Release Checklist Gate

Checklist gate for production release - must pass all items before deploying to production.

16 stars

Best use case

Release Checklist Gate is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Checklist gate for production release - must pass all items before deploying to production.

Teams using Release Checklist Gate 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

$curl -o ~/.claude/skills/release-checklist-gate/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/backend/release-checklist-gate/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/release-checklist-gate/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Release Checklist Gate Compares

Feature / AgentRelease Checklist GateStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Checklist gate for production release - must pass all items before deploying to production.

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

# Release Checklist Gate

## Skill Profile
*(Select at least one profile to enable specific modules)*
- [ ] **DevOps**
- [x] **Backend**
- [ ] **Frontend**
- [ ] **AI-RAG**
- [ ] **Security Critical**

## Overview
Checklist gate for production release - must pass all items before deploying to production.

## Why This Matters
- **Safety**: Don't miss important steps
- **Quality**: Release has standards
- **Accountability**: Know who approved
- **Rollback ready**: Ready to rollback if problems occur

---

## 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**:
  - Release version
  - Release notes
  - Changelog
  - Test results
  - Rollback plan
* **Entry Conditions**:
  - All code changes are merged
  - Tests are passing
  - Documentation is complete
* **Outputs**:
  - Release checklist (completed)
  - Approval status
  - Release announcement
  - Deployment status
* **Artifacts Required (Deliverables)**:
  - Release checklist
  - Release notes
  - Changelog
  - Migration guide
  - Rollback plan
  - Monitoring dashboard
* **Acceptance Evidence**:
  - All checklist items checked
  - All approvals obtained
  - Tests pass in staging
  - Rollback plan tested
* **Success Criteria**:
  - 100% of checklist items complete
  - All approvals obtained
  - No blocking issues
  - Rollback ready

## Skill Composition
* **Depends on**: [definition-of-done](./definition-of-done/SKILL.md), [lint-test-typecheck-policy](./lint-test-typecheck-policy/SKILL.md)
* **Compatible with**: [ci-pipeline-generator](../67-codegen-scaffolding-automation/ci-pipeline-generator/SKILL.md)
* **Conflicts with**: None
* **Related Skills**: [security-scan-policy](./security-scan-policy/SKILL.md)

---

## 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 & Changelog

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

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