copilot-coding-agent▌
supercent-io/skills-template · updated Apr 8, 2026
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Automate issue-to-Draft-PR pipeline by labeling issues for GitHub Copilot assignment.
- ›Label an issue with ai-copilot to trigger GitHub Actions, which auto-assigns Copilot via GraphQL and initiates code generation, branch creation, and Draft PR opening
- ›Requires GitHub Copilot Pro+, Business, or Enterprise; one-time setup deploys the workflow, registers a PAT secret, and creates the label
- ›Copilot PRs are treated as external contributions and require manual approval before CI runs; subs
GitHub Copilot Coding Agent — Issue → Draft PR automation
If you add the
ai-copilotlabel to an issue, GitHub Actions automatically assigns it to Copilot, and Copilot creates a branch → writes code → opens a Draft PR.
When to use this skill
- When PMs/designers create issues and Copilot starts implementation without a developer
- When offloading backlog issues (refactors/docs/tests) to Copilot
- When delegating follow-up work created by Vibe Kanban / Conductor to Copilot
- When automating pipelines like Jira → GitHub Issue → Copilot PR
Prerequisites
- GitHub plan: Copilot Pro+, Business, or Enterprise
- Copilot Coding Agent enabled: Must be enabled in repo settings
- gh CLI: Authenticated
- PAT: Personal Access Token with
reposcope
One-time setup
# One-click setup (register token + deploy workflow + create label)
bash scripts/copilot-setup-workflow.sh
This script does:
- Register
COPILOT_ASSIGN_TOKENas a repo secret - Deploy
.github/workflows/assign-to-copilot.yml - Create the
ai-copilotlabel
Usage
Option 1: GitHub Actions automation (recommended)
# Create issue + ai-copilot label → auto-assign Copilot
gh issue create \
--label ai-copilot \
--title "Add user authentication" \
--body "Implement JWT-based auth with refresh tokens. Include login, logout, refresh endpoints."
Option 2: Add a label to an existing issue
# Add label to issue #42 → trigger Actions
gh issue edit 42 --add-label ai-copilot
Option 3: Assign directly via script
export COPILOT_ASSIGN_TOKEN=<your-pat>
bash scripts/copilot-assign-issue.sh 42
How it works (technical)
Issue created/labeled
↓
GitHub Actions triggered (assign-to-copilot.yml)
↓
Look up Copilot bot ID via GraphQL
↓
replaceActorsForAssignable → set Copilot as assignee
↓
Copilot Coding Agent starts processing the issue
↓
Create branch → write code → open Draft PR
↓
Auto-assign you as PR reviewer
Required GraphQL header:
GraphQL-Features: issues_copilot_assignment_api_support,coding_agent_model_selection
GitHub Actions workflows
| Workflow | Trigger | Purpose |
|---|---|---|
assign-to-copilot.yml |
Issue labeled ai-copilot |
Auto-assign to Copilot |
copilot-pr-ci.yml |
PR open/update | Run CI (build + tests) |
Copilot PR limitations
Copilot is treated like an external contributor.
- PRs are created as Draft by default
- Before the first Actions run, a manual approval from someone with write access is required
- After approval,
copilot-pr-ci.ymlCI runs normally
# Check CI after manual approval
gh pr list --search 'head:copilot/'
gh pr view <pr-number>
planno (plannotator) integration — optional
Review the issue spec in planno before assigning to Copilot (independent skill, not required):
Review and approve this issue spec in planno
After approval, add the ai-copilot label → trigger Actions.
Common use cases
1. Label-based Copilot queue
PM writes an issue → add ai-copilot label
→ Actions auto-assigns → Copilot creates Draft PR
→ Team only performs PR review
2. Combined with Vibe Kanban / Conductor
Follow-up issues created by Vibe Kanban:
refactors/docs cleanup/add tests
→ ai-copilot label → Copilot handles
→ Team focuses on main feature development
3. External system integration
Jira issue → Zapier/webhook → auto-create GitHub Issue
→ ai-copilot label → Copilot PR
→ Fully automated pipeline
4. Refactoring backlog processing
# Bulk-add label to backlog issues
gh issue list --label "tech-debt" --json number \
| jq '.[].number' \
| xargs -I{} gh issue edit {} --add-label ai-copilot
Check results
# List PRs created by Copilot
gh pr list --search 'head:copilot/'
# Specific issue status
gh issue view 42
# PR CI status
gh pr checks <pr-number>
References
- GitHub Copilot Coding Agent overview
- Ask Copilot to create a PR (GraphQL example)
- Official docs: assign Copilot to an issue
- Copilot PR permissions/limitations
- GitHub Copilot coding agent (VSCode docs)
Quick Reference
=== Setup ===
bash scripts/copilot-setup-workflow.sh one-time setup
=== Issue assignment ===
gh issue create --label ai-copilot ... new issue + auto-assign
gh issue edit <num> --add-label ai-copilot existing issue
bash scripts/copilot-assign-issue.sh <num> manual assign
=== Verify results ===
gh pr list --search 'head:copilot/' Copilot PR list
gh pr view <num> PR details
gh pr checks <num> CI status
=== Constraints ===
Copilot Pro+/Business/Enterprise required
First PR requires manual approval (treated as an external contributor)
PAT: repo scope required
How to use copilot-coding-agent 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 copilot-coding-agent
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches copilot-coding-agent from GitHub repository supercent-io/skills-template 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 copilot-coding-agent. Access the skill through slash commands (e.g., /copilot-coding-agent) 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
4.5★★★★★52 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
We added copilot-coding-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Anika Robinson· Dec 24, 2024
Solid pick for teams standardizing on skills: copilot-coding-agent is focused, and the summary matches what you get after install.
- ★★★★★Sophia Tandon· Dec 20, 2024
Registry listing for copilot-coding-agent matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Rahul Santra· Nov 19, 2024
Useful defaults in copilot-coding-agent — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aanya Malhotra· Nov 15, 2024
copilot-coding-agent has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★William Khanna· Nov 11, 2024
copilot-coding-agent reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Pratham Ware· Oct 10, 2024
Registry listing for copilot-coding-agent matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★William Johnson· Oct 6, 2024
copilot-coding-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Brown· Oct 2, 2024
We added copilot-coding-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Arjun Chawla· Sep 13, 2024
copilot-coding-agent has been reliable in day-to-day use. Documentation quality is above average for community skills.
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