Skill Builder Pro

Automated Skill development tool. User provides prompt + feature description, and the agent auto-completes the full ClawHub Skill creation, testing, and publishing workflow.

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

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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:

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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/:

text

└── <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

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🏗️ 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

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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：

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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：

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└── <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