What are the five discovery layers mentioned?

The layers are: CLAUDE.md (Quick Reference), capabilities.json (Structure), Skills Index (Workflows), Weaviate (Semantic Search), and Source Code (Implementation Details).

What is the primary source of truth for CodeCompass capabilities?

The .ai/capabilities.json file serves as the definitive map, detailing all domains, modules, and entry points within the platform.

When should I use the discover-capabilities skill?

Apply this skill at the start of a session, when a user asks 'What can CodeCompass do?', or before implementing new features to check for existing logic.

How does this skill improve token efficiency?

Instead of performing broad grep/glob searches across the codebase, it targets structured discovery layers, significantly reducing exploratory token consumption.

CodeCompass Capability Discovery

Name: CodeCompass Capability Discovery
Author: PearlThoughts

byPearlThoughts

Productividad y Flujo de Trabajo

Explores and maps the capabilities, modules, and architectural domains of the CodeCompass platform using structured metadata.

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This meta-skill provides a structured framework for navigating the expansive CodeCompass ecosystem. By leveraging a multi-layered discovery approach—ranging from high-level summaries in CLAUDE.md to granular module maps in capabilities.json—it eliminates inefficient manual exploration. It is essential for initial session onboarding, planning complex feature implementations, and identifying pre-existing modules across five distinct domains, including code analysis, semantic search, and requirements extraction.

Características Principales

Multi-layered discovery spanning documentation, JSON maps, and semantic search
0 GitHub stars
Structured mapping of 5 architectural domains and 21+ modules
Dependency and relationship tracing between internal modules
Automated parsing of capabilities.json for instant system awareness
Decision tree logic to determine the most efficient discovery path

Casos de Uso

Pre-implementation research to identify and reuse existing modules
Initial session onboarding to understand the available toolset and environment
Complex workflow planning by mapping dependencies and entry points

Acerca de

Características Principales

Multi-layered discovery spanning documentation, JSON maps, and semantic search
0 GitHub stars
Structured mapping of 5 architectural domains and 21+ modules
Dependency and relationship tracing between internal modules
Automated parsing of capabilities.json for instant system awareness
Decision tree logic to determine the most efficient discovery path

Casos de Uso

Pre-implementation research to identify and reuse existing modules
Initial session onboarding to understand the available toolset and environment
Complex workflow planning by mapping dependencies and entry points