Does this support multi-language repositories?

Yes, the specialized agents analyze all file types, configurations, and documentation found in the repository regardless of the programming language used.

What happens during the Q&A state?

The AI identifies gaps in its understanding that cannot be resolved by code analysis alone and asks the user specific, prioritized questions to fill those knowledge gaps.

Do I need to run any other commands before aid-discover?

Yes, you must run /aid-init first to scaffold the Knowledge Base directory and initialize the discovery state.

How does the quality gate grading work?

The skill reviews generated documentation against a minimum acceptable grade (default 'A'). If the content is insufficient, the skill enters 'FIX' mode to improve the documents.

What is a 'brownfield' project in the context of this skill?

A brownfield project refers to an existing codebase that is already under development or in production, which typically requires discovery to understand its structure and logic.

AID Project Discover

Name: AID Project Discover
Author: AndreVianna

byAndreVianna

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Aprendizaje y Documentación

Conducts automated, high-fidelity discovery of existing codebases to build a structured knowledge base and documentation set.

aid-discover is a sophisticated state-machine driven skill designed for brownfield projects—existing codebases that require deep, structured understanding. It orchestrates five specialized subagents—Scout, Architect, Analyst, Integrator, and Quality—to analyze repository files, configurations, and documentation. Beyond simple scanning, it implements a rigorous quality gate that grades documentation from A-F and utilizes an interactive Q&A loop to capture human context, ensuring the resulting .aid/knowledge/ directory provides an accurate, production-grade foundation for AI-Integrated Development.

Características Principales

01Generates 16 structured knowledge documents covering architecture, API contracts, and tech debt

023 GitHub stars

03Includes a built-in quality gate with customizable A-F grading thresholds

04Orchestrates 5 specialized subagents for comprehensive repository analysis

05Implements a state-machine workflow (Generate, Review, Q&A, Fix, Approval)

06Facilitates an interactive Q&A loop to resolve ambiguities and collect domain knowledge

Casos de Uso

01Rapidly onboarding onto a complex legacy codebase with missing or outdated documentation

02Preparing an existing repository for high-context AI-assisted refactoring or feature development

03Performing technical audits of architecture, security models, and cross-cutting concerns

Características Principales

01Generates 16 structured knowledge documents covering architecture, API contracts, and tech debt

023 GitHub stars

03Includes a built-in quality gate with customizable A-F grading thresholds

04Orchestrates 5 specialized subagents for comprehensive repository analysis

05Implements a state-machine workflow (Generate, Review, Q&A, Fix, Approval)

06Facilitates an interactive Q&A loop to resolve ambiguities and collect domain knowledge

Casos de Uso

01Rapidly onboarding onto a complex legacy codebase with missing or outdated documentation

02Preparing an existing repository for high-context AI-assisted refactoring or feature development

03Performing technical audits of architecture, security models, and cross-cutting concerns