Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Traces execution paths through the code graph with criticality scoring and Mermaid charts. Use when understanding how a function propagates through the system.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Trace execution flows through the codebase using the code knowledge graph.
cartograph:dependency-graph)pensive:blast-radius)This skill requires the gauntlet plugin for graph data. Discover it:
GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)
If gauntlet is not installed: Fall back to static
analysis. Use grep to trace function calls and build
a Mermaid diagram manually from import/call patterns.
Skip graph-specific steps.
If installed but no graph.db: Tell the user to run
/gauntlet-graph build.
Accept target: Get a function name or entry point from the user (or trace all entry points).
Run flow tracing (requires gauntlet):
python3 "$GRAPH_QUERY" --action flows --depth 15
To filter by entry point:
python3 "$GRAPH_QUERY" --action flows --entry "main"
Fallback (no gauntlet): Trace calls with rg (or grep):
# Prefer rg (ripgrep) for speed; fall back to grep
if command -v rg &>/dev/null; then
rg -n "function_name\(" --type py . | head -20
else
grep -rn "function_name(" --include="*.py" . | head -20
fi
Build the call tree manually from search results.
Display as indented tree:
main() [criticality: 0.72]
-> validate_input()
-> parse_config()
-> process_data()
-> db.execute_query()
-> cache.store()
-> send_response()
Generate Mermaid flowchart:
flowchart LR
main --> validate_input
main --> process_data
main --> send_response
validate_input --> parse_config
process_data --> db.execute_query
process_data --> cache.store
Show criticality breakdown:
| Factor | Weight | Meaning |
|---|---|---|
| File spread | 0.30 | Touches many files |
| Security | 0.25 | Contains auth/crypto code |
| External calls | 0.20 | Unresolved dependencies |
| Test gap | 0.15 | Untested nodes in flow |
| Depth | 0.10 | Deep call chains |
[criticality: N.NN]flowchart LR generated with edges representing
each caller-to-callee relationship in the traced pathrg/grep
analysis is used and the absence of graph data is notedgraph.db is absent, user is
told to run /gauntlet-graph build before the skill halts아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.