Nm Cartograph Code Communities
v1.0.0Detect architectural clusters in the codebase using community detection on the code knowledge graph. Shows module boundaries, cohesion, and coupling warnings
⭐ 0· 57·1 current·1 all-time
by@athola
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & 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.
Assessment
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.Like a lobster shell, security has layers — review code before you run it.
latestvk977e9wep9m9pt5q9hhbw6t58984n42y
License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🦞 Clawdis