Openapi Governance
* **Depends on**: None * **Compatible with**: None * **Conflicts with**: None * **Related Skills**: None # Overview Comprehensive guide to OpenAPI governance, API design standards, automated linting,
Best use case
Openapi Governance is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
* **Depends on**: None * **Compatible with**: None * **Conflicts with**: None * **Related Skills**: None # Overview Comprehensive guide to OpenAPI governance, API design standards, automated linting,
Teams using Openapi Governance should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/openapi-governance/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Openapi Governance Compares
| Feature / Agent | Openapi Governance | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
* **Depends on**: None * **Compatible with**: None * **Conflicts with**: None * **Related Skills**: None # Overview Comprehensive guide to OpenAPI governance, API design standards, automated linting,
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Openapi Governance
## Skill Profile
*(Select at least one profile to enable specific modules)*
- [ ] **DevOps**
- [x] **Backend**
- [ ] **Frontend**
- [ ] **AI-RAG**
- [ ] **Security Critical**
## Overview
This skill provides comprehensive guidance and best practices for implementation. It enables teams to achieve reliable, maintainable, and scalable solutions.
### When to use / When NOT to use
* ✅ **Use when**: Implementing this capability in your project
* ✅ **Use when**: Need to follow established patterns and conventions
* ❌ **Avoid when**: The requirements don't match this skill's scope
* ❌ **Avoid when**: Simpler alternatives would suffice
## Why This Matters
- **Reduces Technical Debt**: Following established patterns prevents costly rework
- **Increases System Stability**: Proper implementation reduces bugs and downtime
- **Improves Team Velocity**: Clear guidance helps teams work more efficiently
- **Reduces Maintenance Costs**: Well-structured code is easier to maintain
- **Ensures Investment Confidence**: Following standards gives confidence in technical decisions
## 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**:
- Requirements and specifications
- Existing codebase and architecture
- Team context and constraints
* **Entry Conditions**:
- Project repository initialized
- Development environment set up
- Required dependencies installed
* **Outputs**:
- Implementation code and documentation
- Test cases and test results
- Deployment artifacts
* **Artifacts Required (Deliverables)**:
- Source code changes
- Updated documentation
- Test coverage reports
* **Acceptance Evidence**:
- All tests passing
- Code review approved
- Documentation updated
* **Success Criteria**:
- Meets all functional requirements
- Follows established patterns
- Test coverage ≥ 80%
## Skill Composition
* **Depends on**: None
* **Compatible with**: None
* **Conflicts with**: None
* **Related Skills**: None
# Overview
Comprehensive guide to OpenAPI governance, API design standards, automated linting, breaking change detection, and API lifecycle management
## 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
```python
# 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
- **Semantic Versioning**: Use semantic versioning
- **Backward Compatibility**: Maintain compatibility
- **Deprecation Policy**: Clear deprecation timeline
- **Migration Guides**: Help users migrateRelated Skills
awesome-copilot-root-agent-governance
Use when: the task directly matches agent governance responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
ai-development-governance
AI-augmented development controls, GitHub Copilot governance, LLM security, AI-generated code review per Hack23 Secure Development Policy
data-governance-enrichment
Enrich CRM data: tools, waterfall approach, automation, quality control. Use when designing or improving data enrichment in rev ops.
agent-governance
Implement hooks for permission control and security in custom agents. Use when adding security controls, blocking dangerous operations, implementing audit trails, or designing permission governance.
bgo
Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.
mcp-create-declarative-agent
Skill converted from mcp-create-declarative-agent.prompt.md
MCP Architecture Expert
Design and implement Model Context Protocol servers for standardized AI-to-data integration with resources, tools, prompts, and security best practices
mathem-shopping
Automatiserar att logga in på Mathem.se, söka och lägga till varor från en lista eller recept, hantera ersättningar enligt policy och reservera leveranstid, men lämnar varukorgen redo för manuell checkout.
math-modeling
本技能应在用户要求"数学建模"、"建模比赛"、"数模论文"、"数学建模竞赛"、"建模分析"、"建模求解"或提及数学建模相关任务时使用。适用于全国大学生数学建模竞赛(CUMCM)、美国大学生数学建模竞赛(MCM/ICM)等各类数学建模比赛。
matchms
Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.
managing-traefik
Manages Traefik reverse proxy for local development. Use when routing domains to local services, configuring CORS, checking service health, or debugging connectivity issues.
managing-skills
Install, find, update, and manage agent skills. Use when the user wants to add a new skill, search for skills that do something, check if skills are up to date, or update existing skills. Triggers on: install skill, add skill, get skill, find skill, search skill, update skill, check skills, list skills.