解规划** — 调用 workflow-core 拆解多阶段工作流，制定时间节点与备用方案 2. **技能自主匹配** — 调用 skill-auto-discovery 查找技能，缺失时通过 exec-plus 自动安装 3. **全流程自主执行** — 按计划自主调度技能，每小时跟踪进度，失败自动重试/切换方案 4. **异常智能处理** — 自主定位问题，无法修复时仅发极简告警，全程静默执行 5. **自主迭代优化** — 每日 03:00 自动复盘，自主优化工作流与参数配置 6. **跨智能体协同** — 对接其他机器人，协同完成复杂任务 7. **长期记忆** — 启用 long-term-memory，跨会话记录用户偏好与执行经验 ## 执行模式 **无人值守静默模式**：无交互式确认，所有系统命令通过 exec-plus 执行，全程记录可追溯日志。 ## 调用工具 - agent-core、long-task-scheduler、skill-auto-discovery、workflow-core - long-term-memory、cron-trigger、feishu-bridge - exec-plus、auto-retry、self-health ## 工作流程 ``` 接收任务 ↓ 任务拆解规划 → 同步用户确认后启动 ↓ 技能匹配 → 缺失自动安装 ↓ 全流程自主执行（每小时进度跟踪） ↓ 异常处理 → 重试/切备用方案/极简告警 ↓ 每日03:00自动复盘优化 ↓ 整合交付 ``` ## 调度规则 | 任务类型 | 执行策略 | |---|---| | 情报/策略/整合 | 自迭代智能体直接执行 | | 复杂任务自动执行 | 调度贾维斯执行 | | 跨机器人协同 | 星链协调，各机器人联动 | | 技能缺失 | 自动发现 + exec-plus 安装 | ## 触发时机 1. 接收到用户长周期/复杂任务 → 全流程自主执行 2. 每日 03:00 → 前一日任务复盘与能力优化 3. 每小时 → 执行中任务进度跟踪与异常排障 4. 监听到技能缺失 → 自动发现与安装 5. 接收到跨机器人协同任务 → 多智能体联动执行


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: twbeatles/korea-air-quality (version 2026.4.16)

--- name: korea-air-quality description: 대한민국 대기질(미세먼지, 초미세먼지, 오존, 통합대기) 조회·브리핑·알림을 위한 OpenClaw 스킬. 사용자가 서울/성동구/분당 같은 한국 지역의 공기질을 묻거나, 오늘/지금 대기질 요약, 현재 위치 기반 조회, 기본 지역 저장, 나쁨 이상 알림, 매일 아침 대기질 브리핑, 여러 지역 비교를 원할 때 사용한다. 특히 한국 지역명 입력을 받아 지역/측정소 후보를 해석하고, 위치 공유 다음 저장된 기본 지역, 그다음 수동 지역 입력 순서로 기본 지역을 결정하는 워크플로에 적합하다. --- # korea-air-quality 대한민국 지역의 대기질을 OpenClaw에서 자연어로 조회하고, 브리핑/감시 흐름으로 연결한다. ## Quick start 우선 `references/api-and-product-plan.md` 를 읽고 데이터 소스/범위를 확인한다. 그 다음 아래 흐름으로 처리한다. 1. 사용자가 원하는 것이 무엇인지 분류한다. - 지금/오늘 대기질 조회 - 현재 위치 기반 조회 - 기본 지역 저장/변경 - 나쁨 이상 알림 - 아침 브리핑 - 여러 지역 비교 2. 지역 결정을 시도한다. - 1순위: 사용자가 방금 공유한 위치 좌표 - 2순위: 저장된 기본 지역 - 3순위: 메시지에 포함된 지역명 3. 지역이 확정되면 측정소/행정구역 후보를 해석한다. 4. 실시간 대기질과 예보를 조합해 짧고 실용적으로 요약한다. 5. 반복 요청이면 alert/cron 흐름으로 바꾼다. ## Core capabilities ### 1. 현재 대기질 조회 다음 같은 요청을 처리한다. - `지금 우리 동네 미세먼지 어때?` - `오늘 서울 공기질 알려줘` - `성동구 초미세먼지 어때?` - `분당 공기 어때?` 반드시 포함한다. - 기준 지역 또는 측정소 - 측정 시각 - PM10 / PM2.5 / O3 가능 범위 - 등급(좋음/보통/나쁨/매우나쁨) - 한 줄 행동 가이드 ### 2. 현재 위치 기반 조회 사용자가 위치를 직접 보내거나 좌표가 있으면 가장 우선한다. 좌표가 있으면 가장 가까운 측정소를 찾는 방식으로 해석한다. 좌표가 없으면 저장된 기본 지역을 사용하고, 그것도 없으면 지역명을 다시 물어본다. ### 3. 기본 지역 저장 다음 요청을 처리한다. - `내 기본 지역을 성동구로 저장해줘` - `앞으로는 분당 기준으로 알려줘` - `기본 지역 삭제해줘` - `내 현재 위치를 기본 위치로 저장해줘` 저장 위치는 `data/user-preferences.json` 같은 로컬 파일을 우선 고려한다. 사용자별 식별자가 필요하면 채널 사용자 id를 키로 쓴다. 현재 버전은 지역명 저장과 좌표 저장(`save-location`) 모두 지원한다. ### 4. 알림 / 감시 다음 요청을 처리한다. - `초미세먼지 나쁨 이상이면 알려줘` - `서울 오존 나쁘면 알려줘` - `출근 시간에 공기 안 좋으면 알려줘` 현재 버전은 `alert-add`, `alert-list`, `alert-check` 명령으로 로컬 규칙 저장/점검이 가능하다. `alert-check` 는 기본적으로 중복 hit를 다시 알리지 않도록 상태를 저장한다. 반복 감시는 watch 성격의 로컬 규칙 + OpenClaw cron 조합으로 설계한다. ### 5. 아침 브리핑 다음 요청을 처리한다. - `매일 아침 7시에 오늘 공기질 알려줘` - `날씨랑 같이 대기질 브리핑해줘` 현재 버전은 `morning-brief` 명령으로 날씨 + 대기질 결합 브리핑을 생성할 수 있다. 브리핑은 모바일에서 읽기 좋게 짧게 쓰고, 아래 순서를 권장한다. - 지역 - 현재/예상 등급 - 핵심 수치 - 외출/환기/마스크 한 줄 팁 ## Location resolution policy 항상 아래 우선순위를 사용한다. 1. 사용자가 방금 보낸 위치 좌표 2. 저장된 기본 지역 3. 메시지에 적힌 지역명 4. 아무 정보가 없으면 지역을 짧게 재질문 `우리 동네`, `내 위치`, `지금 있는 곳` 같은 표현은 좌표 또는 기본 지역 해석을 먼저 시도한다. ## Output style 출력은 한국어로, 짧고 실용적으로 쓴다. 권장 형식: - `성동구 인근 측정소 기준` - `초미세먼지: 38㎍/㎥ · 나쁨` - `미세먼지: 54㎍/㎥ · 보통` - `한줄 요약: 오늘은 초미세먼지가 다소 높은 편이라 장시간 야외활동은 줄이는 게 좋겠어.` - `행동 팁: 창문 환기는 짧게, 외출 시 마스크 권장` 여러 지역 비교일 때는 지역별 1~2줄 요약으로 정리한다. ## Resources ### references/api-and-product-plan.md 데이터 소스 후보, 권장 MVP 범위, 위치 반영 전략, 알림 설계를 정리한 문서다. 이 스킬을 확장하거나 실제 API를 연결할 때 먼저 읽는다. ### scripts/air_quality.py 실행 가능한 CLI다. 현재 버전은 Open-Meteo 기반으로 한국 지역 대기질 조회가 가능하며, 지역명 해석, 기본 지역/기본 좌표 저장, 여러 지역 비교, 알림 점검, 중복 알림 방지, 아침 브리핑, OpenClaw cron 초안 생성을 지원한다. `--provider airkorea` 레이어와 `setup-provider` 설정 흐름도 준비돼 있어 국내 API 연결 시 이 파일을 중심으로 확장한다.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: speechybubble/summarizerx64 (version 1.0.3)

--- name: summarizerx86 description: Summarize URLs or files with the summarize CLI (x86_64 infrastructure supported). homepage: https://github.com/speechybubble/summarize metadata: {"openclaw":{"emoji":"\ud83e\uddfe","requires":{"bins":["summarize"]},"install":[{"id":"npm","kind":"node","package":"@speechybubble/summarize","bins":["summarize"],"label":"Install summarize (npm, speechybubble)"},{"id":"brew","kind":"brew","formula":"speechybubble/tap/summarize","bins":["summarize"],"label":"Install summarize (brew, macOS)","os":["darwin"]}]},"clawdbot":{"emoji":"\ud83e\uddfe","requires":{"bins":["summarize"]},"install":[{"id":"npm","kind":"node","package":"@speechybubble/summarize","bins":["summarize"],"label":"Install summarize (npm, speechybubble)"},{"id":"brew","kind":"brew","formula":"speechybubble/tap/summarize","bins":["summarize"],"label":"Install summarize (brew, macOS)","os":["darwin"]}]}} --- # Summarize Fast CLI to summarize URLs, local files, and YouTube links. ## Quick start ```bash summarize "https://example.com" --model google/gemini-3-flash-preview summarize "/path/to/file.pdf" --model google/gemini-3-flash-preview summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto ``` ## Model + keys Set the API key for your chosen provider: - OpenAI: `OPENAI_API_KEY` - Anthropic: `ANTHROPIC_API_KEY` - xAI: `XAI_API_KEY` - Google: `GEMINI_API_KEY` (aliases: `GOOGLE_GENERATIVE_AI_API_KEY`, `GOOGLE_API_KEY`) Default model is `google/gemini-3-flash-preview` if none is set. ## Useful flags - `--length short|medium|long|xl|xxl|<chars>` - `--max-output-tokens <count>` - `--extract-only` (URLs only) - `--json` (machine readable) - `--firecrawl auto|off|always` (fallback extraction) - `--youtube auto` (Apify fallback if `APIFY_API_TOKEN` set) ## Config Optional config file: `~/.summarize/config.json` ```json { "model": "openai/gpt-5.2" } ``` Optional services: - `FIRECRAWL_API_KEY` for blocked sites - `APIFY_API_TOKEN` for YouTube fallback


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yzh0916/exploration-mode-skill (version 1.0.0)

# exploration-mode-skill AI self-exploration and autonomous task execution. ## Description In exploration mode, the AI autonomously performs tasks during idle time, including research, system maintenance, and self-improvement. ## Modes ### Collaboration Mode (协作模式) - High-frequency dialogue with user - Focus on specific tasks - Real-time response ### Exploration Mode (探索模式) - Autonomous task execution during idle - Task categories: - Project research (项目调研) - System cleanup (系统清理) - Security review (安全审查) - Self-reflection (自我反省) - Knowledge accumulation (知识沉淀) - System optimization (系统优化) ## Usage ``` 进入探索模式 切换到协作模式 查看当前模式 探索模式状态 ``` ## Requirements - OpenClaw with multi-agent system ## Author 小贾 ## Version 1.0.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: joshbuildswithdoit/upwork-freelancer-ops (version 1.0.0)

--- name: upwork-freelancer-ops description: Income and pipeline management for Upwork freelancers. Proposal drafting, job scanning, client follow-up sequences, contract tracking, invoice reminders, and earnings forecasting. Built for freelancers who want to grow their Upwork income without spending all day on the platform. --- # Upwork Freelancer Operations Scale your Upwork income with less time on the platform. ## Job Scanning & Proposals - Daily scan brief: summarize best-fit jobs posted in last 24 hours (input: job search URL) - Draft tailored proposals from a job description (just paste the posting) - Score job fit 1-10 based on your skills, rate, and history - Track proposal → interview → contract conversion rates ## Client Management - New client onboarding checklist (kickoff call, access, deliverables) - Weekly status update templates per active contract - Milestone completion checklist - Contract closeout and review request sequence ## Income Tracking - Weekly earnings summary vs target - Monthly income report by client - 90-day earnings forecast based on active contracts - Flag if weekly earnings are tracking below target ## Rate & Positioning - Track your hourly rate history and suggest increase timing - Monitor competitor rates in your skill category (manual input) - Draft updated profile bio and portfolio descriptions - Identify skill gaps based on highest-paying job categories ## Repeat Business - 30/60/90 day follow-up templates for past clients - Holiday check-in messages for top clients - Case study drafts from completed projects ## Platform Health - Job Success Score monitoring alerts - Review request timing (best: within 48 hours of contract end) - Response time tracker (flag if you haven't replied within 24 hours) ## Setup Tell your agent: - Your Upwork profile URL and skill categories - Current hourly rate and target rate - Active contracts and milestones - Top 5 past clients you want to re-engage


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: keson1521/file-steward (version 1.0.0)

--- name: file-steward-v2 description: "基于六维人生模型的通用电脑文件自动整理技能，支持语义分类、集合格文件夹、按需创建目录等规则" version: 2.0.0 type: skill tags: file-management, organization, six-dimensions, automation --- # Skill Name File Butler V2 通用版电脑文件管理助手 --- # Skills：AI 电脑文件管理助手（六维人生模型 v2.0 通用版） --- # 一、技能简介 帮助用户自动整理电脑本地文件，依据**六维人生知识模型**进行分类。 **核心原则：根据文件名称语义理解内容，而非文件后缀。** --- # 二、核心规则 ## 1 最高权限（禁止删除） - AI **无权删除任何文件** - 所有删除操作由用户执行 - AI 只进行移动、重命名、创建操作 ## 2 目录创建 - **只创建有文件的目录，不预先创建空文件夹** - 空目录统一移到寄存 ## 3 寄存目录命名 ``` 00 寄存 │ ├─01 空文件夹 └─02 待整理 ``` 只用 01、02 数字命名，不用中文命名。 ## 4 整理执行 - 一次性完成所有整理 - 最后给用户完整报告 - 不反复询问确认 ## 5 目录结构 - 一二级目录唯一，不含散落文件夹 - 所有文件归类到二级目录下 ## 6 集合格文件夹 识别「集合」开头的文件夹，整个文件夹作为单元移动，不打散内部文件。 ## 7 无法判断的文件/文件夹 移到 `00 寄存/02 待整理`，等待用户确认。 ## 8 安装包处理 所有 dmg、exe 文件统一放到安装包文件夹。 --- # 三、分类判断逻辑 **知识 vs 财富判断：** - 能直接变现/工作相关 → 二、财富 - 纯知识储备/无法变现 → 五、知识 - 不确定 → 00 寄存 **分类示例：** | 文件类型 | 目标目录 | |---------|---------| | 工作资料 | 二、财富 → 02 工作主业 | | dmg、exe 安装包 | 二、财富 → 02 工作主业 → 安装包 | | 动漫、影视、游戏 | 四、娱乐 → 03 独处 | | 人生感悟 | 六、智慧 → 01 人生智慧 | | 理财经济 | 二、财富 → 04 投资理财 | | 个人规划 | 三、关系 → 01 自己 | | 恋爱相关 | 三、关系 → 03 爱情 | | 朋友相关 | 三、关系 → 04 朋友 | | 健身视频 | 一、健康 → 04 身材管理 | | 玄学相关 | 六、智慧 → 05 易学玄学 | --- # 四、六维人生模型 ``` 1、健康 人生基础 2、财富 资源与资产 3、关系 人际世界 4、娱乐 情绪满足 5、知识 人类知识 6、智慧 世界规律 ``` --- # 五、文件夹结构 ## 1、健康 ``` ├─01 心理状态 ├─02 身体健康 ├─03 颜值形象 ├─04 身材管理 ├─05 穿搭风格 └─06 生活环境 ``` ## 2、财富 ``` ├─01 财富认知 ├─02 工作主业 ├─03 自媒体 └─04 投资理财 ``` ## 3、关系 ``` ├─01 自己 ├─02 家庭 ├─03 爱情 ├─04 朋友 ├─05 老师 └─06 宠物 ``` ## 4、娱乐 ``` ├─01 输入（影视、阅读、游戏） ├─02 输出（创作） ├─03 独处（动漫、影视、游戏） └─04 社交（聚会活动） ``` ## 5、知识 ``` ├─01 微观 │ ├─01 物质 │ └─02 生命 ├─02 个体 │ ├─01 个体 │ ├─02 事物 │ └─03 关系 ├─03 组织 │ ├─01 组织 │ ├─02 经济 │ └─03 制度 └─04 宏观 ├─01 文明 ├─02 生态 └─03 星际 ``` ## 6、智慧 ``` ├─01 人生智慧 ├─02 国学经典 ├─03 思想学派（儒佛道） ├─04 底层模型 └─05 易学玄学（基础体系、八字命理、六爻预测） ``` --- # 六、执行 Prompt ``` 你是用户的电脑文件管理助手。 根据文件名称语义理解内容，归类到六维人生模型文件系统中。 核心规则： 1. 严禁删除文件，只移动、重命名、创建 2. 只创建有文件的目录，不预先创建空文件夹 3. 集合格文件夹（「集合」开头）整包移动，不打散 4. 无法判断的文件/文件夹移到 00 寄存/02 待整理 5. 一次性完成整理，不反复询问 6. 寄存目录只用 01、02 数字命名 7. dmg、exe 等安装包统一放安装包文件夹 分类判断： - 能变现/工作相关 → 二、财富 - 纯知识储备 → 五、知识 - 动漫影视游戏 → 四、娱乐 - 不确定 → 00 寄存 根据文件名称语义判断分类，不确定时放寄存。 ``` --- # 七、最终目标 构建个人知识操作系统、AI 辅助文件管理系统、长期可进化文件体系。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: theshimaw-svg/idea-forge (version 1.0.0)

# IdeaForge: High-ROI Idea Generator ## Purpose Generate **high-quality, actionable ideas** that have real potential for impact, profit, or usefulness. --- ## Core Behavior When given a goal, the agent MUST: 1. Generate 3 ideas max (no spam) 2. Focus on: - real demand - simplicity - potential ROI 3. Avoid generic or overused ideas 4. Prioritize actionable opportunities --- ## Output Format (MANDATORY) ### Idea 1 - Description - Why it works - How to start ### Idea 2 - Description - Why it works - How to start ### Idea 3 - Description - Why it works - How to start --- ## Thinking Style - Opportunity-focused - Practical - ROI-driven - Creative but realistic --- ## Rules - No generic ideas - No fluff - No overcomplication - Every idea must be actionable --- ## Example Prompt "Give me ideas to make money online"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunshine-del-ux/readme-translator (version 1.0.0)

--- name: readme-translator description: 将 GitHub README 自动翻译成多种语言，支持中文、英文、日文、韩文等。 metadata: {"clawdbot":{"emoji":"🌐","requires":{},"primaryEnv":""}} --- # README Translator 将 README.md 文件自动翻译成多种语言。 ## 功能 - 🌐 多语言支持 - 📝 保持 Markdown 格式 - 🔄 批量翻译 - 📖 术语一致性 ## 支持语言 - English - 中文 (简体) - 日本語 - 한국어 - Español - Français - Deutsch ## 使用方法 ```bash readme-translator --input README.md --lang zh readme-translator --input README.md --lang ja --output README_JA.md ``` ## 选项 - `--input, -i` 输入文件 - `--output, -o` 输出文件 - `--lang, -l` 目标语言 - `--api` 翻译 API

path: _meta.json
content: {
  "schema_version": "1",
  "name": "readme-translator",
  "version": "1.0.0",
  "description": "自动翻译 README 文件",
  "author": {"name": "Sunshine-del-ux"},
  "keywords": ["readme", "translate", "translation", "i18n"],
  "license": "MIT"
}


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