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Meta-skill creation and optimization system that analyzes, designs, and builds new specialized skills using the integrated toolset. Transforms skill creation from art to science through pattern recognition, intelligent tool selection, and automated quality assurance.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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| name | skill-forge |
| description | Meta-skill creation and optimization system that analyzes, designs, and builds new specialized skills using the integrated toolset. Transforms skill creation from art to science through pattern recognition, intelligent tool selection, and automated quality assurance. |
| license | Apache 2.0 |
| tools | [] |
This meta-skill provides end-to-end skill creation and optimization services by orchestrating multiple expert systems, pattern recognition capabilities, and intelligent tool selection. It transforms the process of creating new specialized skills from an art form into a systematic, science-based approach that learns from existing skills and continuously improves the creation process.
Key Capabilities:
Perfect for:
Triggers:
Use when: Defining new skill scope and identifying domain requirements
Tools Used:
/sc:analyze skill-creation-requirements
Deep Research Agent: domain-specific research and capability analysis
Pattern Recognition Expert: existing skill pattern analysis
Business Panel: market need and value proposition assessment
Activities:
Use when: Designing the skill architecture based on learned patterns
Tools Used:
/sc:design --type meta-skill skill-architecture
Pattern Recognition Expert: successful pattern extraction and analysis
Skill Architect: skill design and workflow orchestration
Tool Integration Specialist: optimal tool combination analysis
Activities:
Use when: Planning the technical implementation and tool integration
Tools Used:
/sc:design --type integration tool-selection-strategy
Tool Integration Specialist: MCP server and tool capability analysis
Sequential MCP: complex tool integration reasoning
Context7 MCP: best practices and implementation patterns
Activities:
Use when: Creating standardized skill templates and implementations
Tools Used:
/sc:implement skill-template-generation
Skill Architect: skill template design and standardization
Quality Assurance Expert: quality checklists and validation criteria
User Experience Designer: user interface and experience optimization
Activities:
Use when: Ensuring new skill meets quality standards and user requirements
Tools Used:
/sc:test skill-comprehensive-validation
Quality Assurance Expert: systematic quality assessment
Pattern Recognition Expert: pattern consistency validation
User Testing: usability and effectiveness validation
Playwright MCP: skill workflow simulation and testing
Activities:
Use when: Rolling out the new skill and establishing improvement processes
Tools Used:
/sc:implement skill-deployment-and-monitoring
Tool Integration Specialist: tool setup and configuration
Quality Assurance Expert: monitoring and feedback systems
User Experience Designer: adoption strategies and user training
Activities:
| Command | Use Case | Output |
|---|---|---|
/sc:design --type meta-skill | Skill architecture design | Meta-skill specifications and workflow |
/sc:analyze skill-patterns | Pattern analysis | Successful pattern identification |
/sc:implement skill-template | Template generation | Standardized skill templates |
/sc:test skill-quality | Quality validation | Quality assessment and improvement |
| Technique | Role | Benefit |
|---|---|---|
| Pattern Recognition | Learning from existing skills | Identifies successful creation patterns |
| Meta-Prompting Analysis | Skill prompt optimization | Creates effective skill prompt patterns |
| Self-Consistency Validation | Quality assurance | Ensures skill consistency and reliability |
| Persona-Pattern Hybrid | Expert coordination | Optimizes expert collaboration patterns |
| Server | Expertise | Use Case |
|---|---|---|
| Sequential | Complex reasoning | Skill architecture analysis and optimization |
| Context7 | Pattern library | Best practices and implementation patterns |
| Serena | Pattern memory | Stores and retrieves successful skill patterns |
| Playwright | Skill testing | Automated skill workflow validation |
| Tavily | Research | Domain research and market analysis |
User: "Create a skill for healthcare compliance and regulatory requirements"
Workflow:
1. Phase 1: Analyze healthcare domain, HIPAA requirements, and compliance needs
2. Phase 2: Recognize patterns from existing skills (security, architecture) and design workflow
3. Phase 3: Select tools: Security Engineer, Deep Research Agent, Regulatory Expert Panel
4. Phase 4: Generate healthcare compliance skill templates with audit trails and validation
5. Phase 5: Validate with healthcare professionals and compliance testing
6. Phase 6: Deploy with monitoring and continuous regulatory updates
Output: Healthcare compliance skill with 95% accuracy in regulatory identification
User: "Design a meta-skill for data science and machine learning workflows"
Workflow:
1. Phase 1: Analyze data science domain, ML workflows, and tool requirements
2. Phase 2: Extract patterns from existing analytical skills and design architecture
3. Phase 3: Select tools: Python Expert, Performance Engineer, Data Analysis tools
4. Phase 4: Create data science skill templates with ML pipeline orchestration
5. Phase 5: Validate with data scientists and ML engineers
6. Phase 6: Deploy with model monitoring and continuous optimization
Output: Data science skill supporting end-to-end ML pipeline development
User: "Optimize our existing skills based on usage patterns and feedback"
Workflow:
1. Phase 1: Analyze usage data, feedback patterns, and performance metrics
2. Phase 2: Identify successful patterns and areas for improvement
3. Phase 3: Recommend tool upgrades and workflow optimizations
4. Phase 4: Generate improved skill templates and configurations
5. Phase 5: Validate improvements with A/B testing and user feedback
6. Phase 6: Deploy optimizations and establish monitoring
Output: 30% improvement in skill effectiveness and user satisfaction
skill-creation-package/
├── analysis/
│ ├── domain-analysis.md # Domain research and requirements
│ ├── pattern-recognition.md # Successful pattern analysis
│ ├── market-assessment.md # Market need and value analysis
│ └── use-case-mapping.md # Target user and use case analysis
├── design/
│ ├── skill-architecture.md # Meta-skill architecture design
│ ├── workflow-orchestration.md # Expert coordination workflows
│ ├── tool-integration-strategy.md # Tool selection and integration
│ └── quality-framework.md # Quality criteria and metrics
├── templates/
│ ├── skill-template.md # Standardized SKILL.md template
│ ├── readme-template.md # Standardized README.md template
│ ├── examples-template.md # Standardized examples.md template
│ └── config-template.md # Configuration template
├── validation/
│ ├── quality-assessment.md # Quality validation results
│ ├── pattern-consistency.md # Pattern consistency validation
│ ├── usability-testing.md # User experience validation
│ └── performance-validation.md # Performance and integration testing
├── implementation/
│ ├── deployment-guide.md # Skill deployment procedures
│ ├── tool-setup.md # Tool configuration and setup
│ ├── user-training.md # User adoption and training materials
│ └── monitoring-setup.md # Continuous monitoring and feedback
└── documentation/
├── creation-patterns.md # Successful creation patterns
├── best-practices.md # Skill creation best practices
├── tool-compatibility.md # Tool compatibility matrix
└── improvement-framework.md # Continuous improvement framework
This skill forge meta-skill transforms the process of creating new specialized capabilities into a systematic, learnable, and continuously improving science. It leverages the full power of your integrated development toolset while learning from each new skill created to continuously enhance the creation process itself.