<!-- SYNC:critical-thinking-mindset -->

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.

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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.

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Export Graph

Export the complete knowledge graph from

.code-graph/graph.db

to a readable JSON file.

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

---

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 <!-- SYNC:critical-thinking-mindset:reminder -->
• MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact. <!-- /SYNC:critical-thinking-mindset:reminder --> <!-- SYNC:ai-mistake-prevention:reminder -->
• MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction. <!-- /SYNC:ai-mistake-prevention:reminder -->