github-workflow-automation▌
davila7/claude-code-templates · updated Apr 8, 2026
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Patterns for automating GitHub workflows with AI assistance, inspired by Gemini CLI and modern DevOps practices.
🔧 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: ${{ secrets.ANTHROPIC_API_KEY }}
1.2 Review Comment Patterns
# AI Review Structure
## 📋 Summary
Brief description of what this PR does.
## ✅ What looks good
- Well-structured code
- Good test coverage
- Clear naming conventions
## ⚠️ Potential Issues
1. **Line 42**: Possible null pointer exception
```javascript
// Current
user.profile.name;
// Suggested
user?.profile?.name ?? "Unknown";
```
- Line 78: Consider error handling
// Add try-catch or .catch()
💡 Suggestions
- Consider extracting the validation logic into a separate function
- Add JSDoc comments for public methods
🔒 Security Notes
- No sensitive data exposure detected
- API key handling looks correct
### 1.3 Focused Reviews
```yaml
# Review only specific file types
- name: Filter code files
run: |
files=$(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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches github-workflow-automation from GitHub repository davila7/claude-code-templates and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★72 reviews- ★★★★★William Mensah· Dec 24, 2024
Solid pick for teams standardizing on skills: github-workflow-automation is focused, and the summary matches what you get after install.
- ★★★★★Ava Jain· Dec 16, 2024
We added github-workflow-automation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ava Sharma· Dec 16, 2024
Solid pick for teams standardizing on skills: github-workflow-automation is focused, and the summary matches what you get after install.
- ★★★★★Kiara Wang· Dec 8, 2024
Registry listing for github-workflow-automation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ira Farah· Dec 8, 2024
Useful defaults in github-workflow-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Olivia Haddad· Dec 4, 2024
github-workflow-automation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kaira Park· Nov 27, 2024
Useful defaults in github-workflow-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noah Martinez· Nov 27, 2024
Registry listing for github-workflow-automation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hana Ghosh· Nov 15, 2024
github-workflow-automation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mia Iyer· 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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