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Install in OpenClaw
/install nm-cartograph-code-communities
Description
Detect architectural clusters in the codebase using community detection on the code knowledge graph. Shows module boundaries, cohesion, and coupling warnings
Usage Guidance
This skill is internally coherent and does what it says: detect communities using a graph tool or, if absent, by scanning directories and imports. Before running it, decide whether you trust the local 'gauntlet' plugin it may invoke — that will execute code on your machine. If you prefer safety, run the fallback file-scanning commands manually in a controlled environment or review the gauntlet plugin's graph_query.py source first. Also be aware the fallback will read repository files (which could include sensitive data) when computing coupling/cohesion.
Capability Assessment
Purpose & Capability
Name/description match the instructions: the skill runs a community-detection action via a graph-query tool when available and otherwise analyzes directory structure and imports. The declared requirements (none) are proportional to an instruction-only skill that leverages other local tooling.
Instruction Scope
Instructions are focused on code-graph community detection and provide a clear fallback that scans repo files and imports. One important note: the preferred path runs an external script found at ~/.claude/plugins/.../gauntlet/graph_query.py (python3 "$GRAPH_QUERY" --action communities). Invoking that script will execute code from the gauntlet plugin (not provided by this skill) so you should only run it if you trust that plugin. The fallback commands (find, rg/grep, sed, xargs) will traverse and read repository files, which is expected but may surface sensitive files if present.
Install Mechanism
There is no install spec and no downloads. This is instruction-only, so nothing gets written or installed by the skill itself.
Credentials
The skill declares no environment variables, credentials, or config paths. All referenced paths are local discovery of an optional plugin and repository file scanning, which are proportionate to its purpose.
Persistence & Privilege
always is false and the skill is user-invocable; it does not request persistent presence or modify agent/system settings. Autonomous invocation is allowed by platform default but not elevated by this skill.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install nm-cartograph-code-communities - After installation, invoke the skill by name or use
/nm-cartograph-code-communities - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial public release of code-communities skill.
- Detects architectural clusters and module boundaries in codebases using community detection algorithms.
- Provides module cohesion and coupling analysis, with warnings for high coupling.
- Supports both graph-based (with gauntlet plugin) and directory-based analysis (as fallback).
- Generates visualizations in Mermaid flowcharts.
- Offers improvement suggestions based on cohesion and coupling metrics.
Metadata
Frequently Asked Questions
What is Nm Cartograph Code Communities?
Detect architectural clusters in the codebase using community detection on the code knowledge graph. Shows module boundaries, cohesion, and coupling warnings. It is an AI Agent Skill for Claude Code / OpenClaw, with 94 downloads so far.
How do I install Nm Cartograph Code Communities?
Run "/install nm-cartograph-code-communities" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Nm Cartograph Code Communities free?
Yes, Nm Cartograph Code Communities is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Nm Cartograph Code Communities support?
Nm Cartograph Code Communities is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Nm Cartograph Code Communities?
It is built and maintained by athola (@athola); the current version is v1.0.0.
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