Create Merge Request Changelog

Create Merge Request Changelog

Generate comprehensive, business-focused merge request descriptions by analyzing git changes. This skill adapts the structured analysis approach from JIRA release notes to work purely with git data.

Quick Start

# Analyze current branch against main
/create-merge-request-changelog

# Analyze specific branch
/create-merge-request-changelog feature/new-auth

# Analyze specific branch against custom target
/create-merge-request-changelog feature/new-auth develop

# Analyze specific commit range
/create-merge-request-changelog abc123f def456g

Workflow

Option A: From Git Branch (Most Common)

Step 1: Extract Git Data

Run the Python CLI to gather commits, diffs, and statistics:

python scripts/analyze_git_changes.py [base_ref] [head_ref] --output-dir /tmp/mr-analysis

Examples:

# Current branch vs main (default)
python scripts/analyze_git_changes.py

# Specific branch vs develop
python scripts/analyze_git_changes.py develop feature/new-auth

# Specific commit range
python scripts/analyze_git_changes.py abc123f def456g

# Custom output directory
python scripts/analyze_git_changes.py main HEAD --output-dir ./analysis-output

Output files created:

• commits_oneline.txt - Concise commit list
• commits_detailed.txt - Full commit messages with metadata
• changes.diff - Complete unified diff
• changes_stat.txt - Diffstat summary
• changed_files.txt - File list with change status (A/M/D)
• changes_numstat.txt - Per-file line changes
• summary.json - Machine-readable statistics

Option B: From Existing GitLab MR

Step 1: Fetch MR Data

Use the GitLab CLI integration to fetch existing MR data:

python scripts/fetch_gitlab_mr.py <mr-id> --output /tmp/mr-data.json

Examples:

# By MR ID
python scripts/fetch_gitlab_mr.py 123

# By !notation
python scripts/fetch_gitlab_mr.py !123

# By full URL
python scripts/fetch_gitlab_mr.py https://gitlab.com/org/project/-/merge_requests/123

# Without diff (faster)
python scripts/fetch_gitlab_mr.py 123 --no-diff

# Custom output file
python scripts/fetch_gitlab_mr.py 123 --output ./mr-metadata.json

Output: JSON file with MR metadata, commits, and diffs

Step 2: Analyze Changes with AI

Load the analysis prompts and use them to categorize changes:

1. Read the primary analysis prompt from references/analysis_prompts.md
2. Load the extracted git data from Step 1 output files
3. Run the analysis using the prompt template with your git data
4. Get structured JSON with categorized changes

The AI analysis will:

• Categorize changes into domains (bug fixes, enhancements, tech debt, docs, testing, build/CI, non-functional)
• Extract intent from commit messages and diff patterns
• Identify breaking changes and migration requirements
• Determine affected components
• Assess impact on users and developers

Step 3: Format MR Description

Use the Python formatter to generate polished markdown from AI analysis:

python scripts/format_mr_description.py <analysis-json> --output /tmp/mr-description.md

Examples:

# With markdown preview (default)
python scripts/format_mr_description.py analysis.json

# Without preview
python scripts/format_mr_description.py analysis.json --no-preview

# Custom output file
python scripts/format_mr_description.py analysis.json --output ./MR_DESCRIPTION.md

# With custom title
python scripts/format_mr_description.py analysis.json --title "feat: Add authentication system"

The formatter automatically:

• Applies templates from references/output_template.md
• Formats each category (bug fixes, enhancements, tech debt, etc.)
• Truncates long file lists (>5 files)
• Highlights breaking changes
• Generates deployment notes if needed
• Renders markdown preview with Rich

Step 4: Use the Description

The final MR description is ready to:

• Copy to clipboard (from preview or file)
• Paste into GitLab/GitHub MR/PR
• Save as documentation
• Use in release notes

Integration examples:

# GitLab MR creation
python scripts/format_mr_description.py analysis.json --output /tmp/mr.md --no-preview
glab mr create --fill --description "$(cat /tmp/mr.md)"

# GitHub PR creation
python scripts/format_mr_description.py analysis.json --output /tmp/pr.md --no-preview
gh pr create --fill --body-file /tmp/pr.md

# Copy to clipboard (macOS)
python scripts/format_mr_description.py analysis.json --no-preview --output - | pbcopy

# Copy to clipboard (Linux)
python scripts/format_mr_description.py analysis.json --no-preview --output - | xclip -selection clipboard

Change Categorization Guide

Bug Fixes 🐛

Indicators:

• Commit keywords: "fix", "bug", "issue", "correct", "resolve"
• Diff patterns: Added error handling, validation, defensive checks

What to capture:

• What was broken
• How it's now fixed
• Who/what was affected
• Migration steps if behavior changed

Enhancements ✨

Indicators:

• Commit keywords: "feat", "add", "improve", "enhance", "implement"
• Diff patterns: New functions/classes, expanded APIs, additional features

What to capture:

• New capability added
• Benefits to users/developers
• How to use it
• API additions or changes

Technical Debt 🏗️

Indicators:

• Commit keywords: "refactor", "cleanup", "simplify", "optimize"
• Diff patterns: Code reorganization, reduced duplication, dependency updates

What to capture:

• Why refactoring was needed
• What changed internally
• How it improves maintainability/performance
• Developer-facing improvements

Documentation 📚

Indicators:

• File patterns: *.md, *.rst, README, docs/
• Diff patterns: Only documentation files or comments changed

What to capture:

• What documentation was added/updated
• Where to find it
• Why it helps users/developers

Testing 🧪

Indicators:

• File patterns: test_*.py, *.spec.js, tests/, __tests__/
• Commit keywords: "test", "coverage", "spec"

What to capture:

• What's now tested
• Test type (unit/integration/e2e)
• Coverage improvements

Build & CI 🔧

Indicators:

• File patterns: .github/workflows/, .gitlab-ci.yml, Dockerfile, build scripts
• Commit keywords: "ci", "build", "deploy", "docker"

What to capture:

• Build/deployment changes
• Workflow improvements
• Developer experience enhancements

Non-Functional 🧹

Indicators:

• Commit keywords: "chore", "style", "format", "lint"
• Diff patterns: Whitespace, formatting, import ordering, minor config

What to capture:

• Type (formatting/linting/config/deps)
• Brief description
• Files affected

Breaking Changes Detection

Automatically detect and highlight breaking changes:

Detection patterns:

• Commit messages: "BREAKING CHANGE", "breaking:", "incompatible"
• API changes: Removed public functions, changed signatures
• Configuration: Required new environment variables
• Database: Schema migrations, data migrations

Required information:

• What changed and why it breaks compatibility
• Migration steps for users
• Affected APIs or components
• Commit references

Advanced Usage

Custom Component Mapping

Map files to logical components for better organization:

{
  "src/auth/*": "Authentication",
  "src/api/*": "API Layer",
  "src/database/*": "Data Layer",
  "tests/*": "Test Suite",
  "docs/*": "Documentation"
}

Conventional Commits Integration

The skill automatically recognizes conventional commit prefixes:

• fix: → Bug Fixes
• feat: → Enhancements
• refactor: → Technical Debt
• docs: → Documentation
• test: → Testing
• chore: → Non-Functional
• ci: → Build & CI
• perf: → Performance (Enhancement with perf tag)
• style: → Non-Functional (formatting)

GitLab/GitHub CLI Integration

Use with GitLab or GitHub CLI tools:

# Generate description and create GitLab MR
/create-merge-request-changelog
# ... Copy output to clipboard ...
glab mr create --fill --description "$(pbpaste)"

# Generate description and create GitHub PR
/create-merge-request-changelog
# ... Save output to file ...
gh pr create --fill --body-file /tmp/mr-description.md

Example Output

# feat: Add user authentication with JWT and refresh tokens

## Summary

This merge request implements a complete user authentication system using JWT tokens with refresh token rotation. The implementation includes password hashing with bcrypt, token expiration handling, and secure session management.

## Statistics

- **Commits**: 12
- **Files Changed**: 18
- **Lines Added**: 1,247
- **Lines Deleted**: 89

## Changes by Category

### ✨ Enhancements

- **JWT-based authentication system with refresh token rotation**
  - **Feature:** Complete authentication flow with access tokens (15min) and refresh tokens (7 days)
  - **Benefits:** Secure, stateless authentication with automatic token refresh for better UX
  - **Usage:** POST /api/auth/login with credentials, use returned access token in Authorization header
  - **Files:** `src/auth/jwt_service.py`, `src/auth/token_store.py`, `src/api/auth_routes.py`
  - **Technical Details:** Uses RS256 algorithm, tokens stored in Redis, automatic cleanup of expired tokens

- **Password strength validation and bcrypt hashing**
  - **Feature:** Enforces strong passwords (min 12 chars, mixed case, numbers, symbols) with bcrypt hashing
  - **Benefits:** Prevents weak passwords and protects against rainbow table attacks
  - **Usage:** Automatic validation on user registration and password changes
  - **Files:** `src/auth/password_validator.py`, `src/models/user.py`

### 🧪 Testing

- **Comprehensive authentication test suite**: Added 47 unit tests and 12 integration tests covering all auth flows
  - **Type:** unit, integration
  - **Files:** `tests/unit/test_jwt_service.py`, `tests/integration/test_auth_flow.py`

### 📚 Documentation

- **Authentication API documentation with examples**: Complete API docs with curl examples and error codes
  - **Location:** `docs/api/authentication.md`, added to main README
  - **Importance:** Enables developers to integrate authentication without trial-and-error

## Components Affected

- **Authentication**: Complete new authentication system
- **API Layer**: New /api/auth endpoints
- **User Model**: Added password hashing and token tracking
- **Database**: New refresh_tokens table

## Breaking Changes ⚠️

- **User model now requires password hashing**
  - **Migration:** Run `python scripts/migrate_passwords.py` to hash existing plaintext passwords
  - **Affected:** `src/models/user.py`, all user creation code
  - **Commits:** abc123f, def456g

---

*Generated by Claude Code `/create-merge-request-changelog` skill*

Tips for Best Results

1. Use descriptive commit messages: The AI analysis works better with clear commit messages
2. Follow conventional commits: Helps with automatic categorization
3. Break up large MRs: Smaller, focused changes generate clearer descriptions
4. Review and edit: The AI-generated description is a starting point - edit for your specific context
5. Include context: Add project-specific details or link to related issues/docs

Troubleshooting

Issue: Analysis script fails

Check:

• Are you in a git repository?
• Does the base branch exist?
• Are there commits between base and head?

Solution:

git rev-parse --git-dir  # Verify git repo
git branch -a | grep <branch-name>  # Verify branch exists
git log <base>..<head>  # Verify commits exist

Issue: Too many files in diff

Solution: Use filters to focus analysis:

# Exclude generated files
git diff <base>..<head> -- . ':(exclude)dist/*' ':(exclude)*.lock'

Issue: AI categorization incorrect

Solution: The analysis is based on patterns - you can:

• Override categories manually in the final description
• Improve commit messages for future MRs
• Add project-specific detection patterns to the prompts

Complete Workflow Examples

Example 1: New MR from Git Branch

# 1. Extract git data
python scripts/analyze_git_changes.py main feature/auth-system --output-dir /tmp/analysis

# 2. Analyze with AI (using prompts from references/analysis_prompts.md)
# - Read commits and diffs from /tmp/analysis
# - Categorize changes using the primary analysis prompt
# - Save categorized JSON to /tmp/analysis/categorized.json

# 3. Format into MR description
python scripts/format_mr_description.py /tmp/analysis/categorized.json --output /tmp/mr-desc.md

# 4. Create GitLab MR
glab mr create --fill --description "$(cat /tmp/mr-desc.md)"

Example 2: Update Existing GitLab MR

# 1. Fetch existing MR data
python scripts/fetch_gitlab_mr.py 123 --output /tmp/mr-data.json

# 2. Analyze with AI (enhance existing description)
# - Read MR metadata from /tmp/mr-data.json
# - Re-analyze commits/diffs with updated prompts
# - Save enhanced JSON to /tmp/enhanced.json

# 3. Format improved description
python scripts/format_mr_description.py /tmp/enhanced.json --output /tmp/updated-mr.md

# 4. Update MR description
glab mr update 123 --description "$(cat /tmp/updated-mr.md)"

Example 3: Quick Analysis for Review

# Quick preview without saving files
python scripts/analyze_git_changes.py | \
  python scripts/format_mr_description.py --preview

CLI Scripts Reference

analyze_git_changes.py

Extracts git data between two references.

Usage: python scripts/analyze_git_changes.py [BASE_REF] [HEAD_REF] [OPTIONS]

Options:

• --output-dir PATH: Output directory (default: current directory)

Defaults: BASE_REF=main, HEAD_REF=HEAD

fetch_gitlab_mr.py

Fetches GitLab MR data using glab CLI.

Usage: python scripts/fetch_gitlab_mr.py MR_ID [OPTIONS]

Options:

• --output PATH: Output JSON file (default: stdout)
• --no-diff: Skip diff fetching (faster)

MR ID formats: 123, !123, or full URL

format_mr_description.py

Formats AI analysis into markdown MR description.

Usage: python scripts/format_mr_description.py ANALYSIS_JSON [OPTIONS]

Options:

• --output PATH: Output file (default: stdout, - for explicit stdout)
• --title TEXT: Custom MR title
• --no-preview: Skip markdown preview
• --max-files INT: Max files to show per change (default: 5)

Resources

• scripts/analyze_git_changes.py: Python CLI to extract git data
• scripts/fetch_gitlab_mr.py: Python CLI to fetch GitLab MR data via glab
• scripts/format_mr_description.py: Python CLI to format AI analysis into markdown
• scripts/README.md: Comprehensive CLI documentation and examples
• references/analysis_prompts.md: AI prompts for categorization and formatting
• references/output_template.md: MR description template structure