rlm

🥈Silver

Recursive Language Model patterns for Claude Code enable handling massive contexts (10M+ tokens) by treating them as external variables. Ideal for operations teams managing large datasets, it integrates with Claude Code to analyze extensive files without direct tool calls.

4120Updated Today

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/richardwhiteii/rlm.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/richardwhiteii/rlm

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm analyzing a large dataset from [COMPANY] in the [INDUSTRY] sector. The data is stored in [FILE_TYPE] files, each containing [TOKEN_COUNT] tokens. Using RLM patterns with Claude Code, help me: 1) Identify key trends, 2) Detect anomalies, and 3) Suggest optimizations. I'll provide the data in chunks as needed.

Example Output

# Analysis of [COMPANY] Dataset

## Key Trends Identified
- **Seasonal Patterns**: Significant spikes in [METRIC] during [SEASON]
- **Growth Areas**: [INDUSTRY] segment showing 15% YoY growth
- **Customer Behavior**: Increased [ACTION] among [DEMOGRAPHIC]

## Anomalies Detected
- **Outlier**: [METRIC] value of [VALUE] in [TIME_PERIOD]
- **Inconsistency**: [DATA_POINT] doesn't align with [RELATED_DATA]

## Optimization Suggestions
- **Recommendation 1**: Implement [STRATEGY] to capitalize on [TREND]
- **Recommendation 2**: Investigate [ANOMALY] to prevent [RISK]
- **Recommendation 3**: Adjust [PROCESS] to better serve [CUSTOMER_SEGMENT]