MCPInstall

suggest-awesome-github-copilot-prompts

by github OfficialAI & Agents

Suggest relevant GitHub Copilot prompt files from the awesome-copilot repository based on current repository context and chat history, avoiding duplicates with existing prompts in this repository, and identifying outdated prompts that need updates.

Installs
6.8K
Last 24h
—
Trending rank
—
Security
Warn

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/github/awesome-copilot --skill suggest-awesome-github-copilot-prompts
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads suggest-awesome-github-copilot-prompts automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·This skill suggests and updates GitHub Copilot prompts by retrieving content from the official awesome-copilot repository. All network and file operations are consistent with its documented purpose and utilize official vendor infrastructure for updates.
Socket PassNo alerts
Snyk WarnMEDIUM ·Risk: MEDIUM · No issues
Runlayer WarnMEDIUM ·1/1 file flagged

SKILL.md

The instructions your agent reads when the skill is triggered.

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    • Prompts that are outdated (content differs)
    • Key differences in outdated prompts (tools, description, content)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 PromptDescriptionAlready InstalledSimilar Local PromptSuggestion Rationale
code-review.prompt.mdAutomated code review prompts❌ NoNoneWould enhance development workflow with standardized code review processes
documentation.prompt.mdGenerate project documentation✅ Yescreate_oo_component_documentation.prompt.mdAlready covered by existing documentation prompts
debugging.prompt.mdDebug assistance prompts⚠️ Outdateddebugging.prompt.mdTools 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

FAQ

What does the suggest-awesome-github-copilot-prompts skill do?

Suggest relevant GitHub Copilot prompt files from the awesome-copilot repository based on current repository context and chat history, avoiding duplicates with existing prompts in this repository, and identifying outdated prompts that need updates.

How do I install suggest-awesome-github-copilot-prompts?

Run npx skills add https://github.com/github/awesome-copilot --skill suggest-awesome-github-copilot-prompts in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/suggest-awesome-github-copilot-prompts/ (personal) or .claude/skills/suggest-awesome-github-copilot-prompts/ (project).

Which AI agents can use suggest-awesome-github-copilot-prompts?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is suggest-awesome-github-copilot-prompts safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk flagged items to review; Runlayer flagged items to review. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains suggest-awesome-github-copilot-prompts?

It is published by github in github/awesome-copilot, and it is an official skill from the maker of the technology it covers. It has 6,765 recorded installs.

More from github/awesome-copilot

View all →

git-commit

github/awesome-copilot

Use this skill when the user wants to make a Git commit or split changes into more than one commit. Also use it when the user types "/commit" or asks for a conventional commit. The skill examines the diff, stages the related files, writes a Conventional Commits message, and runs git commit. Do not use it when the user wants only a commit message and no commit. Do not use it to push, open a pull request, rebase, or change the commit history.

OfficialCode Quality & Workflow47.4K installs

excalidraw-diagram-generator

github/awesome-copilot

Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.

OfficialDocs & Writing31.9K installs

multi-stage-dockerfile

github/awesome-copilot

Create optimized multi-stage Dockerfiles for any language or framework

Official29.8K installs

documentation-writer

github/awesome-copilot

Diátaxis Documentation Expert. An expert technical writer specializing in creating high-quality software documentation, guided by the principles and structure of the Diátaxis technical documentation authoring framework.

OfficialDocs & Writing27.9K installs

prd

github/awesome-copilot

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

Official24.1K installs

refactor

github/awesome-copilot

Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.

OfficialCode Quality & Workflow22.4K installs

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs