| name | blast-radius |
| role | entrypoint |
| description | 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. |
| model_hint | standard |
| tags | ["code-review","impact-analysis","risk-scoring"] |
| tools | [] |
| dependencies | ["imbue:review-core","imbue:structured-output"] |
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=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)
If gauntlet is not installed (GRAPH_QUERY is empty):
Fall back to a manual impact analysis using git diff
and grep to trace imports and call sites. Skip graph
steps and go directly to step 3 (manual mode).
If gauntlet is installed but no graph.db exists:
Tell the user: "Run /gauntlet-graph build first."
Steps
-
Show current changes: Run git diff --stat to
show the user what files changed.
-
Run impact analysis (requires gauntlet):
python3 "$GRAPH_QUERY" \
--action impact --base-ref HEAD --depth 2
Fallback tier 1 (sem available, no gauntlet):
Use sem for cross-file dependency tracing:
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):
if command -v rg &>/dev/null; then
git diff --name-only HEAD | while read f; do
stem="${f%.*}"; stem="${stem##*/}"
[ -z "$stem" ] && continue
rg -l "$stem" . 2>/dev/null
done | sort -u
else
git diff --name-only HEAD | while read f; do
stem="${f%.*}"; stem="${stem##*/}"
[ -z "$stem" ] && continue
grep -rl "$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.
-
Display results in priority order:
Format the output as a table:
Risk | Node | File | Anchor | Reason
0.85 | auth.py::verify_token | auth.py:45 | `def verify_token(token):` | untested, security
0.62 | db.py::execute_query | db.py:112 | `cursor.execute(query, params)` | high fan-in
0.41 | api.py::handle_request | api.py:78 | `def handle_request(req):` | flow participant
The Anchor column is the verbatim source text at the cited line.
It lets a reviewer confirm the finding without re-running the tool.
-
Highlight untested functions: List any affected
functions that lack test coverage (no TESTED_BY edge).
-
Show overall risk: Display the overall risk level
(low/medium/high) based on the maximum risk score.
-
Suggest actions:
- 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"
Verify Findings Are Grounded (blast-radius:findings-verified)
Every finding must cite a real location and a verbatim anchor. Write
findings to .review/findings.json and confirm each citation resolves:
python plugins/imbue/scripts/citation_verifier.py \
--findings .review/findings.json --repo-root .
Drop or label UNVERIFIED any finding the verifier fails (exit 1); only
verified findings enter the report. See Skill(imbue:review-core) Step 5
and Skill(imbue:structured-output) for the schema.
Exit Criteria
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 |