rlm-processing

Identify codebase sections and apply Recursive Language Model patterns for analysis.

4|2|Updated Jan 20, 2026

Search Tags:#llm #repl #code-analysis #context-management #rlm #large-codebases

Ready to run

npx skills add https://github.com/anis-marrouchi/ralf --skill rlm-processing

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rlm-processing
Source: https://github.com/anis-marrouchi/ralf/tree/main/skills/rlm-processing
Command: npx skills add https://github.com/anis-marrouchi/ralf --skill rlm-processing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze large codebases efficiently by externalizing context and applying Recursive Language Model (RLM) patterns to operate beyond token limits.

Core Features & Use Cases

External context management: load files into a REPL and chunk, filter, and process code without flooding the LLM context.
Semantic sub-tasks: use llm_query() for focused analyses like pattern discovery, cross-file tracing, and dependency mapping.
End-to-end workflow: supports multi-file analysis across modules, APIs, and configuration files for wide-scale auditing or refactoring.
Use Case: When a project exceeds token limits or requires cross-file reasoning across multiple interconnected files.

Quick Start

Provide a high-level plan to analyze a large repository using RLM patterns.

Frequently Asked Questions about rlm-processing

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

FAQPage Schema

How do I analyze a large codebase that exceeds LLM token limits?▼

You can analyze large codebases exceeding token limits by externalizing context into a REPL-based loader, then chunking and filtering files to apply Recursive Language Model patterns for semantic analysis.

What is a Recursive Language Model pattern for code analysis?▼

A Recursive Language Model pattern for code analysis uses an llm_query interface to break large repository reasoning into focused semantic sub-tasks, enabling cross-file tracing and dependency mapping across interdependent modules.

When do I need external context management for codebase analysis?▼

You need external context management when a project requires correlating multiple interdependent files, locating cross-cutting concerns across modules, or performing wide-scale auditing that exceeds standard LLM context windows.

How do I trace cross-cutting concerns across multiple modules?▼

Trace cross-cutting concerns across multiple modules by loading interconnected files into a REPL context loader, applying file-chunking strategies, and using the llm_query interface to execute focused pattern discovery and dependency mapping.

Does this code analysis approach work without a REPL-based context loader?▼

No, this code analysis approach requires a REPL-based context loader to externalize files and an llm_query interface to perform semantic sub-tasks, as the workflow depends on chunking and processing code outside the LLM context.

What are the limitations of using RLM patterns for large codebase analysis?▼

Limitations of using RLM patterns for large codebase analysis include the strict requirement for a REPL-based context loader and llm_query interface, meaning it cannot operate on raw files directly without external context management infrastructure.