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rlm

Chunk large context files and delegate analysis to subagents for synthesis.

Updated Aug 23, 2026
Search Tags:#workflow#subagents#repl#chunking#large-context#rlm#document-analysis
One-click install
npx skills add https://github.com/Qredence/skill-fleet --skill rlm-qredence
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rlm
Source: https://github.com/Qredence/skill-fleet/tree/main/skills/practices/rlm
Command: npx skills add https://github.com/Qredence/skill-fleet --skill rlm-qredence

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Process very large context files that exceed model limits by chunking content, delegating analysis to subagents, and synthesizing results to produce actionable insights.

Core Features & Use Cases

  • Chunking: Split large documents, logs, transcripts, or scraped data into manageable chunks without losing context.
  • Subagent orchestration: Dispatch per-chunk analysis to specialized subagents and collect structured results.
  • Synthesis: Aggregate chunk results into a coherent, end-to-end understanding suitable for reporting or decision-making.
  • Stateful workflow: Maintain context and buffers across steps to support iterative analysis on the same file.

Quick Start

Initialise the REPL with a large context file, then chunk the content, analyze each chunk with subagents, and synthesize the final results.

Frequently Asked Questions about rlm

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

FAQPage Schema
How do I analyze large documents that exceed model context limits?▼

To analyze large documents exceeding context limits, chunk the content into manageable segments, delegate per-chunk extraction to subagents, and synthesize the aggregated results into a coherent end-to-end understanding.

What is the best way to process large log files for structured data extraction?▼

Processing large log files involves partitioning the text into chunks, dispatching each chunk to specialized subagents for structured extraction, and synthesizing the collected outputs into actionable insights.

Can I use a subagent workflow to analyze scraped content over 100k characters?▼

Yes, a configurable subagent workflow can analyze scraped content over 100k characters by partitioning the data, performing iterative inspections via a persistent REPL, and synthesizing the extracted results.

How does chunking content help with large transcript analysis?▼

Chunking content allows large transcript analysis to bypass single-pass context limits by splitting the text, delegating per-chunk evaluation to subagents, and maintaining stateful buffers to synthesize a final understanding.

Does processing huge scraped content require a persistent REPL?▼

Processing huge scraped content requires a root orchestrator and a persistent REPL to maintain stateful context and buffers across iterative chunking and subagent analysis steps.

What are the limitations of single-pass analysis for large context files?▼

Single-pass analysis fails on large context files exceeding 100k characters due to context limits, requiring a chunking and subagent orchestration workflow to iteratively inspect and extract information without losing context.

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