RLM REPL Environment

Name: RLM REPL Environment
Availability: InStock
Author: Magic8Ball

Provide a persistent Python REPL with global context and batched sub-LM queries.

Updated Jan 20, 2026

Search Tags:#context #python #repl #recursive #llm_query #batched-prompting

Ready to run

npx skills add https://github.com/Magic8Ballin/rlm-skills --skill rlm-repl-environment

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: RLM REPL Environment
Source: https://github.com/Magic8Ballin/rlm-skills/tree/main/rlm-repl-environment
Command: npx skills add https://github.com/Magic8Ballin/rlm-skills --skill rlm-repl-environment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a persistent Python REPL environment that enables recursive prompting and batched sub-LM queries, helping teams reason over large inputs without losing context.

Core Features & Use Cases

Persistent context is exposed via a global context variable containing the original input.
The llm_query(prompt) function enables recursive delegation to sub-LMs for chunked analysis.
Signaling completion with FINAL() or FINAL_VAR() ensures explicit, verifiable outputs.
Print statements reveal intermediate results with truncation to support iterative reasoning.
The workflow supports batching and error-handling guidance to manage large contexts safely.

Quick Start

Start the REPL, inspect the available tools, and use llm_query() to delegate tasks in batches. When a result is ready, call FINAL() or FINAL_VAR() to emit the answer.

Frequently Asked Questions about RLM REPL Environment

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

FAQPage Schema

How do I maintain persistent context in a Python REPL for recursive LLM reasoning?▼

A persistent Python REPL maintains a global context variable containing the original input, enabling recursive reasoning and iterative exploration without losing context. It exposes an accessible API with context, llm_query, FINAL, and FINAL_VAR functions.

What is the best way to delegate batched sub-LM queries for large-context analysis?▼

Batched sub-LM querying is handled via the llm_query(prompt) function, which enables recursive delegation to sub-LMs for chunked analysis. The workflow supports batching limits and error-handling guidance to manage large contexts safely.

How do I signal task completion in a recursive REPL loop?▼

You signal completion in a recursive REPL loop by calling FINAL() or FINAL_VAR() to emit explicit, verifiable outputs. This ensures safe completion signaling within the persistent Python environment.

Can I see intermediate results during iterative exploration in a REPL environment?▼

Yes, print statements reveal intermediate results with truncation to support iterative reasoning during recursive exploration. This allows you to inspect available tools and use llm_query() to delegate tasks in batches.

Does the recursive REPL environment require any external dependencies?▼

The recursive REPL environment requires no external dependencies, implementing its persistent context and batched sub-LM querying interface entirely within a standalone Python environment.

When should I use a persistent REPL for recursive prompting instead of standard LLM queries?▼

Use a persistent REPL for recursive prompting when targeting scenarios requiring large-context reasoning, iterative exploration, and safe completion signaling. Standard LLM queries lose context, whereas this REPL loop preserves a global context variable.