Librarian Skill

Deep codebase exploration across remote repositories.

How to Use This Skill

Reference Structure

Reading Order

1. Start with tool-routing.md → choose tool strategy
2. If using opensrc:
   • Read opensrc-api.md for API details
   • Read opensrc-examples.md for patterns
3. Before responding: linking.md + diagrams.md for output formatting

Tool Arsenal

Quick Decision Trees

"How does X work?"

Known library?
├─ Yes → context7.resolve-library-id → context7.query-docs
│        └─ Need internals? → opensrc.fetch → read source
└─ No  → grep_app search → opensrc.fetch top result

"Find pattern X"

Specific repo?
├─ Yes → opensrc.fetch → opensrc.grep → read matches
└─ No  → grep_app (broad) → opensrc.fetch interesting repos

"Explore repo structure"

1. opensrc.fetch(target)
2. opensrc.files("github.com/owner/repo", "**/*")
3. Read: README, package.json, src/index.*
4. Create architecture diagram (see diagrams.md)

"Compare X vs Y"

1. opensrc.fetch(["X", "Y"])
2. Use source.name from results for subsequent calls
3. opensrc.grep(pattern, { sources: [nameX, nameY] })
4. Read comparable files, synthesize differences

Critical: Source Naming Convention

After fetching, always use source.name for subsequent calls:

const [{ source }] = await opensrc.fetch("vercel/ai");
const files = await opensrc.files(source.name, "**/*.ts");

When NOT to Use opensrc

Output Guidelines

1. Comprehensive final message - only last message returns to main agent
2. Parallel tool calls - maximize efficiency
3. Link every file reference - see linking.md
4. Diagram complex relationships - see diagrams.md
5. Never mention tool names - say "I'll search" not "I'll use opensrc"

References

• Tool Routing Decision Trees
• opensrc API Reference
• opensrc Code Examples
• GitHub Linking Patterns
• Mermaid Diagram Patterns