> Night Market Skill — ported from claude-night-market/pensive. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

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

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:
• 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):