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

Run the CLI Live (never expect pre-injected blast-radius)

This skill is the on-invoke home for blast-radius analysis. There is no auto-injected, pre-computed blast-radius context — you MUST ATTENTION run the CLI yourself to get LIVE impact data for the current working tree. Frozen/stale numbers are wrong by definition once the diff changes:

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

Post-Grep Trace Trigger (run a trace after grep surfaces a key file)

When a grep/glob during this analysis surfaces an important entry-point file — an entity, command, query, event/command handler, controller, bus message/consumer, component, store, or api-service — immediately run a graph trace on it before concluding. Grep finds files; the trace reveals callers, consumers, bus messages, event chains, and tests that grep CANNOT find:

python .claude/scripts/code_graph trace <key-entry-file> --direction both --json

Pattern: grep finds files → graph trace reveals full system flow → grep verifies specific details.

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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Blast Radius

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

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

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

• 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, NEVER guess as fact.

• 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.