skill-generator

id	SKL-skill-SKILLGENERATOR
name	Skill Generator
description	The Meta-Skill Generator is the foundational cognitive engine that instructs an AI Agent on *how* to generate a perfect `SKILL.md` file using the existing `skill-template.md`. This skill acts as the o
version	1.0.0
status	active
owner	@cerebra-team
last_updated	2026-02-22
category	Backend
tags	["api","backend","server","database"]
stack	["Python","Node.js","REST API","GraphQL"]
difficulty	Intermediate

Skill Generator

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

The Meta-Skill Generator is the foundational cognitive engine that instructs an AI Agent on how to generate a perfect SKILL.md file using the existing skill-template.md. This skill acts as the orchestration hub for self-expansion, enabling agents to create new skills autonomously while maintaining strict adherence to architectural standards, guardrails, and validation requirements.

Why This Matters

• Self-Expansion: Enables the Agent ecosystem to grow autonomously by generating new skills on demand
• Standardization: Ensures every generated skill follows the exact same structure and quality standards
• Validation: Provides built-in checks to prevent malformed or incomplete skill definitions
• Architectural Integrity: Maintains the "Twin-Engine" intelligence architecture through proper metadata and relationship mapping

---

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Business requirement or technical specification
  • Target technology stack (optional, can be inferred)
  • Reference to skill-template.md at D:\Cerebra\CerebraSkills\templates\skill-template.md
  • Existing skills catalog (for relationship mapping)
• Entry Conditions:
  • skill-template.md exists and is accessible
  • Agent has read access to the CerebraSkills repository
  • Target output directory exists or can be created
• Outputs:
  • Fully-formed, valid SKILL.md file
  • Validation report (optional, for quality assurance)
• Artifacts Required (Deliverables):
  • SKILL.md file with complete YAML frontmatter
  • All required sections populated according to template
  • Proper markdown formatting and structure
• Acceptance Evidence:
  • Generated skill passes markdown linting
  • YAML frontmatter is valid and parseable
  • All required sections are present and non-empty
  • Relationship graph references are valid
• Success Criteria:
  • Template structure compliance: 100%
  • YAML frontmatter validity: 100%
  • Required sections completeness: 100%
  • Relationship graph accuracy: 100%

Skill Composition

• Depends on: None (Foundational skill)
• Compatible with: All skills (this skill generates them)
• Conflicts with: None
• Related Skills: intelligence-router, skill-architect

---

Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Type	Focus Area	Required Scenarios / Mocks
Unit	Core Logic	Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage
Integration	DB / API	All external API calls or database connections must be mocked during unit tests
E2E	User Journey	Critical user flows to test
Performance	Latency / Load	Benchmark requirements
Security	Vuln / Auth	SAST/DAST or dependency audit
Frontend	UX / A11y	Accessibility checklist (WCAG), Performance Budget (Lighthouse score)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure