Gemini RLM (Minimal)

gemini-rlm-min

Minimal implementation of Recursive Language Models (RLM) using Gemini 2.0 Flash and a local Python REPL. Enables processing of massive contexts via the Gemini CLI.

majiayu000/claude-skill-registry·0installs663starsSecurity

SKILL.md

Full skill instructions

Gemini RLM (Minimal)

Purpose: Provide a lightweight, CLI-based implementation of the Recursive Language Model architecture using Google's Gemini models. This skill allows for processing extremely large documents by orchestrating chunking, sub-LLM processing, and synthesis entirely via a Python script and the Gemini API.

Architecture

Based on arXiv:2512.24601 - Recursive Language Models.

Component	Implementation	Model
Root LLM	`gem_rlm.py` (Orchestrator)	Gemini 2.0 Flash
Sub-LLM	`gem_rlm.py` (Chunk Processor)	Gemini 2.0 Flash
External Environment	`scripts/rlm_repl.py`	Python 3

Prerequisites

Environment Variable: GEMINI_API_KEY must be set in your shell environment.
```
export GEMINI_API_KEY="your_api_key_here"
```

Usage

The primary entry point is the gem_rlm.py script.

Syntax

${SKILLS_ROOT}/gemini-rlm-min/gem_rlm.py --context <path_to_large_file> --query <"your query"> [options]

Options

--chunk-size: Size of chunks in characters (default: 50000)
--overlap: Overlap between chunks in characters (default: 0)

Examples

Analyze a large log file:

export GEMINI_API_KEY="AIza..."
${SKILLS_ROOT}/gemini-rlm-min/gem_rlm.py --context ./large_logs.txt --query "Identify all security exceptions and their timestamps"

Summarize a book:

${SKILLS_ROOT}/gemini-rlm-min/gem_rlm.py --context ./mobydick.txt --query "Summarize the relationship between Ahab and Starbuck" --chunk-size 100000

How It Works

Initialization: The script initializes a persistent Python REPL (rlm_repl.py) and loads the large context file into memory.
Chunking: The context is split into manageable chunks (e.g., 50k chars) using the REPL.
Sub-LLM Processing: The script iterates through each chunk, sending it to gemini-2.0-flash-exp with a prompt to extract relevant information.
Synthesis: The extracted findings from all chunks are aggregated and sent to the Root LLM (also Gemini 2.0 Flash) to generate the final answer.

File Structure

gemini-rlm-min/
├── SKILL.md              # This definition file
├── gem_rlm.py            # Main CLI Orchestrator
├── scripts/
│   └── rlm_repl.py       # Persistent REPL environment
└── state/                # Runtime state storage (chunks, pickle files)

Integration with IRP

This skill serves as a high-speed, low-overhead alternative to the full rlm-context-manager when:

Quick analysis is needed via CLI.
The context needs to be processed entirely by Gemini models.
Minimal dependencies are preferred (no complex agent setup required).

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/majiayu000/claude-skill-registry --skill gemini-rlm-min

View on GitHub

skills/data/gemini-rlm-min/SKILL.md

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