blast-radius

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.

2. 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 
   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):

   # 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.

3. 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
   

4. Highlight untested functions: List any affected functions that lack test coverage (no TESTED_BY edge).

5. Show overall risk: Display the overall risk level (low/medium/high) based on the maximum risk score.

6. 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):