Can I get the results in a machine-readable format?

Yes, RLM supports a -json flag which outputs the final analysis and logs in JSON format for easy integration into other developer workflows.

Does this skill require an external API key?

Yes, the RLM Go implementation requires a valid ANTHROPIC_API_KEY environment variable to perform the recursive LLM calls.

How does RLM achieve 40% token savings?

RLM saves tokens by programmatically exploring context chunks. Instead of sending the entire large file to the LLM at once, it uses sub-queries to fetch only relevant snippets, avoiding redundant full-context processing.

What programming language does the RLM environment use?

RLM uses a Go REPL. The model generates Go code to interact with the context, perform string operations, and trigger sub-queries.

What is the minimum context size recommended for RLM?

RLM is optimized for large contexts. It is recommended for use with data over 50KB. For contexts smaller than 10KB, the overhead of the recursive environment may not be worth the minimal token savings.

Recursive Language Model (RLM)

Name: Recursive Language Model (RLM)
Author: XiaoConstantine

byXiaoConstantine

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Data Science & ML

Processes massive text contexts with high token efficiency using recursive inference-time scaling and Go-based exploration.

About

RLM (Recursive Language Model) is a specialized tool designed to help Claude handle arbitrarily large contexts exceeding 50KB by treating prompts as external objects that can be programmatically examined. By utilizing a Go-based REPL environment, the skill allows Claude to execute recursive sub-queries and iterative data exploration, achieving approximately 40% token savings compared to traditional full-context processing. This approach is ideal for complex analysis tasks, large-scale log processing, and deep data extraction where standard context windows are insufficient or too costly.

Key Features

Go REPL environment for precise text manipulation and logic execution
Recursive context processing for files and strings over 50KB
4 GitHub stars
Support for iterative 'Query' and 'FINAL' patterns for structured reasoning
Flexible input handling via files, inline strings, or stdin piping
Up to 40% token cost reduction via programmatic context exploration

Use Cases

Analyzing massive server log files to identify unique error patterns and frequencies
Extracting specific data points or user records from large-scale JSON datasets
Performing comprehensive code audits and documentation generation across entire source directories

About

Key Features

Go REPL environment for precise text manipulation and logic execution
Recursive context processing for files and strings over 50KB
4 GitHub stars
Support for iterative 'Query' and 'FINAL' patterns for structured reasoning
Flexible input handling via files, inline strings, or stdin piping
Up to 40% token cost reduction via programmatic context exploration

Use Cases

Analyzing massive server log files to identify unique error patterns and frequencies
Extracting specific data points or user records from large-scale JSON datasets
Performing comprehensive code audits and documentation generation across entire source directories