360 Secure SkillHub
360 安全技能中心

code-communities

对代码库执行社区检测,按内聚度列出模块聚类、标记跨社区高耦合边并生成架构流程图,最后给出拆分模块或引入接口的改进建议。优先依赖 gauntlet 提供的代码图数据,缺失时回退到目录结构与导入关系分析。

athola
athola
热度 63.13
最近更新 2026-08-26 21:09:14
分类:开发依赖:代码执行 / 读写文件应用场景:代码审查 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add nm-cartograph-code-communities
Metadata
name
code-communities
description
Detects architectural clusters and coupling boundaries via community detection on the code graph
version
1.9.8
triggers
["identifying module groupings or refactoring targets"]
metadata
{"openclaw":{"homepage":"https://github.com/athola/claude-night-market/tree/master/plugins/cartograph","emoji":"🦞"}}
source
claude-night-market
source_plugin
cartograph

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

Code Community Detection

Identify architectural clusters and module boundaries in the codebase.

Prerequisites

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.

Steps

  1. 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|/[^/]*$||' | sort | uniq -c | sort -rn
    
    # Cross-directory imports (rg preferred, grep fallback)
    if command -v rg &>/dev/null; then
      rg "^from |^import " --type py -l . | \
        xargs -I{} rg "^from \w+ import|^import \w+" {} --no-filename
    else
      grep -rh "^from \|^import " --include="*.py" .
    fi | sort | uniq -c | sort -rn | head -20
    

    Group by top-level directories and count cross-directory imports to estimate coupling.

  2. 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
    
  3. 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)
    
  4. 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
    
  5. Suggest improvements:

    • Low cohesion (<0.5): "Consider splitting this module into more focused components"
    • High coupling (>20 edges): "Consider introducing an interface to reduce direct dependencies"

Algorithm

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