graph-export

name	graph-export
description	[Code Intelligence] Use when you need to export the code review knowledge graph. Flag: --format={json|mermaid} (default json); --format=json dumps the full graph to a JSON file, --format=mermaid renders a single file as a Mermaid flowchart diagram.
disable-model-invocation	false
version	1.0.0

Quick Summary

Goal: [Code Intelligence] Export the code review knowledge graph. --format=json (default) dumps all nodes (functions, classes, files) and edges (calls, imports, inheritance) from graph.db for external analysis; --format=mermaid renders a single file's internal call graph as a Mermaid flowchart in markdown (folds former /graph-export-mermaid).

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:

• Format flag (see Format Mode): --format=json (default) = full graph → .code-graph/graph-export.json (CLI export); --format=mermaid = single-file diagram, requires <path> (CLI export-mermaid).
• 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

Format Mode (--format=)

--format	CLI verb	Output	Requires
json (default)	code_graph export --json	Full graph dump → .code-graph/graph-export.json	—
mermaid	code_graph export-mermaid <path> --json	Single-file Mermaid diagram → .code-graph/<name>-graph.md	<path>

--format=json dumps ALL nodes + edges from the whole graph. --format=mermaid renders ONE file's internal call graph as a Mermaid flowchart (folds former /graph-export-mermaid). Pick --format FIRST (default json), then run the matching branch below.

Steps

--format=json (default) — full graph → JSON

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 {source-root}/auth {source-root}/api --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.

--format=mermaid — single file → Mermaid diagram

1. Export file graph as Mermaid — Run via Bash (positional or --file flag both work):

   python .claude/scripts/code_graph export-mermaid <relative-path> --json
   # OR
   python .claude/scripts/code_graph export-mermaid --file <relative-path> --json
   

   Default output: .code-graph/<path-based-unique-name>-graph.md (e.g., docs--project-config-graph.md)

2. Custom output path (optional):

   python .claude/scripts/code_graph export-mermaid <relative-path> -o custom-path.md --json
   

3. Report results: File path, node count, edge count.

Mermaid scope: functions/classes/test functions within the file + internal call relationships (caller AND callee in-file); class membership via nested subgraphs; edge types calls/imports/inherits/implements/tests/depends. Excludes external/stdlib calls, cross-file callers, and CONTAINS edges (shown structurally). Implicit MESSAGE_BUS / TRIGGERS_EVENT / API_ENDPOINT edges render when present.

Output Format

--format=json:

{
  "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": 
               
  

--format=mermaid: a markdown file containing a flowchart TD — functions/classes as nodes, calls/imports as labelled edges, class membership as nested subgraphs (e.g., login -->|calls| validate).

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

---

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:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

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 + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

Closing Reminders

IMPORTANT MUST ATTENTION Goal: Export the code review knowledge graph — --format=json dumps the full graph to JSON, --format=mermaid renders one file as a Mermaid flowchart.

IMPORTANT MUST ATTENTION — Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.

• Critical Thinking: traced file:line proof per claim, confidence >80% to act.

• 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.