github-repo-explore

Anleitung zum Durchsuchen und Erkunden externer GitHub-Repositories mit der gh CLI. Verwenden Sie dies, wenn Sie Referenzimplementierungen, Muster oder Codebeispiele benötigen…

npx skills add https://github.com/microsoft/semantic-link-labs --skill github-repo-explore

GitHub Repository Search and Exploration

This skill enables agents to discover, clone, and explore external GitHub repositories when implementing features that may benefit from reference implementations in open-source projects.

When to Use This Skill

Use this skill when you need to:

  • Find reference implementations for features you're building
  • Explore how other projects implement similar patterns
  • Search for code examples across GitHub
  • Clone external repos for local exploration and learning
  • Find libraries or tools that could inform your implementation

Do NOT use this skill when:

  • The task is straightforward and doesn't need external references
  • The codebase already has established patterns to follow
  • You're working on proprietary code that shouldn't reference external sources

Resource Directory

All external GitHub repositories MUST be stored in:

.agent_cache/resources/

This is the centralized location for all cloned external repositories. Always check here first before searching GitHub.

Directory Structure

.agent_cache/
└── resources/
    ├── microsoft/
    │   ├── semantic-kernel/
    │   └── TypeChat/
    ├── langchain-ai/
    │   └── langchain/
    └── Significant-Gravitas/
        └── AutoGPT/

Repositories are organized by {owner}/{repo} structure to prevent naming conflicts.


Workflow Overview

┌─────────────────────────────────────────────────────────────────┐
│  1. Check Local Cache (.agent_cache/resources/)                 │
│     └─ If found → Use local repo for exploration                │
└────────────────────────────┬────────────────────────────────────┘
                             │ Not found
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│  2. Authenticate with GitHub (if needed)                        │
│     └─ gh auth login                                            │
└────────────────────────────┬────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│  3. Search GitHub for Repositories                              │
│     └─ gh search repos <query>                                  │
│     └─ gh search code <query>                                   │
└────────────────────────────┬────────────────────────────────────┘
                             │ Found match
                             ▼
┌───────────────────────────────────────────────────────────────────┐
│  4. Clone to Cache Directory                                      │
│     └─ gh repo clone owner/repo .agent_cache/resources/owner/repo │
└────────────────────────────┬──────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│  5. Explore Repository                                          │
│     └─ Use gh commands and file reading tools                   │
└─────────────────────────────────────────────────────────────────┘

Phase 1: Check Local Cache First

Before searching GitHub, always check if a relevant repository is already cloned locally.

List Cached Repositories

# List all cached repositories
ls -la .agent_cache/resources/

# List repos by owner
ls -la .agent_cache/resources/microsoft/

# Search for specific repo by name
find .agent_cache/resources -type d -name "*semantic*" 2>/dev/null

Explore Local Repository

If the repository exists locally, use standard file tools:

# View repository structure
tree -L 2 .agent_cache/resources/microsoft/semantic-kernel/

# Search for patterns in local repo
grep -r "planning" .agent_cache/resources/microsoft/semantic-kernel/python/ --include="*.py" | head -20

# Find specific files
find .agent_cache/resources/microsoft/semantic-kernel -name "*.md" -path "*skills*"

Phase 2: GitHub Authentication

If you need to search or clone from GitHub, ensure authentication is set up.

Check Authentication Status

gh auth status

Interactive Login (if needed)

# Interactive login - user will authenticate via browser
gh auth login

This opens a browser for the user to authenticate with their GitHub account.

Verify Login

# Verify authentication worked
gh auth status

Phase 3: Search GitHub

Use gh search commands to find relevant repositories or code.

Search for Repositories

# Search repos by keywords
gh search repos "agent planning skills" --limit 10

# Search with language filter
gh search repos "agent planning" --language python --limit 10

# Search by owner
gh search repos --owner microsoft "semantic kernel" --limit 10

# Search with JSON output for parsing
gh search repos "agent planning" --json fullName,description,stargazersCount --limit 5

# Sort by stars (most popular)
gh search repos "agent planning" --sort stars --limit 10

# Filter by number of stars
gh search repos "agent framework" --stars ">1000" --limit 10

Search for Code

# Search code across GitHub
gh search code "TaskLedger" --limit 10

# Search code in specific language
gh search code "def plan" --language python --limit 10

# Search code in specific repo
gh search code "planning" --repo microsoft/semantic-kernel --limit 20

# Search code by filename
gh search code "skills" --filename "*.md" --limit 10

# Search with JSON output
gh search code "agent skills" --json path,repository --limit 10

View Repository Details

# View repo README and description
gh repo view microsoft/semantic-kernel

# View repo with specific fields as JSON
gh repo view microsoft/semantic-kernel --json name,description,stargazerCount,defaultBranchRef

# Open repo in browser
gh repo view microsoft/semantic-kernel --web

Phase 4: Clone Repository to Cache

When you find a useful repository, clone it to the cache directory.

Clone Commands

# Create cache directory structure
mkdir -p .agent_cache/resources/{owner}

# Clone repository
gh repo clone microsoft/semantic-kernel .agent_cache/resources/microsoft/semantic-kernel

# Clone with shallow history (faster, uses less disk)
gh repo clone microsoft/semantic-kernel .agent_cache/resources/microsoft/semantic-kernel -- --depth 1

# Clone specific branch
gh repo clone microsoft/semantic-kernel .agent_cache/resources/microsoft/semantic-kernel -- --branch main --depth 1

Update Existing Clone

# Pull latest changes
cd .agent_cache/resources/microsoft/semantic-kernel && git pull && cd -

Phase 5: Explore Repository

Once cloned, use a combination of gh commands and file reading tools.

Repository Structure Exploration

# View directory structure
tree -L 3 .agent_cache/resources/microsoft/semantic-kernel/python/ | head -50

# Find README files
find .agent_cache/resources/microsoft/semantic-kernel -name "README.md" | head -10

# Find Python files related to agents
find .agent_cache/resources/microsoft/semantic-kernel -path "*agents*" -name "*.py" | head -20

Code Search in Local Clone

# Search for specific patterns
grep -r "class.*Agent" .agent_cache/resources/microsoft/semantic-kernel/python --include="*.py" | head -20

# Find function definitions
grep -rn "def plan" .agent_cache/resources/microsoft/semantic-kernel/python --include="*.py"

# Search for imports
grep -r "from.*import" .agent_cache/resources/microsoft/semantic-kernel/python --include="*.py" | grep planning

Read Specific Files

After identifying relevant files with grep/find, use the read_file tool to examine content:

# Example files to read
read_file .agent_cache/resources/microsoft/semantic-kernel/python/semantic_kernel/agents/orchestration/magentic.py

Useful gh Commands Reference

Repository Commands

CommandDescription
gh repo view <owner/repo>View repo README and details
gh repo view <owner/repo> --json <fields>Get repo metadata as JSON
gh repo clone <owner/repo> <path>Clone repo to specific path
gh repo list <owner>List repos owned by user/org

Search Commands

CommandDescription
gh search repos <query>Search for repositories
gh search code <query>Search within code
gh search issues <query>Search issues
gh search prs <query>Search pull requests

Search Filters

FilterExampleDescription
--language--language pythonFilter by programming language
--owner--owner microsoftFilter by repository owner
--stars--stars ">1000"Filter by star count
--limit--limit 20Limit number of results
--sort--sort starsSort results (stars, forks, updated)
--json--json fullName,descriptionOutput as JSON

JSON Fields for Repos

Common fields for --json output:

  • fullName - owner/repo
  • name - repo name only
  • description - repo description
  • stargazersCount - number of stars
  • language - primary language
  • url - GitHub URL
  • defaultBranchRef - main branch name

Example Workflows

Example 1: Find Reference Implementations for REST API Patterns

# 1. Check local cache
ls .agent_cache/resources/ 2>/dev/null || echo "Cache empty"

# 2. Search GitHub for Fabric/Power BI SDK examples
gh search repos "fabric rest api python" --stars ">50" --limit 10

# 3. View promising repo
gh repo view microsoft/semantic-link-labs

# 4. Clone to cache
mkdir -p .agent_cache/resources/microsoft
gh repo clone microsoft/semantic-link-labs .agent_cache/resources/microsoft/semantic-link-labs -- --depth 1

# 5. Explore API patterns
grep -r "_base_api" .agent_cache/resources/microsoft/semantic-link-labs --include="*.py" | head -20

Example 2: Search for Code Patterns

# Search for specific pattern across GitHub
gh search code "FabricRestClient" --language python --limit 5

# Clone repo containing the pattern
gh repo clone owner/repo .agent_cache/resources/owner/repo -- --depth 1

# Find and read the file
grep -rn "FabricRestClient" .agent_cache/resources/owner/repo --include="*.py"

Example 3: Explore Multiple Repos

# Search for agent frameworks
gh search repos "agent framework" --language python --stars ">2000" --json fullName,description,stargazersCount

# Clone top candidates
for repo in "langchain-ai/langchain" "Significant-Gravitas/AutoGPT"; do
    owner=$(dirname $repo)
    mkdir -p ".agent_cache/resources/$owner"
    gh repo clone "$repo" ".agent_cache/resources/$repo" -- --depth 1
done

# Compare implementations
diff <(grep -r "def plan" .agent_cache/resources/langchain-ai/langchain --include="*.py" | head -5) \
     <(grep -r "def plan" .agent_cache/resources/Significant-Gravitas/AutoGPT --include="*.py" | head -5)

Best Practices

  1. Always check local cache first -- Avoid redundant cloning
  2. Use shallow clones -- Add -- --depth 1 to save disk space and time
  3. Organize by owner/repo -- Prevents naming conflicts
  4. Use JSON output -- Easier to parse programmatically
  5. Start with grep/find -- Narrow down before reading full files
  6. Respect rate limits -- GitHub has API rate limits; cache results
  7. Clean up periodically -- Remove unused repos from cache

Troubleshooting

Authentication Issues

# Check current auth status
gh auth status

# Re-authenticate
gh auth login

# Use different auth method
gh auth login --with-token < token.txt

Rate Limiting

# Check rate limit status
gh api rate_limit

# If rate limited, wait or use cached repos

Clone Failures

# If clone fails, try with HTTPS explicitly
git clone https://github.com/owner/repo.git .agent_cache/resources/owner/repo

# Or try shallow clone
git clone --depth 1 https://github.com/owner/repo.git .agent_cache/resources/owner/repo