graph-blast-radius

name	graph-blast-radius
description	[Code Intelligence] Use when you need to analyze the blast radius of current code changes using the structural knowledge graph.
version	1.0.0

Quick Summary

Goal: [Code Intelligence] Analyze the blast radius of current code changes using the structural knowledge graph. Shows impacted files, functions, test coverage gaps, and risk level. Requires graph to be built first via /graph-build.

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. 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?

---

Blast Radius

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

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.