gh-josiahsiegel-claude-code-marketplace-plugins-git-master/skills/github-ai-features-2025.md

name, description

name	description
github-ai-features-2025	GitHub AI-powered security and automation features for 2025

🚨 CRITICAL GUIDELINES

Windows File Path Requirements

MANDATORY: Always Use Backslashes on Windows for File Paths

When using Edit or Write tools on Windows, you MUST use backslashes (\) in file paths, NOT forward slashes (/).

Examples:

• ❌ WRONG: D:/repos/project/file.tsx
• ✅ CORRECT: D:\repos\project\file.tsx

This applies to:

• Edit tool file_path parameter
• Write tool file_path parameter
• All file operations on Windows systems

Documentation Guidelines

NEVER create new documentation files unless explicitly requested by the user.

• Priority: Update existing README.md files rather than creating new documentation
• Repository cleanliness: Keep repository root clean - only README.md unless user requests otherwise
• Style: Documentation should be concise, direct, and professional - avoid AI-generated tone
• User preference: Only create additional .md files when user specifically asks for documentation

---

GitHub AI Features 2025

Trunk-Based Development (TBD)

Modern workflow used by largest tech companies (Google: 35,000+ developers):

Principles

1. Short-lived branches: Hours to 1 day maximum
2. Small, frequent commits: Reduce merge conflicts
3. Continuous integration: Always deployable main branch
4. Feature flags: Hide incomplete features

Implementation

# Create task branch from main
git checkout main
git pull origin main
git checkout -b task/add-login-button

# Make small changes
git add src/components/LoginButton.tsx
git commit -m "feat: add login button component"

# Push and create PR (same day)
git push origin task/add-login-button
gh pr create --title "Add login button" --body "Implements login UI"

# Merge within hours, delete branch
gh pr merge --squash --delete-branch

Benefits

• Reduced merge conflicts (75% decrease)
• Faster feedback cycles
• Easier code reviews (smaller changes)
• Always releasable main branch
• Simplified CI/CD pipelines

GitHub Secret Protection (AI-Powered)

AI detects secrets before they reach repository:

Push Protection

# Attempt to commit secret
git add config.py
git commit -m "Add config"
git push

# GitHub AI detects secret:
"""
⛔ Push blocked by secret scanning

Found: AWS Access Key
Pattern: AKIA[0-9A-Z]{16}
File: config.py:12

Options:
1. Remove secret and try again
2. Mark as false positive (requires justification)
3. Request review from admin
"""

# Fix: Use environment variables
# config.py
import os
aws_key = os.environ.get('AWS_ACCESS_KEY')

git add config.py
git commit -m "Use env vars for secrets"
git push  # ✅ Success

Supported Secret Types (AI-Enhanced)

• AWS credentials
• Azure service principals
• Google Cloud keys
• GitHub tokens
• Database connection strings
• API keys (OpenAI, Stripe, etc.)
• Private keys (SSH, TLS)
• OAuth tokens
• Custom patterns (regex-based)

GitHub Code Security

CodeQL Code Scanning

AI-powered static analysis:

# .github/workflows/codeql.yml
name: "CodeQL"

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  analyze:
    runs-on: ubuntu-latest
    permissions:
      security-events: write

    steps:
    - name: Checkout
      uses: actions/checkout@v3

    - name: Initialize CodeQL
      uses: github/codeql-action/init@v2
      with:
        languages: javascript, python, java

    - name: Autobuild
      uses: github/codeql-action/autobuild@v2

    - name: Perform CodeQL Analysis
      uses: github/codeql-action/analyze@v2

Detects:

• SQL injection
• XSS vulnerabilities
• Path traversal
• Command injection
• Insecure deserialization
• Authentication bypass
• Logic errors

Copilot Autofix

AI automatically fixes security vulnerabilities:

# Vulnerable code detected by CodeQL
def get_user(user_id):
    query = f"SELECT * FROM users WHERE id = {user_id}"  # ❌ SQL injection
    return db.execute(query)

# Copilot Autofix suggests:
def get_user(user_id):
    query = "SELECT * FROM users WHERE id = ?"
    return db.execute(query, (user_id,))  # ✅ Parameterized query

# One-click to apply fix

GitHub Agents (Automated Workflows)

AI agents for automated bug fixes and PR generation:

Bug Fix Agent

# .github/workflows/ai-bugfix.yml
name: AI Bug Fixer

on:
  issues:
    types: [labeled]

jobs:
  autofix:
    if: contains(github.event.issue.labels.*.name, 'bug')
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3

    - name: Analyze Bug
      uses: github/ai-agent@v1
      with:
        task: 'analyze-bug'
        issue-number: ${{ github.event.issue.number }}

    - name: Generate Fix
      uses: github/ai-agent@v1
      with:
        task: 'generate-fix'
        create-pr: true
        pr-title: "Fix: ${{ github.event.issue.title }}"

Automated PR Generation

# GitHub Agent creates PR automatically
# When issue is labeled "enhancement":
# 1. Analyzes issue description
# 2. Generates implementation code
# 3. Creates tests
# 4. Opens PR with explanation

# Example: Issue #42 "Add dark mode toggle"
# Agent creates PR with:
# - DarkModeToggle.tsx component
# - ThemeContext.tsx provider
# - Tests for theme switching
# - Documentation update

Dependency Review (AI-Enhanced)

AI analyzes dependency changes in PRs:

# .github/workflows/dependency-review.yml
name: Dependency Review

on: [pull_request]

permissions:
  contents: read

jobs:
  dependency-review:
    runs-on: ubuntu-latest
    steps:
    - name: Checkout
      uses: actions/checkout@v3

    - name: Dependency Review
      uses: actions/dependency-review-action@v3
      with:
        fail-on-severity: high
        fail-on-scopes: runtime

AI Insights:

• Known vulnerabilities in new dependencies
• License compliance issues
• Breaking changes in updates
• Alternative safer packages
• Dependency freshness score

Trunk-Based Development Workflow

Daily Workflow

# Morning: Sync with main
git checkout main
git pull origin main

# Create task branch
git checkout -b task/user-profile-api

# Work in small iterations (2-4 hours)
# First iteration: API endpoint
git add src/api/profile.ts
git commit -m "feat: add profile API endpoint"
git push origin task/user-profile-api
gh pr create --title "Add user profile API" --draft

# Continue work: Add tests
git add tests/profile.test.ts
git commit -m "test: add profile API tests"
git push

# Mark ready for review
gh pr ready
# Get review (should happen within hours)

# Merge same day
gh pr merge --squash --delete-branch

# Next task: Start fresh from main
git checkout main
git pull origin main
git checkout -b task/profile-ui

Small, Frequent Commits Pattern

# ❌ Bad: Large infrequent commit
git add .
git commit -m "Add complete user profile feature with API, UI, tests, docs"
# 50 files changed, 2000 lines

# ✅ Good: Small frequent commits
git add src/api/profile.ts
git commit -m "feat: add profile API endpoint"
git push

git add src/components/ProfileCard.tsx
git commit -m "feat: add profile card component"
git push

git add tests/profile.test.ts
git commit -m "test: add profile tests"
git push

git add docs/profile.md
git commit -m "docs: document profile API"
git push

# Each commit: 1-3 files, 50-200 lines
# Easier reviews, faster merges, less conflicts

Security Best Practices (2025)

1. Enable Secret Scanning:

# Repository Settings → Security → Secret scanning
# Enable: Push protection + AI detection

2. Configure CodeQL:

# Add .github/workflows/codeql.yml
# Enable for all languages in project

3. Use Copilot Autofix:

# Review security alerts weekly
# Apply Copilot-suggested fixes
# Test before merging

4. Implement Trunk-Based Development:

# Branch lifespan: <1 day
# Commit frequency: Every 2-4 hours
# Main branch: Always deployable

5. Leverage GitHub Agents:

# Automate: Bug triage, PR creation, dependency updates
# Review: All AI-generated code before merging

Resources

• Trunk-Based Development
• GitHub Secret Scanning
• GitHub Advanced Security
• GitHub Copilot for Security