Do I need an API key for this skill?

Yes, this skill requires a Voyage AI API key (VOYAGE_API_KEY) to compute the embeddings used for semantic discovery.

Does it re-embed everything every time I run it?

No, it uses incremental updates to compare current content against the existing embedding store, only processing new or modified items to save time and API credits.

What does the Project Content Embedder do?

It generates vector embeddings for your project's skills, agents, and tools, allowing Claude to find and use them via semantic search rather than just keyword matching.

How does it handle Voyage AI rate limits?

The skill includes built-in rate limiting that automatically batches items and introduces necessary delays between requests to comply with Voyage AI API constraints.

Project Content Embedder

Name: Project Content Embedder
Author: jrc1883

byjrc1883

•

データサイエンスとML

Manages project-specific embeddings for skills, agents, and tools to enable semantic discovery and intelligent routing within Claude Code.

The Project Content Embedder skill automates the creation and management of semantic embeddings for local project resources, including custom skills, specialized agents, and MCP tools. By utilizing Voyage AI with built-in rate limiting and incremental updates, it ensures your project-specific logic is discoverable via natural language search. This skill bridges the gap between static documentation and active AI context, allowing Claude to intelligently select the right tool or agent for any given task based on semantic relevance rather than simple keyword matching.

主な機能

015 GitHub stars

02Multi-type support for indexing skills, agents, commands, and MCP tools

03Built-in Voyage AI rate-limiting and batching for stable embedding generation

04Export functionality for seamless integration with MCP semantic search servers

05Incremental updates with intelligent caching to minimize API usage and costs

06Detailed status reporting and health checks for project embedding coverage

ユースケース

01Generating embeddings for local MCP tools to enhance tool selection accuracy

02Synchronizing updated documentation with the semantic routing database during development

03Making custom project-specific skills discoverable via natural language queries

主な機能

015 GitHub stars

02Multi-type support for indexing skills, agents, commands, and MCP tools

03Built-in Voyage AI rate-limiting and batching for stable embedding generation

04Export functionality for seamless integration with MCP semantic search servers

05Incremental updates with intelligent caching to minimize API usage and costs

06Detailed status reporting and health checks for project embedding coverage

ユースケース

01Generating embeddings for local MCP tools to enhance tool selection accuracy

02Synchronizing updated documentation with the semantic routing database during development

03Making custom project-specific skills discoverable via natural language queries