github-ai-features-2025

🚨 CRITICAL GUIDELINES

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "github-ai-features-2025" with this command: npx skills add josiahsiegel/claude-plugin-marketplace/josiahsiegel-claude-plugin-marketplace-github-ai-features-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

  • βœ… 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

  • Short-lived branches: Hours to 1 day maximum

  • Small, frequent commits: Reduce merge conflicts

  • Continuous integration: Always deployable main branch

  • 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:

""" β›” 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 # βœ… 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,)) # βœ… 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

βœ… 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)

  • Enable Secret Scanning:

Repository Settings β†’ Security β†’ Secret scanning

Enable: Push protection + AI detection

  • Configure CodeQL:

Add .github/workflows/codeql.yml

Enable for all languages in project

  • Use Copilot Autofix:

Review security alerts weekly

Apply Copilot-suggested fixes

Test before merging

  • Implement Trunk-Based Development:

Branch lifespan: <1 day

Commit frequency: Every 2-4 hours

Main branch: Always deployable

  • 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

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Coding

defender-for-devops

No summary provided by upstream source.

Repository SourceNeeds Review
Coding

github-actions-2025

No summary provided by upstream source.

Repository SourceNeeds Review
Coding

agent-development

No summary provided by upstream source.

Repository SourceNeeds Review
Coding

skill-development

No summary provided by upstream source.

Repository SourceNeeds Review