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Detects architectural clusters and coupling boundaries via community detection on the code graph. Use when identifying module groupings or refactoring targets.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/athola/claude-night-market --skill code-communities명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules. Use when vetting ideas. Not for QA; use night-market-validation-and-qa.
Generates and self-executes a diff-derived test plan for a PR. Use when validating PR changes before merge. Do not use for code review; use sanctum:pr-review.
Curate the web-capture index. Use when the capture backlog grows, captures sit unprocessed at seedling/pending, or to surface stored research during work.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | code-communities |
| description | Detects architectural clusters and coupling boundaries via community detection on the code graph. Use when identifying module groupings or refactoring targets. |
Identify architectural clusters and module boundaries in the codebase.
cartograph:dependency-graph)cartograph:architecture-diagram)This skill requires the gauntlet plugin for graph data. Discover it:
GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)
If gauntlet is not installed: Fall back to directory structure analysis. Group files by directory and use import statements to identify module boundaries. Generate a Mermaid diagram from directory-level relationships.
If installed but no graph.db: Tell the user to run
/gauntlet-graph build.
Run community detection (requires gauntlet):
python3 "$GRAPH_QUERY" --action communities
Fallback (no gauntlet): Analyze directory structure and cross-directory imports:
# Directory-level grouping
find . -name "*.py" -not -path "*/node_modules/*" | \
sed 's|/[^/]*$||' | | -c | -rn
-v rg &>/dev/null;
rg -- py -l . | \
xargs -I{} rg {} --no-filename
grep -rh --include= .
| | -c | -rn | -20
Group by top-level directories and count cross-directory imports to estimate coupling.
Display clusters:
Community | Nodes | Cohesion | Description
auth | 12 | 0.85 | Authentication module
db | 8 | 0.92 | Database access layer
api/handlers | 15 | 0.71 | API request handlers
utils | 6 | 0.45 | Shared utilities
Show coupling warnings: If communities have
10 cross-boundary edges, highlight them:
WARNING: High coupling between 'auth' and 'api/handlers'
(23 cross-community edges, severity: high)
Generate Mermaid diagram:
flowchart TB
subgraph auth[Auth Module - cohesion 0.85]
verify_token
check_permissions
end
subgraph db[DB Layer - cohesion 0.92]
execute_query
connection_pool
end
auth -->|"23 edges"| api
db -->|"5 edges"| api
Suggest improvements:
Uses the Leiden algorithm (when igraph is available) with edge-type-specific weights. Falls back to file-based grouping otherwise.
| Edge Type | Weight |
|---|---|
| CALLS | 1.0 |
| INHERITS | 0.8 |
| IMPLEMENTS | 0.7 |
| IMPORTS_FROM | 0.5 |
| TESTED_BY | 0.4 |
| CONTAINS | 0.3 |
flowchart TB generated with one subgraph per community
showing cohesion score in the subgraph label