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blast-radius

Analyzes code change impact with risk scoring and affected-node mapping. Use before merging to understand what a change touches and what lacks test coverage.

314estrelas
Atualizado há 4 meses

Ver no GitHub ↗ Licença: MIT

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Blast Radius Analysis

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

Prerequisites

This skill requires the gauntlet plugin for graph data. Check if it's available:

GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)

If gauntlet is not installed (GRAPH_QUERY is empty): Fall back to a manual impact analysis using git diff and grep to trace imports and call sites. Skip graph steps and go directly to step 3 (manual mode).

If gauntlet is installed but no graph.db exists: Tell the user: "Run /gauntlet-graph build first."

Steps

  1. Show current changes: Run git diff --stat to

show the user what files changed.

  1. Run impact analysis (requires gauntlet):

``bash python3 "$GRAPH_QUERY" \ --action impact --base-ref HEAD --depth 2 ``

Fallback tier 1 (sem available, no gauntlet): Use sem for cross-file dependency tracing: ``bash if command -v sem &>/dev/null; then sem impact --json <changed-file> fi ``

This traces real function-level dependencies instead of filename matching. See leyline:sem-integration for detection patterns.

Fallback tier 2 (no sem, no gauntlet): Trace callers of changed functions with rg (or grep): ``bash # Prefer rg for speed; fall back to grep if command -v rg &>/dev/null; then git diff --name-only HEAD | while read f; do stem="${f%.}"; stem="${stem##/}" [ -z "$stem" ] && continue # skip dotfiles (.gitignore etc.) rg -l "$stem" . 2>/dev/null done | sort -u else git diff --name-only HEAD | while read f; do stem="${f%.}"; stem="${stem##/}" [ -z "$stem" ] && continue # skip dotfiles (.gitignore etc.) grep -rl "$stem" . 2>/dev/null done | sort -u fi ``

Note: this searches all file types. For Python-only projects, add --type py to rg or --include="*.py" to grep to reduce false positives.

  1. Display results in priority order:

Format the output as a table: `` Risk | Node | File | Reason 0.85 | auth.py::verify_token | auth.py:45 | untested, security 0.62 | db.py::execute_query | db.py:112 | high fan-in 0.41 | api.py::handle_request | api.py:78 | flow participant ``

  1. Highlight untested functions: List any affected

functions that lack test coverage (no TESTED_BY edge).

  1. Show overall risk: Display the overall risk level

(low/medium/high) based on the maximum risk score.

  1. Suggest actions:
  2. For high-risk nodes: "Consider adding tests before

merging"

  • For security-sensitive nodes: "Review authentication

and authorization logic carefully"

  • For high-fan-in nodes: "Changes here affect many

callers; verify backward compatibility"

Risk Scoring Model

Five weighted factors (sum capped at 1.0):

| Factor | Weight | Meaning | |--------|--------|---------| | Test gap | 0.30 | No test coverage | | Security | 0.20 | Auth/crypto/SQL keywords | | Flow participation | 0.25 | Part of execution flows | | Cross-community | 0.15 | Called from other modules | | Caller count | 0.10 | High fan-in function |

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