AgentSkillsCN

rlm

递归语言模型,适用于处理超大上下文(超过50KB)。在对标记效率要求较高的复杂分析任务中尤为适用。通过采用Query()和FINAL()模式,让大型语言模型以程序化方式探索上下文,从而实现40%的标记节省。

SKILL.md
--- frontmatter
name: rlm
description: Recursive Language Model for processing large contexts (>50KB). Use for complex analysis tasks where token efficiency matters. Achieves 40% token savings by letting the LLM programmatically explore context via Query() and FINAL() patterns.
allowed-tools:
  - Bash

RLM - Recursive Language Model

RLM is an inference-time scaling strategy that enables LLMs to handle arbitrarily long contexts by treating prompts as external objects that can be programmatically examined and recursively processed.

When to Use

Use rlm instead of direct LLM calls when:

  • Processing large contexts (>50KB of text)
  • Token efficiency is important (40% savings on large contexts)
  • The task requires iterative exploration of data
  • Complex analysis that benefits from sub-queries

Do NOT Use When

  • Context is small (<10KB) - overhead not worth it
  • Simple single-turn questions
  • Tasks that don't require data exploration

Command Usage

bash
# Basic usage with context file
~/.local/bin/rlm -context <file> -query "<query>" -verbose

# With inline context
~/.local/bin/rlm -context-string "data" -query "<query>"

# Pipe context from stdin
cat largefile.txt | ~/.local/bin/rlm -query "<query>"

# JSON output for programmatic use
~/.local/bin/rlm -context <file> -query "<query>" -json

Options

FlagDescriptionDefault
-contextPath to context file-
-context-stringContext string directly-
-queryQuery to run against contextRequired
-modelLLM model to useclaude-sonnet-4-20250514
-max-iterationsMaximum iterations30
-verboseEnable verbose outputfalse
-jsonOutput result as JSONfalse
-log-dirDirectory for JSONL logs-

How It Works

RLM uses a Go REPL environment where LLM-generated code can:

  1. Access context as a string variable
  2. Make recursive sub-LLM calls via Query() for focused analysis
  3. Use standard Go operations for text processing
  4. Signal completion with FINAL() when done

The Query() Pattern

go
// LLM generates code like this inside the REPL:
chunk := context[0:10000]
summary := Query("Summarize the key findings in this text: " + chunk)
// ... iterate through more chunks
FINAL(combinedResult)

The FINAL() Pattern

The LLM signals completion by calling:

  • FINAL("answer") - Return a string answer
  • FINAL_VAR(variableName) - Return value of a variable

Token Efficiency Benefits

For large contexts (>50KB), RLM typically achieves 40% token savings by:

  • Only sending relevant context chunks to sub-queries
  • Avoiding repeated full-context processing
  • Using programmatic iteration instead of full-context reasoning

Examples

Analyze Log Files

bash
rlm -context server.log -query "Find all unique error patterns and their frequencies"

Process JSON Data

bash
rlm -context data.json -query "Extract all user IDs with failed transactions" -verbose

Code Analysis

bash
cat src/*.go | rlm -query "Identify all exported functions and their purposes"

Requirements

  • ANTHROPIC_API_KEY environment variable must be set
  • Binary installed at ~/.local/bin/rlm

Installation

bash
# Quick install
curl -fsSL https://raw.githubusercontent.com/XiaoConstantine/rlm-go/main/install.sh | bash

# Or with Go
go install github.com/XiaoConstantine/rlm-go/cmd/rlm@latest