What is the primary benefit of using the RLM skill?

The primary benefit is efficiency; it allows Claude to process contexts larger than 50KB while reducing token consumption by approximately 40% through programmatic exploration rather than full-context ingestion.

How does the Query() pattern work within RLM?

The LLM generates Go code that calls a Query() function. This function creates a sub-LLM call focused on a specific chunk of the context, allowing for iterative analysis without overloading the main prompt.

When should I avoid using the RLM skill?

You should avoid using RLM for small contexts (under 10KB) or simple single-turn questions, as the overhead of the recursive environment is not necessary for these tasks.

What are the technical requirements for RLM?

Users must have an ANTHROPIC_API_KEY environment variable set and have the rlm-go binary installed at ~/.local/bin/rlm.

Recursive Language Model Context Processor

Name: Recursive Language Model Context Processor
Author: XiaoConstantine

byXiaoConstantine

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データサイエンスとML

Processes massive contexts through programmatic exploration and recursive analysis to achieve significant token savings.

概要

RLM (Recursive Language Model) is a powerful inference-time scaling skill that allows Claude to handle arbitrarily long contexts by treating prompts as external objects. By using a Go-based REPL environment, it enables the model to programmatically examine data, perform recursive sub-queries, and iterate through chunks of text. This approach is particularly effective for contexts exceeding 50KB, where it can reduce token usage by up to 40% by avoiding the need to process the entire context in every turn.

主な機能

Programmatic Go REPL for precise data navigation
4 GitHub stars
Native support for files, strings, and stdin pipes
Selective chunking via recursive Query() patterns
Up to 40% token savings on high-volume context analysis
Recursive inference-time scaling for large datasets

ユースケース

Extracting specific data points from multi-megabyte JSON files
Deep code analysis across large-scale repositories
Analyzing massive server log files for unique error patterns

概要

主な機能

Programmatic Go REPL for precise data navigation
4 GitHub stars
Native support for files, strings, and stdin pipes
Selective chunking via recursive Query() patterns
Up to 40% token savings on high-volume context analysis
Recursive inference-time scaling for large datasets

ユースケース

Extracting specific data points from multi-megabyte JSON files
Deep code analysis across large-scale repositories
Analyzing massive server log files for unique error patterns