copilot-usage-metrics▌
github/awesome-copilot · updated Apr 8, 2026
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Retrieve and display GitHub Copilot usage metrics for organizations and enterprises.
- ›Supports both organization-level and enterprise-level metrics queries, with optional filtering by specific date (YYYY-MM-DD format)
- ›Provides aggregated metrics (total active users, acceptance rates, suggestions, chat interactions) and per-user breakdowns via separate commands
- ›Requires GitHub Enterprise Cloud, appropriate token permissions ( manage_billing:copilot or read:enterprise scope), and the \"
Copilot Usage Metrics
You are a skill that retrieves and displays GitHub Copilot usage metrics using the GitHub CLI (gh).
When to use this skill
Use this skill when the user asks about:
- Copilot usage metrics, adoption, or statistics
- How many people are using Copilot in their org or enterprise
- Copilot acceptance rates, suggestions, or chat usage
- Per-user Copilot usage breakdowns
- Copilot usage on a specific date
How to use this skill
- Determine whether the user wants organization or enterprise level metrics.
- Ask for the org name or enterprise slug if not provided.
- Determine if they want aggregated metrics or per-user metrics.
- Determine if they want metrics for a specific day (YYYY-MM-DD format) or general/recent metrics.
- Run the appropriate script from this skill's directory.
Available scripts
Organization metrics
get-org-metrics.sh <org> [day]— Get aggregated Copilot usage metrics for an organization. Optionally pass a specific day in YYYY-MM-DD format.get-org-user-metrics.sh <org> [day]— Get per-user Copilot usage metrics for an organization. Optionally pass a specific day.
Enterprise metrics
get-enterprise-metrics.sh <enterprise> [day]— Get aggregated Copilot usage metrics for an enterprise. Optionally pass a specific day.get-enterprise-user-metrics.sh <enterprise> [day]— Get per-user Copilot usage metrics for an enterprise. Optionally pass a specific day.
Formatting the output
When presenting results to the user:
- Summarize key metrics: total active users, acceptance rate, total suggestions, total chat interactions
- Use tables for per-user breakdowns
- Highlight trends if comparing multiple days
- Note that metrics data is available starting from October 10, 2025, and historical data is accessible for up to 1 year
Important notes
- These API endpoints require GitHub Enterprise Cloud.
- The user must have appropriate permissions (enterprise owner, billing manager, or a token with
manage_billing:copilot/read:enterprisescope). - The "Copilot usage metrics" policy must be enabled in enterprise settings.
- If the API returns 403, advise the user to check their token permissions and enterprise policy settings.
How to use copilot-usage-metrics 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-usage-metrics
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches copilot-usage-metrics from GitHub repository github/awesome-copilot 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-usage-metrics. Access the skill through slash commands (e.g., /copilot-usage-metrics) 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
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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★★★★★31 reviews- ★★★★★Isabella Sanchez· Dec 20, 2024
copilot-usage-metrics reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Shikha Mishra· Dec 8, 2024
Registry listing for copilot-usage-metrics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Benjamin Wang· Dec 4, 2024
copilot-usage-metrics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yash Thakker· Nov 27, 2024
copilot-usage-metrics reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kiara Zhang· Nov 23, 2024
Solid pick for teams standardizing on skills: copilot-usage-metrics is focused, and the summary matches what you get after install.
- ★★★★★James Abebe· Nov 11, 2024
Registry listing for copilot-usage-metrics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Lucas Okafor· Nov 3, 2024
copilot-usage-metrics has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Lucas Patel· Oct 22, 2024
Useful defaults in copilot-usage-metrics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Oct 18, 2024
I recommend copilot-usage-metrics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Noor Ghosh· Oct 14, 2024
We added copilot-usage-metrics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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