Query React internals, trace Django middleware, explore FastAPI routing — without cloning a single repo. Pre-built jCodeMunch indexes compress gigabytes of framework source into megabytes of searchable symbols.
Starter Packs aren't a shortcut. They give your AI agent a capability it didn't have before: deep, symbol-level understanding of frameworks you've never cloned.
Curious how React's fiber reconciler works? Install the pack and ask. Your agent searches 24,000 symbols and retrieves the exact function — no git clone, no 300 MB download, no indexing wait.
Got a confusing Django ORM error? Search the Django pack for that symbol, read the implementation, understand the call path. Faster than searching GitHub, cheaper than reading the source file.
Install the FastAPI pack and index your app. Now find_importers, get_blast_radius, and get_dependency_graph trace across the boundary — from your code into the framework.
No API key. No repo to point at. No config file. Install jCodeMunch, download the free Node.js pack, and run your first query in under 60 seconds. The lowest-friction way to experience symbol-level retrieval.
Everyone on the team gets the same curated index, built from the latest tagged release. No "works on my machine" variance from different clone states, branch checkouts, or index versions.
Pre-built packs mean a container or CI job can have framework context without an indexing step. Zero cold-start penalty. One install-pack command in your Dockerfile.
Your AI agent needs to understand how Express middleware chaining works. Here's what happens with and without a Starter Pack.
Agent reads router/index.js … 4,200 tokens Agent reads middleware/init.js … 2,800 tokens Agent reads application.js … 8,100 tokens Agent scans 3 more files … 6,400 tokens
Requires cloning expressjs/express locally and indexing it first — or reading raw files.
search_symbols("middleware chain")
→ 3 results … 180 tokens
get_symbol_source("Router.use")
→ function body + signature … 310 tokens
No clone. No indexing. Pack was already installed.
Every exported function, class, constant, and type — pre-indexed and searchable. A 932 MB React repo becomes a 3 MB pack. A 1.4 GB Node.js monorepo becomes 10.6 MB. Source is fetched from GitHub on first retrieval, so the download is just metadata.
If you haven't already: uvx jcodemunch-mcp — works with Claude Code, Cursor, Antigravity, and any MCP client.
jcodemunch-mcp install-pack nodejs — downloads the pre-built index to your local index directory. The Node.js pack (76,700+ symbols, 10.6 MB) takes seconds.
The index is live. Ask your AI agent "how does Express middleware chaining work?" and it searches 76,700 symbols, retrieves the exact function, and answers — in ~490 tokens instead of 21,500.
Packs contain symbol indexes — names, locations, signatures, relationships. Source code is fetched from GitHub on first retrieval. That's why a 76K-symbol pack is only 10.6 MB.
Indexes are rebuilt every week from the latest tagged release of each framework. You always get stable, documented API surfaces — not arbitrary commit snapshots.
Starter Pack indexes are identical to what index_folder and index_repo produce. Every jCodeMunch tool works on them: search, blast radius, call hierarchy, ranked context.
Use --force to re-download and overwrite an existing pack. Use --list to see the full catalog with sizes and symbol counts from your terminal.
One command installs jCodeMunch. One command downloads the pack. Then ask your AI agent anything about Express, Fastify, Koa, or Node.js core. No API key, no config, no clone.