Knowledge Graph from Code is a data workflow that chains Neo4j + GitHub to automate a common task. Parse your GitHub repos and build a Neo4j knowledge graph of files, functions, imports, and authors for code intelligence. Once configured, it saves ~15 hours/week on large refactors, plus de-risking of platform-wide changes and runs through Claude Code, Cursor, Windsurf or any MCP-compatible AI agent.
Parse your GitHub repos and build a Neo4j knowledge graph of files, functions, imports, and authors for code intelligence.
Hosted execution needs every MCP on the whitelist. Use the local CLI for this recipe until the missing MCPs are added.
Not yet hostable:
mcpizy recipe install neo4j-github-knowledge-graphGitHub holds your code history and authorship; Neo4j's graph model expresses code relationships (imports, calls, ownership) in a way a relational database cannot. Together they unlock queries that answer 'who understands this module' or 'what breaks if I change this function' in seconds.
Manually trace import chains across files, ask around to find who owns a module, struggle to understand why a change broke something unrelated.
Query the knowledge graph. See every caller of a function, every module it depends on, and who last touched each node — in one Cypher query.
Concrete ROI — not marketing fluff.
Time saved
~15 hours/week on large refactors, plus de-risking of platform-wide changes
This prompt is the workflow. Paste into Claude Code, Cursor, or Windsurf.
You are a code-knowledge-graph agent. Runs weekly or on main-branch push.
Given a GitHub repo:
1. Call github.clone_or_pull(repo, path="./workspace") to fetch latest
2. Parse AST for all source files using language-appropriate parser (ts-morph, jedi, etc.)
3. For each file extract: File node, Function nodes, Class nodes, Import edges
4. Call github.git_blame for each function definition line to get author + last_modified
5. Batch-write to Neo4j via neo4j.execute_cypher with MERGE clauses:
MERGE (f:File {path:$path}) MERGE (fn:Function {name:$name, file:$path}) MERGE (f)-[:CONTAINS]->(fn)
MERGE (a:Author {email:$email}) MERGE (a)-[:LAST_TOUCHED]->(fn)
6. After ingest, run staple queries: cycle detection, hotspots (top 20 most-imported files), orphans, owner coverage
7. Post summary to GitHub Wiki or repo README badge
Idempotent — MERGE makes re-runs safe. Report node + edge counts.How this workflow fires and what env vars you need.
0 2 * * 1 # every Monday at 02:00 UTC (or on push to main)
NEO4J_URINeo4j Bolt URI
e.g. bolt://localhost:7687
NEO4J_USERNAMENeo4j username
e.g. neo4j
NEO4J_PASSWORDNeo4j password
e.g. change-me
GITHUB_REPOTarget repo to graph (owner/name)
e.g. acme/monorepo
Install everything — MCPs, prompt, env template — in a single call.
$ mcpizy recipe install neo4j-github-knowledge-graph ✓ Installs all 2 MCP servers ✓ Writes prompt to ~/.mcpizy/prompts/neo4j-github-knowledge-graph.md ✓ Generates .env.example in current directory ✓ Ready to paste into Claude Code
Requires mcpizy CLI v1.1+ — install via npm i -g mcpizy.
$ mcpizy install neo4j && mcpizy install githubSchedule a Firecrawl scrape of any website and store the structured results directly in a Supabase table for analysis.
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Knowledge Graph from Code is a data automation that uses Neo4j + GitHub together via the Model Context Protocol. Parse your GitHub repos and build a Neo4j knowledge graph of files, functions, imports, and authors for code intelligence.
Setup takes around 45 min setup, instant code intelligence queries. You install the required MCP servers with `mcpizy install neo4j && mcpizy install github`, connect your accounts, and the workflow is ready to run.
Once running, this workflow saves ~15 hours/week on large refactors, plus de-risking of platform-wide changes. The concrete business value: De-risks major refactors: answers 'what breaks if I change X?' in a query, not 2 days of archaeology; Keeps domain knowledge alive after senior engineers leave — the graph survives the headcount.
You need 2 MCP servers: Neo4j (mcpizy install neo4j), GitHub (mcpizy install github). All are installable in one command via the MCPizy CLI and configured in your `.claude.json` or `.cursor/mcp.json`.
Yes. The workflow runs with any MCP-compatible AI agent — Claude Code, Claude Desktop, Cursor, Windsurf, VS Code with Copilot, and custom agents built on the MCP SDK. The MCP servers are identical across clients; only the config file path (`.claude.json` vs `.cursor/mcp.json`) changes.
Install the required MCPs from the marketplace and automate this in 45 min setup.
$ mcpizy install neo4j && mcpizy install github
Free to install. Connect your accounts and this workflow runs itself.