Recursive Decomposition Skill

Handle long-context tasks with Claude Code through recursive decomposition

What It Does • Installation • Usage • How It Works • Benchmarks • Acknowledgments

The Problem

When analyzing large codebases, processing many documents, or aggregating information across dozens of files, Claude's context window becomes a bottleneck. As context grows, "context rot" degrades performance:

Missed details in long documents
Decreased accuracy on information retrieval
Hallucinated connections between distant content
Degraded reasoning over large evidence sets

The Solution

This skill implements Recursive Language Model (RLM) strategies from Zhang, Kraska, and Khattab's 2025 research, enabling Claude Code to handle inputs up to 2 orders of magnitude beyond normal context limits.

Instead of cramming everything into context, Claude learns to:

Filter — Narrow search space before deep analysis
Chunk — Partition inputs strategically
Recurse — Spawn sub-agents for independent segments
Verify — Re-check answers on smaller, focused windows
Synthesize — Aggregate results programmatically

What It Does

Task Type	Without Skill	With Skill
Analyze 100+ files	Context overflow / degraded results	Systematic coverage via decomposition
Multi-document QA	Missed information	Comprehensive extraction
Codebase-wide search	Manual iteration	Parallel sub-agent analysis
Information aggregation	Incomplete synthesis	Map-reduce pattern

Real Test Results

We tested on the Anthropic Cookbook (196 files, 356MB):

Task: "Find all Anthropic API calling patterns across the codebase"

Results:
├── Files scanned: 142
├── Files with API calls: 18
├── Patterns identified: 8 distinct patterns
├── Anti-patterns detected: 4
└── Output: Comprehensive report with file:line references

Installation

Via Claude Code Marketplace

# Add the marketplace
claude plugin marketplace add massimodeluisa/recursive-decomposition-skill

# Install the plugin
claude plugin install recursive-decomposition@recursive-decomposition

From Local Clone

# Clone the repository
git clone https://github.com/massimodeluisa/recursive-decomposition-skill.git ~/recursive-decomposition-skill

# Add as local marketplace
claude plugin marketplace add ~/recursive-decomposition-skill

# Install the plugin
claude plugin install recursive-decomposition

Manual Installation (Skills Directory)

# Copy skill directly to Claude's skills directory
cp -r plugins/recursive-decomposition/skills/recursive-decomposition ~/.claude/skills/

After installation, restart Claude Code for the skill to take effect.

Updating

# Update marketplace index
claude plugin marketplace update

# Update the plugin
claude plugin update recursive-decomposition@recursive-decomposition

Usage

The skill activates automatically when you describe tasks involving:

Large-scale file analysis ("analyze all files in...")
Multi-document processing ("aggregate information from...")
Codebase-wide searches ("find all occurrences across...")
Long-context reasoning ("summarize these 50 documents...")

Example Prompts

"Analyze error handling patterns across this entire codebase"

"Find all TODO comments in the project and categorize by priority"

"What API endpoints are defined across all route files?"

"Summarize the key decisions from all meeting notes in /docs"

"Find security vulnerabilities across all Python files"

Trigger Phrases

The skill recognizes these patterns:

"analyze all files"
"process this large document"
"aggregate information from"
"search across the codebase"
Tasks involving 10+ files or 50k+ tokens

When to Use

The skill is designed for complex, long-context tasks. Use it when:

Analyzing 10+ files simultaneously
Processing documents exceeding 50k tokens
Performing codebase-wide pattern analysis
Extracting information from multiple scattered sources
Multi-hop reasoning requiring evidence synthesis

When NOT to use:

Single file edits → Direct processing is faster
Specific function lookup → Use Grep directly
Tasks < 30k tokens → Overhead not worth it
Time-critical operations → Latency matters more than completeness

How It Works

Decomposition Strategies

1. Filter Before Deep Analysis

1000 files → Glob filter → 100 files
100 files  → Grep filter → 20 files
20 files   → Deep analysis

Result: 50x reduction before expensive processing

2. Strategic Chunking

Uniform: Split by line count or natural boundaries
Semantic: Partition by logical units (functions, classes)
Keyword-based: Group by shared characteristics

3. Parallel Sub-Agents

Main Agent
├── Sub-Agent 1 (Batch A) ─┐
├── Sub-Agent 2 (Batch B) ─┼── Parallel
├── Sub-Agent 3 (Batch C) ─┘
└── Synthesize results

4. Verification Pass

Re-check synthesized answers against focused evidence to catch context rot errors.

Benchmarks

From the RLM paper:

Task	Direct Model	With RLM	Improvement
Multi-hop QA (6-11M tokens)	70%	91%	+21%
Linear aggregation	Baseline	+28-33%	Significant
Quadratic reasoning	<0.1%	58%	Massive
Context scaling	2^14 tokens	2^18 tokens	16x

Cost: RLM approaches are ~3x cheaper than summarization baselines while achieving superior quality.

Repository Structure

recursive-decomposition-skill/
├── .claude-plugin/
│   └── marketplace.json          # Marketplace manifest
├── plugins/
│   └── recursive-decomposition/
│       ├── .claude-plugin/
│       │   └── plugin.json       # Plugin manifest
│       ├── README.md             # Plugin documentation
│       └── skills/
│           └── recursive-decomposition/
│               ├── SKILL.md      # Core skill instructions
│               └── references/
│                   ├── rlm-strategies.md
│                   ├── cost-analysis.md
│                   ├── codebase-analysis.md
│                   └── document-aggregation.md
├── assets/
│   └── logo.png                  # Project logo
├── AGENTS.md                     # Agent-facing docs
├── CONTRIBUTING.md               # Contribution guidelines
├── LICENSE
└── README.md

Skill Contents

File	Purpose
`SKILL.md`	Core decomposition strategies and patterns
`references/rlm-strategies.md`	Detailed techniques from the RLM paper
`references/cost-analysis.md`	When to use recursive vs. direct approaches
`references/codebase-analysis.md`	Full walkthrough: multi-file error handling analysis
`references/document-aggregation.md`	Full walkthrough: multi-document feature extraction

Acknowledgments

This skill is based on the Recursive Language Models research paper. Huge thanks to the authors for their groundbreaking work:

Alex L. Zhang
@a1zhang
_{MIT CSAIL}

Tim Kraska
@tim_kraska
_{MIT Professor}

Omar Khattab
@lateinteraction
_{MIT CSAIL, Creator of DSPy}

Paper

Recursive Language Models

Alex L. Zhang, Tim Kraska, Omar Khattab

arXiv:2512.24601 • December 2025

We propose Recursive Language Models (RLMs), an inference technique enabling LLMs to handle prompts up to two orders of magnitude beyond model context windows through programmatic decomposition and recursive self-invocation over prompt segments.

References

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

Author

Massimo De Luisa — @massimodeluisa

License

MIT License — see LICENSE for details.