Skill: bustes01/skill-builder-pro (version 1.4.0)

--- name: skill-builder-pro description: > Automated Skill development tool. User provides prompt + feature description, and the agent auto-completes the full ClawHub Skill creation, testing, and publishing workflow. version: 1.3.0 metadata: openclaw: requires: bins: - clawhub - python3 emoji: "🏗️" homepage: https://clawhub.ai/BusTes01/skill-builder-pro models: - gpt-4 - deepseek-v4-flash - gemini-2.0-flash --- # 🏗️ Skill Builder Pro A **meta-skill that produces Skills**. User describes what they want, and the agent auto-completes the full journey from ideation to ClawHub publishing. Turns AI into your skill development team. ## Workflow ``` User describes idea → Requirement analysis → Generate SKILL.md → Build directory → Validate locally → Publish to ClawHub ``` ## Step-by-Step ### Step 1: Requirement Gathering Agent collects from user: ``` 1. Skill name (slug, e.g., `my-skill-name`)? 2. One-line description? 3. Required tools / APIs? (curl, web_search, Python...) 4. Does it need API keys? User registration needed? 5. Target audience? 6. Key features (1-3)? 7. Output format? (Markdown / text / JSON / image) 8. Free or paid? Price if paid? ``` ### Step 2: Generate Directory Structure Create standard Skill structure in `clawhub-skills/`: ``` └── <skill-name>/ ├── SKILL.md # Main file (YAML frontmatter + instructions) ├── scripts/ # Helper scripts (optional) └── references/ # Reference docs (optional) ``` ### Step 3: Write SKILL.md Generate ClawHub-compliant SKILL.md with: - YAML frontmatter (`name`, `description`, `version`, `metadata.openclaw`) - English section first, Chinese section second, separated by `---` - English model names only - Feature explanation, usage steps, configuration notes - Output examples and behavior guidelines ### Step 4: Local Validation ```bash clawhub skill publish ./<skill-name> --dry-run ``` Check: - YAML frontmatter format - metadata declarations complete - File size within limits - No external path leaks ### Step 5: Privacy Security Scan (Mandatory) **Run before every publish:** ```bash grep -in "AIzaSy\|sk-\|password\|secret\|@gmail\|@qq\|/home/\|192\.168" ./<skill-name>/SKILL.md ``` Scan checklist: - ❌ Usernames / nicknames → replace with generic terms - ❌ API keys / tokens → should be env vars, never hardcoded - ❌ Email addresses → remove or use placeholder - ❌ Local file paths → use relative or pseudo-paths - ❌ Internal IPs → use `localhost` or generic IPs - ❌ Passwords / secrets → never in SKILL.md - ❌ Personal habits / schedules → generalize ### Step 6: Publish After confirmation: ```bash export PATH="$PATH:$(npm root -g)/.bin" clawhub skill publish ./<skill-name> \ --slug <slug> \ --name "<Display Name>" \ --version <new-version> ``` ### Step 7: Post-Publish Verification ```bash clawhub inspect <username>/<skill-name> ``` Confirm successful listing and return the ClawHub link to the user. ## Bilingual Convention All skills published via this builder follow this convention: - **One SKILL.md with two sections**: English section first, Chinese section second, separated by `---` - **Model names**: always in English (e.g., `gpt-4`, `deepseek-v4-flash`, `sana`, `flux`) - **YAML frontmatter description**: English only ## Required YAML Frontmatter ```yaml --- name: <slug-name> description: <one-line description in English> version: 1.0.0 metadata: openclaw: requires: env: [] # Required env vars bins: [] # Required executables primaryEnv: "" # Primary auth credential emoji: "<emoji>" models: [] # Compatible models --- ``` ## Important Notes 1. **No personal info** in SKILL.md — names, API keys, passwords, paths 2. **Declare all dependencies** — env vars, bins must be complete 3. **Semantic versioning** — 1.0.0, 1.1.0, 2.0.0 4. **Honest about limitations** — clearly state what the skill cannot do 5. **Bilingual format** — English section + Chinese section, not mixed ## Skill Architecture Patterns ### Shared Component Pattern Extract reusable capabilities into standalone skills that others depend on: - `complex-memory-manager` — Privacy-aware memory (T1/T2/T3), encryption, cleanup - `self-iteration-engine` — Usage logging, feedback loops, auto-update decisions Other skills declare dependency: ```yaml metadata: openclaw: requires: skills: - complex-memory-manager - self-iteration-engine ``` When a shared component is updated, check ALL dependent skills for backward compatibility. ### Private Skill Pattern Some skills are for personal use and should NOT be published: - Do not run `clawhub publish` for them - Mark clearly in description: `【⚠️ 私人使用，不发布到 ClawHub】` - Still apply privacy scanning and security checks ### Knowledge Accumulation Pattern Skills needing cross-session learning should: - Store concepts in `memory/concepts/<slug>.md` - Use `complex-memory-manager` for tiered persistence - Use `self-iteration-engine` for usage tracking --- # 🏗️ Skill Builder Pro 一个**生产Skill的Skill**。用户只需描述想要的功能，即可自动完成从构思到上架的完整流程。把AI变成你的Skill开发团队。 ## 工作流程 ``` 用户描述想法 → 需求分析 → 生成SKILL.md → 构建目录 → 本地验证 → 发布上架 ``` ## 分步指南 ### 第一步：需求分析 Agent 向用户收集以下信息： ``` 1. Skill名称叫什么？（如：my-skill-name） 2. 一句话描述？ 3. 需要用到哪些工具或API？（curl、web_search、Python等） 4. 是否需要API Key？用户自行注册？ 5. 目标用户是谁？ 6. 主要功能点（1-3个）？ 7. 输出格式偏好？（Markdown、文本、JSON、图片） 8. 是否免费？如付费，价格？ ``` ### 第二步：生成目录结构 在 `clawhub-skills/` 下创建标准Skill结构： ``` └── <skill-name>/ ├── SKILL.md # 主文件（YAML frontmatter + 说明） ├── scripts/ # 辅助脚本（可选） └── references/ # 参考文档（可选） ``` ### 第三步：编写SKILL.md 生成符合ClawHub规范的SKILL.md，包含： - YAML frontmatter（name、description、version、metadata.openclaw） - 上半部分纯英文、下半部分纯中文，中间用 `---` 分隔 - 模型名称仅使用英文 - 功能说明、使用步骤、配置说明、输出示例、行为准则 ### 第四步：本地验证 ```bash clawhub skill publish ./<skill-name> --dry-run ``` 检查： - YAML frontmatter格式正确 - metadata声明完整 - 文件尺寸在限制内 - 无外部路径泄露 ### 第五步：隐私安全检查（必做） **每次发布前运行：** ```bash grep -in "AIzaSy\|sk-\|password\|secret\|@gmail\|@qq\|/home/\|192\.168" ./<skill-name>/SKILL.md ``` 检查项： - ❌ 用户昵称/真实姓名 → 改为通用表述 - ❌ API Key / Token → 应使用环境变量，绝不硬编码 - ❌ 邮箱地址 → 删除或替换为占位符 - ❌ 本地文件路径 → 使用相对路径或伪路径 - ❌ 内网IP地址 → 使用localhost或通用IP - ❌ 密码/密钥明文 → 绝不出现 - ❌ 个人作息/偏好习惯 → 泛化处理 ### 第六步：发布上架 确认后发布： ```bash export PATH="$PATH:$(npm root -g)/.bin" clawhub skill publish ./<skill-name> \ --slug <slug> \ --name "<显示名称>" \ --version <新版本> ``` ### 第七步：发布后检查 ```bash clawhub inspect <username>/<skill-name> ``` 确认上架成功，将ClawHub链接返回给用户。 ## 双语约定 通过此工具发布的所有Skill遵循以下约定： - **单个SKILL.md包含两个独立区段**：上半部分纯英文，下半部分纯中文，用 `---` 分隔 - **模型名称**：一律使用英文（如 `gpt-4`、`deepseek-v4-flash`、`sana`、`flux`） - **YAML frontmatter描述**：英文 ## 必填YAML Frontmatter字段 ```yaml --- name: <slug名称> description: <英文一句话描述> version: 1.0.0 metadata: openclaw: requires: env: [] # 需要的环境变量 bins: [] # 需要的可执行文件 primaryEnv: "" # 主要认证凭证 emoji: "<emoji>" models: [] # 兼容模型 --- ``` ## 注意事项 1. SKILL.md中不得包含个人信息——姓名、API Key、密码、路径 2. 声明所有依赖——env vars、bins要完整 3. 遵循语义化版本——1.0.0、1.1.0、2.0.0 4. 诚实说明限制——明确告知技能不能做什么 5. 双语格式——英文区段 + 中文区段，不混写 ## Skill 架构模式 ### 共享组件模式 将可复用能力抽取为独立skill供其他skill依赖： - `complex-memory-manager` — 隐私感知记忆管理（T1/T2/T3）、加密、清理 - `self-iteration-engine` — 使用日志、反馈循环、自动更新决策 其他skill在YAML frontmatter中声明依赖。更新共享组件时需检查所有依赖技能的向后兼容。 ### 私人Skill模式 部分skill仅供个人使用，不发布： - 不执行 `clawhub publish` - 描述中标注 `【⚠️ 私人使用，不发布到 ClawHub】` - 仍需执行隐私检查 ### 知识积累模式 需要跨会话学习的skill应： - 概念存入 `memory/concepts/<slug>.md` - 使用 `complex-memory-manager` 做层级化持久存储 - 使用 `self-iteration-engine` 追踪使用和优化


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