graph-export

name	graph-export
description	[Code Intelligence] Use when you need export the code review knowledge graph to a JSON file.
disable-model-invocation	true
version	1.0.0

Quick Summary

Goal: [Code Intelligence] Export the code review knowledge graph to a JSON file. Dumps all nodes (functions, classes, files) and edges (calls, imports, inheritance) from graph.db for external analysis or inspection.

Workflow:

1. Detect — classify request scope and target artifacts.
2. Execute — apply required steps with evidence-backed actions.
3. Verify — confirm constraints, output quality, and completion evidence.

Key Rules:

• MUST ATTENTION keep claims evidence-based (file:line) with confidence >80% to act.
• MUST ATTENTION keep task tracking updated as each step starts/completes.
• NEVER skip mandatory workflow or skill gates.

Prerequisites

• Graph must be built first: run /graph-build if .code-graph/graph.db doesn't exist
• Requires Python 3.10+ with tree-sitter, tree-sitter-language-pack, networkx

Steps

1. Export graph — Run via Bash:

   python .claude/scripts/code_graph export --json
   

Default output: .code-graph/graph-export.json

2. Export specific files only (optional):

   python .claude/scripts/code_graph export --files src/auth.py src/api.py --json
   

This exports only nodes and edges belonging to the specified files.

3. Custom output path (optional):

   python .claude/scripts/code_graph export -o custom-path.json --json
   

4. Report results: File path, node count, edge count, file size.

Output Format

{
  "version": "1.8.4-easyclaude",
  "stats": { "total_nodes": N, "total_edges": N, "files_count": N, "languages": [...] },
  "nodes": [
    { "kind": "Function", "name": "login", "qualified_name": "auth.py::login", "file_path": "...", "line_start": 10, "line_end": 25, "language": "python" }
  ],
  "edges": [
    { "kind": "CALLS", "source": "api.py::handler", "target": "auth.py::login", "file_path": "...", "line": 15 }
  ]
}

Implicit Edges in Export

The exported JSON includes ALL edge types, including implicit connections:

• MESSAGE_BUS — cross-service bus message producer-to-consumer links
• TRIGGERS_EVENT — entity CRUD to event handler links
• PRODUCES_EVENT — event handler to bus producer links
• TRIGGERS_COMMAND_EVENT — command to command event handler links
• API_ENDPOINT — frontend HTTP call to backend route links

These edges are created by the implicit connector and API connector during build/sync.

Use Cases

• Inspect graph contents for debugging
• Feed into external analysis tools
• Verify graph correctness after build
• Share graph snapshot with team members

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Export Graph

Export the complete knowledge graph from .code-graph/graph.db to a readable JSON file.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.