index

Index codebases for semantic search via REST API endpoints.

Updated Jan 15, 2026

Search Tags:#codebase #semantic search #CLI #vector embeddings #index #BrAIny

Ready to run

npx skills add https://github.com/whiteboardev/brainy.chat --skill index

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: index
Source: https://github.com/whiteboardev/brainy.chat/tree/main/.opencode/skills/index
Command: npx skills add https://github.com/whiteboardev/brainy.chat --skill index

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill indexes codebases to enable semantic, vector-based search over source code, letting developers pose natural-language questions and locate relevant files or functions quickly.

Core Features & Use Cases

Semantic indexing of codebases to support vector-based search across languages and projects.
CLI actions to index, reindex, search, list, and delete codebases for project management.
Use Case: A developer wants to find where authentication logic resides or where a database connection is implemented within a project.

Quick Start

Index a codebase: bun scripts/index.ts index /path/to/project --name MyProject
Search indexed code: bun scripts/index.ts search "authentication" --limit 10
Reindex after changes: bun scripts/index.ts reindex
List indexed codebases: bun scripts/index.ts list

Frequently Asked Questions about index

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I make my codebase searchable with natural language queries?▼

Semantic indexing applies vector embeddings to your source code, enabling natural-language queries over code. It allows developers to locate relevant files or functions based on conceptual meaning rather than exact string matches.

How do I index a codebase from the command line?▼

You can index a codebase from the command line by running the index script with your project path and name. To query indexed code, use the search command, and run the reindex command after making codebase changes.

Does semantic code search work with projects of any programming language?▼

Semantic code search applies to projects of any programming language and size. The semantic indexing process generates vector embeddings that support natural-language queries across different codebases without requiring language-specific configurations.

Do I need a specific server environment to index code for vector search?▼

You need to configure a BrAIny server by setting the BRAINY_URL and BRAINY_PROJECT_ID environment variables. The skill uses REST API endpoints to communicate with this server to index, reindex, and search codebases effectively.

What is the best way to find where specific logic is implemented in a large project?▼

The best way to find specific logic is to use semantic search over an indexed codebase. By querying terms like authentication or database connection, the vector-based search quickly navigates you to the relevant files or functions.