Dark Matter Analyzer

Dark Matter Analyzer

Purpose

Dark Matter Mode reveals what is unseen, unsaid, and unmeasured in repositories and codebases. It goes beyond code quality metrics to illuminate the invisible architectures shaping system behavior — identifying strategic drift, documentation inflation, execution gaps, and organizational health patterns that traditional tools miss.

"Every repo is a psyche made visible."

This skill helps diagnose why a repository feels off, not just what is wrong technically.

When to Use This Skill

• Repository feels misaligned but traditional metrics look fine
• Documentation is extensive but team still feels confused
• Stated goals don't match actual work being done
• Need to understand organizational patterns in codebase structure
• Project velocity is high but coherence feels low
• Planning significantly outpaces execution
• Multiple overlapping documents on same topics
• Need to assess "Repository Coherence Index" (RCI)
• Preparing for major refactor or reorganization
• When NOT to use: Simple bug fixes, feature additions, or standard code reviews

Core Methodology

Step 1: Sensing — Signal Ingest

Objective: Capture ambient signals and metadata from the repository

Actions:

1. Scan code signals: commit patterns, refactor frequency, lint suppressions, TODO/FIXME markers
2. Scan documentation signals: README drift, redundant .md files, doc count vs code ratio
3. Scan temporal signals: time lag between decision and execution, feature velocity
4. Scan environmental signals: dependency health, build status, test coverage trends

Key Decisions:

• Scope: Full repository or specific subsystem?
• Depth: Quick scan (3 levels) or deep analysis (all files)?
• Exclusions: What to skip (node_modules, dist, vendor)?

Common Pitfalls:

• ❌ Looking only at code metrics → ✅ Include documentation and temporal patterns
• ❌ Judging patterns as "errors" → ✅ View them as system expressions
• ❌ Analyzing in isolation → ✅ Compare stated intent with observed behavior