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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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Blast Radius

Analyze the structural impact of current code changes using the knowledge graph.

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. Check graph exists — Verify

   .code-graph/graph.db

   exists. If not, suggest

   /graph-build

   .

2. Run blast-radius analysis via Bash:

   python .claude/scripts/code_graph blast-radius --json
   

3. Parse JSON output and present:

   • Changed files: List of modified files (auto-detected from git)
   • Changed nodes: Functions/classes directly modified
   • Impacted nodes: Functions/classes affected within 2 hops (callers, dependents, tests)
   • Impacted files: Additional files that may need attention
   • Truncation: If results were truncated, note total vs shown

4. Risk assessment based on blast radius size:

   • Low risk: <5 impacted nodes, changes well-contained
   • Medium risk: 5-20 impacted nodes, review callers carefully
   • High risk: >20 impacted nodes, consider splitting PR

5. Recommendations:

   • Flag untested changed functions
   • Suggest files to prioritize in review
   • Warn about inheritance/implementation relationship changes

Trace for Deep Impact Analysis

For impact beyond direct callers/importers, use the

trace

command to follow the full chain through implicit connections:

python .claude/scripts/code_graph trace <changed-file> --direction downstream --depth 3 --json

# File-level overview first (10-30x less noise), then drill into functions:
python .claude/scripts/code_graph trace <changed-file> --direction downstream --node-mode file --json

This reveals downstream impact through MESSAGE_BUS edges (cross-service event consumers), TRIGGERS_EVENT (entity event handlers), and other implicit relationships that blast-radius may not surface directly.

Additional Queries

For deeper investigation, run via Bash:

• python ... query callers_of <function> --json

  — who calls this function?

• python ... query tests_for <function> --json

  — what tests cover this?

• python ... query inheritors_of <class> --json

  — what inherits from this?

• python ... query importers_of <file> --json

  — who imports this file?

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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 -->