suggest-awesome-github-copilot-prompts

github/awesome-copilot · updated Apr 8, 2026

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$npx skills add https://github.com/github/awesome-copilot --skill suggest-awesome-github-copilot-prompts
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summary

Analyze current repository context and suggest relevant prompt files from the GitHub awesome-copilot repository that are not already available in this repository.

skill.md

Suggest Awesome GitHub Copilot Prompts

Analyze current repository context and suggest relevant prompt files from the GitHub awesome-copilot repository that are not already available in this repository.

Process

  1. Fetch Available Prompts: Extract prompt list and descriptions from awesome-copilot README.prompts.md. Must use #fetch tool.
  2. Scan Local Prompts: Discover existing prompt files in .github/prompts/ folder
  3. Extract Descriptions: Read front matter from local prompt files to get descriptions
  4. Fetch Remote Versions: For each local prompt, fetch the corresponding version from awesome-copilot repository using raw GitHub URLs (e.g., https://raw.githubusercontent.com/github/awesome-copilot/main/prompts/<filename>)
  5. Compare Versions: Compare local prompt content with remote versions to identify:
    • Prompts that are up-to-date (exact match)
    • Prompts that are outdated (content differs)
    • Key differences in outdated prompts (tools, description, content)
  6. Analyze Context: Review chat history, repository files, and current project needs
  7. Compare Existing: Check against prompts already available in this repository
  8. Match Relevance: Compare available prompts against identified patterns and requirements
  9. Present Options: Display relevant prompts with descriptions, rationale, and availability status including outdated prompts
  10. Validate: Ensure suggested prompts would add value not already covered by existing prompts
  11. Output: Provide structured table with suggestions, descriptions, and links to both awesome-copilot prompts and similar local prompts AWAIT user request to proceed with installation or updates of specific prompts. DO NOT INSTALL OR UPDATE UNLESS DIRECTED TO DO SO.
  12. Download/Update Assets: For requested prompts, automatically:
    • Download new prompts to .github/prompts/ folder
    • Update outdated prompts by replacing with latest version from awesome-copilot
    • Do NOT adjust content of the files
    • Use #fetch tool to download assets, but may use curl using #runInTerminal tool to ensure all content is retrieved
    • Use #todos tool to track progress

Context Analysis Criteria

🔍 Repository Patterns:

  • Programming languages used (.cs, .js, .py, etc.)
  • Framework indicators (ASP.NET, React, Azure, etc.)
  • Project types (web apps, APIs, libraries, tools)
  • Documentation needs (README, specs, ADRs)

🗨️ Chat History Context:

  • Recent discussions and pain points
  • Feature requests or implementation needs
  • Code review patterns
  • Development workflow requirements

Output Format

Display analysis results in structured table comparing awesome-copilot prompts with existing repository prompts:

Awesome-Copilot Prompt Description Already Installed Similar Local Prompt Suggestion Rationale
code-review.prompt.md Automated code review prompts ❌ No None Would enhance development workflow with standardized code review processes
documentation.prompt.md Generate project documentation ✅ Yes create_oo_component_documentation.prompt.md Already covered by existing documentation prompts
debugging.prompt.md Debug assistance prompts ⚠️ Outdated debugging.prompt.md Tools configuration differs: remote uses 'codebase' vs local missing - Update recommended

Local Prompts Discovery Process

  1. List all *.prompt.md files in .github/prompts/ directory
  2. For each discovered file, read front matter to extract description
  3. Build comprehensive inventory of existing prompts
  4. Use this inventory to avoid suggesting duplicates

Version Comparison Process

  1. For each local prompt file, construct the raw GitHub URL to fetch the remote version:
    • Pattern: https://raw.githubusercontent.com/github/awesome-copilot/main/prompts/<filename>
  2. Fetch the remote version using the #fetch tool
  3. Compare entire file content (including front matter and body)
  4. Identify specific differences:
    • Front matter changes (description, tools, mode)
    • Tools array modifications (added, removed, or renamed tools)
    • Content updates (instructions, examples, guidelines)
  5. Document key differences for outdated prompts
  6. Calculate similarity to determine if update is needed

Requirements

  • Use githubRepo tool to get content from awesome-copilot repository prompts folder
  • Scan local file system for existing prompts in .github/prompts/ directory
  • Read YAML front matter from local prompt files to extract descriptions
  • Compare local prompts with remote versions to detect outdated prompts
  • Compare against existing prompts in this repository to avoid duplicates
  • Focus on gaps in current prompt library coverage
  • Validate that suggested prompts align with repository's purpose and standards
  • Provide clear rationale for each suggestion
  • Include links to both awesome-copilot prompts and similar local prompts
  • Clearly identify outdated prompts with specific differences noted
  • Don't provide any additional information or context beyond the table and the analysis

Icons Reference

  • ✅ Already installed and up-to-date
  • ⚠️ Installed but outdated (update available)
  • ❌ Not installed in repo

Update Handling

When outdated prompts are identified:

  1. Include them in the output table with ⚠️ status
  2. Document specific differences in the "Suggestion Rationale" column
  3. Provide recommendation to update with key changes noted
  4. When user requests update, replace entire local file with remote version
  5. Preserve file location in .github/prompts/ directory
how to use suggest-awesome-github-copilot-prompts

How to use suggest-awesome-github-copilot-prompts 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 suggest-awesome-github-copilot-prompts
2

Execute installation command

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

$npx skills add https://github.com/github/awesome-copilot --skill suggest-awesome-github-copilot-prompts

The skills CLI fetches suggest-awesome-github-copilot-prompts from GitHub repository github/awesome-copilot 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/suggest-awesome-github-copilot-prompts

Reload or restart Cursor to activate suggest-awesome-github-copilot-prompts. Access the skill through slash commands (e.g., /suggest-awesome-github-copilot-prompts) 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

4.773 reviews
  • Chaitanya Patil· Dec 28, 2024

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

  • Anaya Sethi· Dec 28, 2024

    suggest-awesome-github-copilot-prompts is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Omar Ndlovu· Dec 16, 2024

    Solid pick for teams standardizing on skills: suggest-awesome-github-copilot-prompts is focused, and the summary matches what you get after install.

  • Ava Kim· Dec 16, 2024

    We added suggest-awesome-github-copilot-prompts from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Naina Lopez· Dec 8, 2024

    suggest-awesome-github-copilot-prompts has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Omar Wang· Dec 8, 2024

    suggest-awesome-github-copilot-prompts reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Rahul Santra· Nov 27, 2024

    Registry listing for suggest-awesome-github-copilot-prompts matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Neel Liu· Nov 27, 2024

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

  • Piyush G· Nov 19, 2024

    suggest-awesome-github-copilot-prompts has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Neel Thompson· Nov 19, 2024

    Registry listing for suggest-awesome-github-copilot-prompts matched our evaluation — installs cleanly and behaves as described in the markdown.

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