Advanced Skill Creator
Advanced skill creation handler that executes the official 5-step research flow with comprehensive analysis and best practices. Ensures proper methodology and standards compliance by following the complete research process, applicable to all timeframes and use cases.
When to use
- - When user mentions "写一个触发", "写skill", "claw skill", "openclaw skill", "moltbot skill", "创建技能", or "写一个让它..."
- When proper skill creation methodology needs to be followed according to official standards
- When ensuring adherence to 5-step research flow (documentation, ClawHub, community, fusion, output)
- For comprehensive skill analysis and creation with best practices
5-Step Research Flow Execution
Step 1: Consult Official Documentation
Comprehensively access official documentation:
- - https://docs.clawd.bot/tools/skills
- https://docs.openclaw.ai/tools/skills
- /tools/clawhub
- /tools/skills-config
Extract key information:
- - SKILL.md format requirements
- YAML frontmatter specifications (name, description, when, examples, metadata.openclaw.*, requires)
- Trigger mechanisms (natural language triggers, when conditions)
- Tool calling conventions (exec, browser, read, write, nodes, MCP)
- Loading precedence (workspace > ~/.openclaw/skills > bundled)
- ClawHub installation methods
- Breaking changes (latest versions)
Step 2: Research Related Public Skills on ClawHub/ClawdHub
Thoroughly query ClawHub/ClawdHub for relevant skills:
- - Search keywords: weather, reminder, schedule, translate, image, cron, memory, task-tracker, notification, backup, automation
- Select 2-4 most relevant skills with high downloads/recent updates/community ratings
- Analyze:
- Trigger descriptions (when, examples)
- YAML metadata
- Pure Markdown vs. scripts/ structure
- Dependency declarations
- Error handling recommendations
- Community feedback (why popular or criticized)
- Security considerations
Step 3: Search Best Practices
Use comprehensive keyword combinations for GitHub searches:
- - "OpenClaw SKILL.md" OR "ClawDBot skill example" OR "Moltbot create skill"
- "SKILL.md" "when:" OR "metadata.openclaw" site:github.com
- "clawhub install" "custom skill" OR "openclaw skill tutorial"
- "skill security" OR "prompt injection prevention" OR "skill best practices"
Focus on:
- - Active GitHub repositories
- Recent commits
- Blog/Reddit/X content
- Security best practices
- Known security pitfalls (prompt injection, exec abuse)
Step 4: Solution Fusion & Comparison
Comprehensively summarize implementation approaches from all three sources:
Compare across key dimensions:
- - Trigger precision (false positive rate)
- Maintainability/readability
- Loading speed/memory impact
- Compatibility (different gateways/channels/versions)
- Security & error isolation
- Upgrade friendliness (dependency on specific tools)
- Dependency management complexity
- Performance optimization
- Error handling robustness
Select optimal solution for current context with 4-7 clear reasons prioritized:
- - Official documentation > High-quality ClawHub skills > Active community solutions > Self-optimization
Step 5: Proper Output Structure
Output must follow exact structure without adding extra headers or showing raw search logs:
- - Use the exact headings: 【最终推荐方案】, 【文件结构预览】, 【完整文件内容】
- Provide complete file contents with proper formatting
- Include tree-style directory structure preview
- Use proper YAML frontmatter in SKILL.md examples
- Ensure comprehensive documentation
Resource Utilization
Documentation Features Utilized
- - YAML frontmatter format (name, description, when, examples, metadata.openclaw.*)
- Trigger mechanism definition (when field)
- Example specification (examples field)
- Metadata definition (metadata.openclaw.requires)
- Standardized skill description structure
Skills Referenced
- - system-monitor: Structure and functional organization
- security-monitor: Metadata definition format
- integrated-system-monitor: Script organization and implementation
- Other existing skills: YAML frontmatter best practices
Community Practices Integrated
- - GitHub popular OpenClaw skill project structures
- Community-recommended security practices (input validation, error handling)
- Optimal metadata configuration methods
- Effective trigger word definition patterns
Custom Scripts Created
- - advancedskillprocessor.py: Implements complete 5-step research flow automation
- Automated documentation query, public skill research, best practice search
- Solution fusion and comparison functionality
- Standardized output generation
- Error handling and logging features
Implementation Requirements
- 1. Execute all 5 steps in strict sequence - no skipping allowed
- Do not rely on memory or "approximately correct" code
- Demonstrate research → comparison → selection logical chain
- Show evidence of consulting official documentation
- Include proper metadata and security considerations
- Provide complete, functional skill implementations with proper structure
- Ensure all outputs follow the exact template structure required
- Apply universally regardless of timeframe or version
- Include security best practices and error handling
- Provide comprehensive examples and use cases
- Include system prompt integration for enhanced AI interaction
- Incorporate thinking model framework for improved decision-making
System Prompt Integration
When creating new skills, include system prompt elements that enhance AI interaction:
"You are now an OpenClaw (formerly ClawDBot / Moltbot) skill development expert, implementing advanced thinking models for enhanced decision-making. Apply structured cognitive processing while balancing speed and accuracy based on specific situational requirements."
Skill Creation Guidelines
- - Apply the multi-stage cognitive processing pipeline during skill design
- Integrate memory systems for continuous learning and improvement
- Balance speed optimization with accuracy enhancement in skill functionality
- Include appropriate system prompts for AI assistants using the skill
- Document decision-making processes for future reference and learning
高级技能创建器
高级技能创建处理器,执行官方5步研究流程,包含全面分析和最佳实践。通过遵循完整的研究流程,确保正确的方法论和标准合规性,适用于所有时间框架和使用场景。
使用时机
- - 当用户提到写一个触发、写skill、claw skill、openclaw skill、moltbot skill、创建技能或写一个让它...
- 当需要按照官方标准遵循正确的技能创建方法论时
- 当需要确保遵循5步研究流程(文档、ClawHub、社区、融合、输出)时
- 用于包含最佳实践的全面技能分析和创建
5步研究流程执行
第1步:查阅官方文档
全面访问官方文档:
- - https://docs.clawd.bot/tools/skills
- https://docs.openclaw.ai/tools/skills
- /tools/clawhub
- /tools/skills-config
提取关键信息:
- - SKILL.md格式要求
- YAML前置元数据规范(名称、描述、触发时机、示例、metadata.openclaw.*、依赖项)
- 触发机制(自然语言触发、触发条件)
- 工具调用约定(exec、browser、read、write、nodes、MCP)
- 加载优先级(工作区 > ~/.openclaw/skills > 内置)
- ClawHub安装方法
- 重大变更(最新版本)
第2步:研究ClawHub/ClawdHub上的相关公开技能
全面查询ClawHub/ClawdHub上的相关技能:
- - 搜索关键词:天气、提醒、日程、翻译、图片、定时任务、记忆、任务追踪、通知、备份、自动化
- 选择2-4个下载量高/近期更新/社区评分高的最相关技能
- 分析:
- 触发描述(触发时机、示例)
- YAML元数据
- 纯Markdown与脚本/结构
- 依赖声明
- 错误处理建议
- 社区反馈(为何受欢迎或受批评)
- 安全考虑
第3步:搜索最佳实践
使用综合关键词组合进行GitHub搜索:
- - OpenClaw SKILL.md 或 ClawDBot skill example 或 Moltbot create skill
- SKILL.md when: 或 metadata.openclaw site:github.com
- clawhub install custom skill 或 openclaw skill tutorial
- skill security 或 prompt injection prevention 或 skill best practices
重点关注:
- - 活跃的GitHub仓库
- 近期提交
- 博客/Reddit/X内容
- 安全最佳实践
- 已知安全陷阱(提示注入、exec滥用)
第4步:解决方案融合与比较
全面总结来自所有三个来源的实现方法:
在关键维度上进行比较:
- - 触发精度(误报率)
- 可维护性/可读性
- 加载速度/内存影响
- 兼容性(不同网关/渠道/版本)
- 安全性与错误隔离
- 升级友好性(对特定工具的依赖)
- 依赖管理复杂度
- 性能优化
- 错误处理健壮性
为当前上下文选择最优解决方案,附带4-7个明确的优先级理由:
- - 官方文档 > 高质量ClawHub技能 > 活跃社区解决方案 > 自我优化
第5步:正确输出结构
输出必须遵循精确结构,不添加额外标题或显示原始搜索日志:
- - 使用精确标题:【最终推荐方案】、【文件结构预览】、【完整文件内容】
- 提供格式正确的完整文件内容
- 包含树状目录结构预览
- 在SKILL.md示例中使用正确的YAML前置元数据
- 确保文档全面完整
资源利用
使用的文档特性
- - YAML前置元数据格式(名称、描述、触发时机、示例、metadata.openclaw.*)
- 触发机制定义(when字段)
- 示例规范(examples字段)
- 元数据定义(metadata.openclaw.requires)
- 标准化技能描述结构
参考的技能
- - system-monitor:结构和功能组织
- security-monitor:元数据定义格式
- integrated-system-monitor:脚本组织和实现
- 其他现有技能:YAML前置元数据最佳实践
整合的社区实践
- - GitHub流行的OpenClaw技能项目结构
- 社区推荐的安全实践(输入验证、错误处理)
- 最优元数据配置方法
- 有效触发词定义模式
创建的自定义脚本
- - advancedskillprocessor.py:实现完整的5步研究流程自动化
- 自动化文档查询、公开技能研究、最佳实践搜索
- 解决方案融合和比较功能
- 标准化输出生成
- 错误处理和日志记录功能
实现要求
- 1. 严格按顺序执行所有5个步骤——不允许跳过
- 不依赖记忆或大致正确的代码
- 展示研究→比较→选择的逻辑链
- 显示查阅官方文档的证据
- 包含正确的元数据和安全考虑
- 提供完整、功能性的技能实现,结构正确
- 确保所有输出遵循所需的精确模板结构
- 无论时间框架或版本如何,普遍适用
- 包含安全最佳实践和错误处理
- 提供全面的示例和使用场景
- 包含系统提示集成以增强AI交互
- 融入思维模型框架以改进决策
系统提示集成
在创建新技能时,包含增强AI交互的系统提示元素:
你现在是OpenClaw(原名ClawDBot / Moltbot)技能开发专家,实施高级思维模型以增强决策能力。应用结构化认知处理,同时根据具体情境要求平衡速度和准确性。
技能创建指南
- - 在技能设计过程中应用多阶段认知处理管道
- 集成记忆系统以实现持续学习和改进
- 在技能功能中平衡速度优化与准确性提升
- 为使用该技能的AI助手包含适当的系统提示
- 记录决策过程以供将来参考和学习