github-workflow-automation

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

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$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill github-workflow-automation
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summary

AI-assisted automation for GitHub workflows including PR reviews, issue triage, CI/CD integration, and Git operations.

  • Automated PR reviews with diff analysis, issue detection, and formatted feedback comments
  • Issue triage with AI classification, auto-labeling, severity assessment, and stale issue management
  • Smart test selection and deployment validation based on changed files and risk analysis
  • Git operations including automated rebasing, conflict resolution, cherry-pick assistance
skill.md

🔧 GitHub Workflow Automation

Patterns for automating GitHub workflows with AI assistance, inspired by Gemini CLI and modern DevOps practices.

When to Use This Skill

Use this skill when:

  • Automating PR reviews with AI
  • Setting up issue triage automation
  • Creating GitHub Actions workflows
  • Integrating AI into CI/CD pipelines
  • Automating Git operations (rebases, cherry-picks)

1. Automated PR Review

1.1 PR Review Action

# .github/workflows/ai-review.yml
name: AI Code Review

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  review:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      pull-requests: write

    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0

      - name: Get changed files
        id: changed
        run: |
          files=$(git diff --name-only origin/${{ github.base_ref }}...HEAD)
          echo "files<<EOF" >> $GITHUB_OUTPUT
          echo "$files" >> $GITHUB_OUTPUT
          echo "EOF" >> $GITHUB_OUTPUT

      - name: Get diff
        id: diff
        run: |
          diff=$(git diff origin/${{ github.base_ref }}...HEAD)
          echo "diff<<EOF" >> $GITHUB_OUTPUT
          echo "$diff" >> $GITHUB_OUTPUT
          echo "EOF" >> $GITHUB_OUTPUT

      - name: AI Review
        uses: actions/github-script@v7
        with:
          script: |
            const { Anthropic } = require('@anthropic-ai/sdk');
            const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

            const response = await client.messages.create({
              model: "claude-3-sonnet-20240229",
              max_tokens: 4096,
              messages: [{
                role: "user",
                content: `Review this PR diff and provide feedback:
                
                Changed files: ${{ steps.changed.outputs.files }}
                
                Diff:
                ${{ steps.diff.outputs.diff }}
                
                Provide:
                1. Summary of changes
                2. Potential issues or bugs
                3. Suggestions for improvement
                4. Security concerns if any
                
                Format as GitHub markdown.`
              }]
            });

            await github.rest.pulls.createReview({
              owner: context.repo.owner,
              repo: context.repo.repo,
              pull_number: context.issue.number,
              body: response.content[0].text,
              event: 'COMMENT'
            });
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

1.2 Review Comment Patterns

# AI Review Structure

## 📋 Summary

Brief description of what this PR does.

## ✅ What looks good

- Well-structured code
- Good test coverage
- Clear naming conventions

## ⚠️ Potential Issues

1. **Line 42**: Possible null pointer exception
   ```javascript
   // Current
   user.profile.name;
   // Suggested
   user?.profile?.name ?? "Unknown";
   ```
  1. Line 78: Consider error handling
    // Add try-catch or .catch()
    

💡 Suggestions

  • Consider extracting the validation logic into a separate function
  • Add JSDoc comments for public methods

🔒 Security Notes

  • No sensitive data exposure detected
  • API key handling looks correct

### 1.3 Focused Reviews

```yaml
# Review only specific file types
- name: Filter code files
  run: |
    files=$(git diff --name-only origin/${{ github.base_ref }}...HEAD | \
            grep -E '\.(ts|tsx|js|jsx|py|go)$' || true)
    echo "code_files=$files" >> $GITHUB_OUTPUT

# Review with context
- name: AI Review with context
  run: |
    # Include relevant context files
    context=""
    for file in ${{ steps.changed.outputs.files }}; do
      if [[ -f "$file" ]]; then
        context+="=== $file ===\n$(cat $file)\n\n"
      fi
    done

    # Send to AI with full file context

2. Issue Triage Automation

2.1 Auto-label Issues

# .github/workflows/issue-triage.yml
name: Issue Triage

on:
  issues:
    types: [opened]

jobs:
  triage:
    runs-on: ubuntu-latest
    permissions:
      issues: write

    steps:
      - name: Analyze issue
        uses: actions/github-script@v7
        with:
          script: |
            const issue = context.payload.issue;

            // Call AI to analyze
            const analysis = await analyzeIssue(issue.title, issue.body);

            // Apply labels
            const labels = [];

            if (analysis.type === 'bug') {
              labels.push('bug');
              if (analysis.severity === 'high') labels.push('priority: high');
            } else if (analysis.type === 'feature') {
              labels.push('enhancement');
            } else if (analysis.type === 'question') {
              labels.push('question');
            }

            if (analysis.area) {
              labels.push(`area: ${analysis.area}`);
            }

            await github.rest.issues.addLabels({
              owner: context.repo.owner,
              repo: context.repo.repo,
              issue_number: issue.number,
              labels: labels
            });

            // Add initial response
            if (analysis.type === 'bug' && !analysis.hasReproSteps) {
              await github.rest.issues.createComment({
                owner: context.repo.owner,
                repo: context.repo.repo,
                issue_number: issue.number,
                body: `Thanks for reporting this issue!

To help us investigate, could you please provide:
- Steps to reproduce the issue
- Expected behavior
- Actual behavior
- Environment (OS, version, etc.)

This will help us resolve your issue faster. 🙏`
              });
            }

2.2 Issue Analysis Prompt

const TRIAGE_PROMPT = `
Analyze this GitHub issue and classify it:

Title: {title}
Body: {body}

Return JSON with:
{
  "type": "bug" | "feature" | "question" | "docs" | "other",
  "severity": "low" | "medium" | "high" | "critical",
  "area": "frontend" | "backend" | "api" | "docs" | "ci" | "other",
  "summary": "one-line summary",
  "hasReproSteps": boolean,
  "isFirstContribution": boolean,
how to use github-workflow-automation

How to use github-workflow-automation on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add github-workflow-automation
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill github-workflow-automation

The skills CLI fetches github-workflow-automation from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/github-workflow-automation

Reload or restart Cursor to activate github-workflow-automation. Access the skill through slash commands (e.g., /github-workflow-automation) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.647 reviews
  • Pratham Ware· Dec 24, 2024

    We added github-workflow-automation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ama Okafor· Dec 20, 2024

    Registry listing for github-workflow-automation matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Olivia Torres· Dec 12, 2024

    Keeps context tight: github-workflow-automation is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aditi Bhatia· Dec 4, 2024

    github-workflow-automation reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ama Mensah· Dec 4, 2024

    I recommend github-workflow-automation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Noah Thomas· Nov 23, 2024

    github-workflow-automation has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Henry Rahman· Nov 23, 2024

    github-workflow-automation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mia Rahman· Nov 23, 2024

    Useful defaults in github-workflow-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yash Thakker· Nov 15, 2024

    github-workflow-automation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Charlotte Kapoor· Nov 11, 2024

    Solid pick for teams standardizing on skills: github-workflow-automation is focused, and the summary matches what you get after install.

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