What makes codebase-onboard different from standard search?

Unlike text-based grep, it uses semantic embeddings to understand the intent of your query, finding relevant code structures even if exact keywords aren't present.

How do I trigger the onboarding workflow?

You can start by asking Claude phrases like 'help me understand this codebase', 'how is this structured', or 'onboard me' to begin the indexing process.

When should I use this skill instead of semantic-explore?

Use codebase-onboard for high-level discovery of a project's structure and conventions; use semantic-explore for answering specific, granular technical questions.

Is my code shared with third parties during search?

No, the embedding and vector search functions are self-hosted via Docker, ensuring your codebase remains private and secure on your own infrastructure.

Do I need a GPU to use this skill?

While the plugin is optimized for GPU acceleration to speed up indexing, it runs via Docker and can be accessed locally or remotely over Tailscale.

Semantic Codebase Onboarding

Name: Semantic Codebase Onboarding
Author: adilasif

byadilasif

0•

Learning & Documentation

Accelerates the process of understanding unfamiliar codebases through GPU-powered semantic search and architectural discovery.

This skill leverages semantic search to help developers quickly grasp the structure, patterns, and architecture of new or complex repositories. By utilizing self-hosted embeddings and reranking via Docker, it enables users to perform natural language queries to identify entry points, data models, and system initialization logic without manual grepping. It is particularly effective for developers joining new teams or those exploring legacy projects, providing a systematic workflow for mapping out dependencies, error handling conventions, and core architectural layers.

Key Features

01Natural language architectural discovery for entry points and routing

02GPU-accelerated semantic indexing of entire local codebases

030 GitHub stars

04Self-hosted local vector search for maximum privacy and security

05Structured summaries of key architectural layers and data models

06Automated identification of coding patterns and conventions

Use Cases

01Mapping out application flow and data models after a major refactor

02Exploring and understanding architectural patterns in large open-source repositories

03Quickly onboarding a new developer to a complex or legacy project

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add adilasif/local-semantic-search-claude-code-plugin codebase-onboard

For use in Claude.ai and ChatGPT

Key Features

01Natural language architectural discovery for entry points and routing

02GPU-accelerated semantic indexing of entire local codebases

030 GitHub stars

04Self-hosted local vector search for maximum privacy and security

05Structured summaries of key architectural layers and data models

06Automated identification of coding patterns and conventions

Use Cases

01Mapping out application flow and data models after a major refactor

02Exploring and understanding architectural patterns in large open-source repositories

03Quickly onboarding a new developer to a complex or legacy project

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add adilasif/local-semantic-search-claude-code-plugin codebase-onboard

For use in Claude.ai and ChatGPT