How does this skill handle contexts over 80K tokens?

It uses strategy selection to partition and map over chunks of data, employing search and peek operations to maintain awareness of structure without overloading the primary context window.

How do I trigger the RLM skill?

The skill activates automatically when task complexity exceeds a specific threshold, or you can manually trigger it using the /rlm command in the Claude Code interface.

What verification tools does it include?

The skill integrates Pydantic for schema validation, Hypothesis for property-based testing, and CPMpy for constraint programming to ensure code changes are logically sound.

What is a verification chain in this context?

A verification chain (Depth=2) is a pattern where the AI uses a secondary reasoning pass or automated tools to validate the primary solution before finalizing the output.

What is RLM Context Management?

RLM Context Management is a specialized skill for Claude Code that uses Recursive Language Model techniques to handle extremely large codebases and complex reasoning tasks by breaking them into manageable sub-queries.

RLM Context Management

Name: RLM Context Management
Author: rand

byrand

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开发者工具

Extends Claude's reasoning capabilities using recursive language model patterns and externalized context handling for massive codebases.

关于

The RLM Context Management skill integrates Recursive Language Model (RLM) patterns into Claude Code to solve the challenge of unbounded context and complex multi-file reasoning. By externalizing conversation history, tool outputs, and working memory into a manageable REPL, it allows Claude to 'peek' into large files and execute recursive sub-queries for deep semantic understanding. This skill is essential for developers working on large-scale legacy systems or complex microservice architectures where traditional context windows are insufficient, providing advanced verification tools like CPMpy and Hypothesis to ensure structural integrity across refactors.

主要功能

Intelligent context partitioning and mapping for datasets exceeding 80K tokens
Advanced verification chains using Pydantic, Hypothesis, and CPMpy constraint modeling
Recursive query spawning to analyze complex modules via deep semantic sub-calls
Unbounded context handling through externalized memory variables and REPL operations
9 GitHub stars
Granular trajectory event tracking for full visibility into the AI's reasoning process

使用场景

Refactoring large, multi-module repositories with complex cross-file dependencies
Deep debugging of massive log files and intertwined tool outputs using search and peek
Automating property-based testing and constraint validation for critical code paths

关于

主要功能

Intelligent context partitioning and mapping for datasets exceeding 80K tokens
Advanced verification chains using Pydantic, Hypothesis, and CPMpy constraint modeling
Recursive query spawning to analyze complex modules via deep semantic sub-calls
Unbounded context handling through externalized memory variables and REPL operations
9 GitHub stars
Granular trajectory event tracking for full visibility into the AI's reasoning process

使用场景

Refactoring large, multi-module repositories with complex cross-file dependencies
Deep debugging of massive log files and intertwined tool outputs using search and peek
Automating property-based testing and constraint validation for critical code paths