suggest-awesome-github-copilot-agents

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-agents
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summary

Analyze current repository context and suggest relevant Custom Agents files from the GitHub awesome-copilot repository that are not already available in this repository. Custom Agent files are located in the agents folder of the awesome-copilot repository.

skill.md

Suggest Awesome GitHub Copilot Custom Agents

Analyze current repository context and suggest relevant Custom Agents files from the GitHub awesome-copilot repository that are not already available in this repository. Custom Agent files are located in the agents folder of the awesome-copilot repository.

Process

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    • Agents that are outdated (content differs)
    • Key differences in outdated agents (tools, description, content)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 Present Options: Display relevant custom agents with descriptions, rationale, and availability status including outdated agents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    • Update outdated agents 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

Awesome-Copilot Custom Agent Description Already Installed Similar Local Custom Agent Suggestion Rationale
amplitude-experiment-implementation.agent.md This custom agent uses Amplitude's MCP tools to deploy new experiments inside of Amplitude, enabling seamless variant testing capabilities and rollout of product features ❌ No None Would enhance experimentation capabilities within the product
launchdarkly-flag-cleanup.agent.md Feature flag cleanup agent for LaunchDarkly ✅ Yes launchdarkly-flag-cleanup.agent.md Already covered by existing LaunchDarkly custom agents
principal-software-engineer.agent.md Provide principal-level software engineering guidance with focus on engineering excellence, technical leadership, and pragmatic implementation. ⚠️ Outdated principal-software-engineer.agent.md Tools configuration differs: remote uses 'web/fetch' vs local 'fetch' - Update recommended

Local Agent Discovery Process

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

Version Comparison Process

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

Requirements

  • Use githubRepo tool to get content from awesome-copilot repository agents folder
  • Scan local file system for existing agents in .github/agents/ directory
  • Read YAML front matter from local agent files to extract descriptions
  • Compare local agents with remote versions to detect outdated agents
  • Compare against existing agents in this repository to avoid duplicates
  • Focus on gaps in current agent library coverage
  • Validate that suggested agents align with repository's purpose and standards
  • Provide clear rationale for each suggestion
  • Include links to both awesome-copilot agents and similar local agents
  • Clearly identify outdated agents 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 agents 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/agents/ directory
how to use suggest-awesome-github-copilot-agents

How to use suggest-awesome-github-copilot-agents 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-agents
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-agents

The skills CLI fetches suggest-awesome-github-copilot-agents 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-agents

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

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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. 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)
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general reviews

Ratings

4.662 reviews
  • Ganesh Mohane· Dec 28, 2024

    I recommend suggest-awesome-github-copilot-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Valentina Jain· Dec 28, 2024

    Keeps context tight: suggest-awesome-github-copilot-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Soo Abebe· Dec 24, 2024

    suggest-awesome-github-copilot-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Omar Reddy· Dec 20, 2024

    Keeps context tight: suggest-awesome-github-copilot-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Mateo Wang· Dec 4, 2024

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

  • Zara Diallo· Nov 27, 2024

    I recommend suggest-awesome-github-copilot-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • William Jain· Nov 23, 2024

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

  • Yuki Agarwal· Nov 19, 2024

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

  • Yuki Bansal· Nov 15, 2024

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

  • Tariq Garcia· Nov 11, 2024

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

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