rpg

Build semantic graphs from codebases using rpg-encoder and store results in .rpg/graph.json.

32|6|Updated Feb 5, 2026
One-click install
npx skills add https://github.com/userFRM/rpg-encoder --skill rpg
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rpg
Source: https://github.com/userFRM/rpg-encoder/tree/main/.claude/skills/rpg
Command: npx skills add https://github.com/userFRM/rpg-encoder --skill rpg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI agents often struggle with raw codebases using grep/cat/find; RPG-Encoder provides a semantic, index-based view that makes code structure, behavior, and dependencies navigable in a single query.

Core Features & Use Cases

  • Build a semantic graph of a repo with Tree-sitter-based parsing and entity features.
  • Search by intent or snippets, trace dependencies, and fetch entity details with a single command.
  • Use cases include understanding code paths, assessing impact, and communicating architecture to teammates.

Quick Start

Initialize the graph by running rpg-encoder build, then query results with rpg-encoder search.

Frequently Asked Questions about rpg

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

FAQPage Schema
How do I build a semantic graph from a codebase for AI-assisted code understanding?

You build a semantic graph by running the rpg-encoder build command, which uses Tree-sitter to parse the codebase. It stores the resulting dependency and entity structure in .rpg/graph.json for querying.

Why do AI agents struggle with raw codebases using grep and cat?

AI agents struggle with grep and cat because these tools lack structural context. A semantic graph index provides a navigable view of code behavior and dependencies, enabling accurate code path understanding in a single query.

Can I trace dependencies and assess impact across software projects with a single command?

Yes, you can trace dependencies and assess impact by running rpg-encoder search. It queries the pre-built semantic graph to fetch entity details and cross-file patterns, replacing manual multi-file searches.

Does Tree-sitter parsing work for cross-file pattern detection in large repositories?

Tree-sitter parsing extracts entity features across the repository to build a comprehensive semantic graph. This graph supports cross-file pattern detection and impact analysis by mapping dependencies navigable in a single query.

What is the best way to index code structure for intent-driven code search?

The best way to index code structure is building a semantic graph using Tree-sitter. This creates an intent-driven search index in .rpg/graph.json, allowing you to query architecture and dependencies instead of raw text.

How to query code architecture and fetch entity details after building a semantic graph?

After building the semantic graph with rpg-encoder build, use rpg-encoder search to query the stored graph. This allows you to fetch entity details, trace dependencies, and understand code paths by intent rather than text matching.