librarian

name	librarian
description	Multi-repository codebase exploration. Research library internals, find code patterns, understand architecture, compare implementations across GitHub/npm/PyPI/crates. Use when needing deep understanding of how libraries work, finding implementations across open source, or exploring remote repository structure.
references	["references/tool-routing.md","references/opensrc-api.md","references/opensrc-examples.md","references/linking.md","references/diagrams.md"]

Librarian Skill

Deep codebase exploration across remote repositories.

How to Use This Skill

Reference Structure

File	Purpose	When to Read
tool-routing.md	Tool selection decision trees	Always read first
opensrc-api.md	API reference, types	Writing opensrc code
opensrc-examples.md	JavaScript patterns, workflows	Implementation examples
linking.md	GitHub URL patterns	Formatting responses
diagrams.md	Mermaid patterns	Visualizing architecture

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

Tool	Best For	Limitations
grep_app	Find patterns across ALL public GitHub	Literal search only
context7	Library docs, API examples, usage	Known libraries only
opensrc	Fetch full source for deep exploration	Must fetch before read

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.tree(source.name) → quick overview
3. opensrc.files(source.name, "**/*.ts") → detailed listing
4. Read: README, package.json, src/index.*
5. 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");

Type	Fetch Spec	Source Name
npm	"zod"	"zod"
npm scoped	"@tanstack/react-query"	"@tanstack/react-query"
pypi	"pypi:requests"	"requests"
crates	"crates:serde"	"serde"
GitHub	"vercel/ai"	"github.com/vercel/ai"
GitLab	"gitlab:org/repo"	"gitlab.com/org/repo"

When NOT to Use opensrc

Scenario	Use Instead
Simple library API questions	context7
Finding examples across many repos	grep_app
Very large monorepos (>10GB)	Clone locally
Private repositories	Direct access

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