suggest-awesome-github-copilot-skills

作者: github

根據當前儲存庫上下文和聊天記錄,從 awesome-copilot 儲存庫中建議相關的 GitHub Copilot 技能,避免與…重複。

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

Suggest Awesome GitHub Copilot Skills

Analyze current repository context and suggest relevant Agent Skills from the GitHub awesome-copilot repository that are not already available in this repository. Agent Skills are self-contained folders located in the skills folder of the awesome-copilot repository, each containing a SKILL.md file with instructions and optional bundled assets.

Process

  1. Fetch Available Skills: Extract skills list and descriptions from awesome-copilot README.skills.md. Must use #fetch tool.
  2. Scan Local Skills: Discover existing skill folders in .github/skills/ folder
  3. Extract Descriptions: Read front matter from local SKILL.md files to get name and description
  4. Fetch Remote Versions: For each local skill, fetch the corresponding SKILL.md from awesome-copilot repository using raw GitHub URLs (e.g., https://raw.githubusercontent.com/github/awesome-copilot/main/skills/<skill-name>/SKILL.md)
  5. Compare Versions: Compare local skill content with remote versions to identify:
    • Skills that are up-to-date (exact match)
    • Skills that are outdated (content differs)
    • Key differences in outdated skills (description, instructions, bundled assets)
  6. Analyze Context: Review chat history, repository files, and current project needs
  7. Compare Existing: Check against skills already available in this repository
  8. Match Relevance: Compare available skills against identified patterns and requirements
  9. Present Options: Display relevant skills with descriptions, rationale, and availability status including outdated skills
  10. Validate: Ensure suggested skills would add value not already covered by existing skills
  11. Output: Provide structured table with suggestions, descriptions, and links to both awesome-copilot skills and similar local skills AWAIT user request to proceed with installation or updates of specific skills. DO NOT INSTALL OR UPDATE UNLESS DIRECTED TO DO SO.
  12. Download/Update Assets: For requested skills, automatically:
    • Download new skills to .github/skills/ folder, preserving the folder structure
    • Update outdated skills by replacing with latest version from awesome-copilot
    • Download both SKILL.md and any bundled assets (scripts, templates, data files)
    • 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, .ts, etc.)
  • Framework indicators (ASP.NET, React, Azure, Next.js, etc.)
  • Project types (web apps, APIs, libraries, tools, infrastructure)
  • Development workflow requirements (testing, CI/CD, deployment)
  • Infrastructure and cloud providers (Azure, AWS, GCP)

🗨️ Chat History Context:

  • Recent discussions and pain points
  • Feature requests or implementation needs
  • Code review patterns
  • Development workflow requirements
  • Specialized task needs (diagramming, evaluation, deployment)

Output Format

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

Awesome-Copilot SkillDescriptionBundled AssetsAlready InstalledSimilar Local SkillSuggestion Rationale
gh-cliGitHub CLI skill for managing repositories and workflowsNone❌ NoNoneWould enhance GitHub workflow automation capabilities
aspireAspire skill for distributed application development9 reference files✅ YesaspireAlready covered by existing Aspire skill
terraform-azurerm-set-diff-analyzerAnalyze Terraform AzureRM provider changesReference files⚠️ Outdatedterraform-azurerm-set-diff-analyzerInstructions updated with new validation patterns - Update recommended

Local Skills Discovery Process

  1. List all folders in .github/skills/ directory
  2. For each folder, read SKILL.md front matter to extract name and description
  3. List any bundled assets within each skill folder
  4. Build comprehensive inventory of existing skills with their capabilities
  5. Use this inventory to avoid suggesting duplicates

Version Comparison Process

  1. For each local skill folder, construct the raw GitHub URL to fetch the remote SKILL.md:
    • Pattern: https://raw.githubusercontent.com/github/awesome-copilot/main/skills/<skill-name>/SKILL.md
  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 (name, description)
    • Instruction updates (guidelines, examples, best practices)
    • Bundled asset changes (new, removed, or modified assets)
  5. Document key differences for outdated skills
  6. Calculate similarity to determine if update is needed

Skill Structure Requirements

Based on the Agent Skills specification, each skill is a folder containing:

  • SKILL.md: Main instruction file with front matter (name, description) and detailed instructions
  • Optional bundled assets: Scripts, templates, reference data, and other files referenced from SKILL.md
  • Folder naming: Lowercase with hyphens (e.g., azure-deployment-preflight)
  • Name matching: The name field in SKILL.md front matter must match the folder name

Front Matter Structure

Skills in awesome-copilot use this front matter format in SKILL.md:

---
name: 'skill-name'
description: 'Brief description of what this skill provides and when to use it'
---

Requirements

  • Use fetch tool to get content from awesome-copilot repository skills documentation
  • Use githubRepo tool to get individual skill content for download
  • Scan local file system for existing skills in .github/skills/ directory
  • Read YAML front matter from local SKILL.md files to extract names and descriptions
  • Compare local skills with remote versions to detect outdated skills
  • Compare against existing skills in this repository to avoid duplicates
  • Focus on gaps in current skill library coverage
  • Validate that suggested skills align with repository's purpose and technology stack
  • Provide clear rationale for each suggestion
  • Include links to both awesome-copilot skills and similar local skills
  • Clearly identify outdated skills with specific differences noted
  • Consider bundled asset requirements and compatibility
  • 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 skills 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 skill folder with remote version
  5. Preserve folder location in .github/skills/ directory
  6. Ensure all bundled assets are downloaded alongside the updated SKILL.md

來自 github 的更多技能

console-rendering
github
在 Go 中使用基於結構體標籤的控制台渲染系統的說明
official
acquire-codebase-knowledge
github
當使用者明確要求對現有程式碼庫進行映射、文件化或入門引導時,使用此技能。觸發詞如「映射此程式碼庫」、「文件化…」等提示。
official
acreadiness-assess
github
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc…
official
acreadiness-generate-instructions
github
透過 AgentRC 指令命令生成量身打造的 AI 代理指令檔案。產生 .github/copilot-instructions.md(預設,建議用於 VS Code 中的 Copilot…
official
acreadiness-policy
github
幫助使用者選取、撰寫或套用 AgentRC 政策。政策可透過停用不相關的檢查、覆寫影響/等級、設定…來自訂整備度評分。
official
add-educational-comments
github
為程式碼檔案添加教育性註解,將其轉化為有效的學習資源。根據三個可設定的知識層級(初學者、中級、進階)調整解釋深度與語氣。若未提供檔案,會自動請求提供,並以編號清單對應以便快速選取。僅透過教育性註解將檔案擴充最多125%(嚴格上限:400行新註解;超過1,000行的檔案上限為300行)。保留檔案編碼、縮排風格、語法正確性及……
official
adobe-illustrator-scripting
github
使用 ExtendScript (JavaScript/JSX) 編寫、除錯及最佳化 Adobe Illustrator 自動化腳本。適用於建立或修改操控…的腳本時。
official
agent-governance
github
宣告式政策、意圖分類與稽核軌跡,用於控制AI代理工具存取與行為。可組合的治理政策定義允許/封鎖的工具、內容過濾器、速率限制與核准要求——以配置而非程式碼形式儲存。語意意圖分類在工具執行前,透過基於模式的訊號偵測危險提示(資料外洩、權限提升、提示注入)。工具層級治理裝飾器在函式層級強制執行政策……
official