Recursive Decomposition Skill

Decompose long-context tasks. Keep the context small.
An Agent Skill for Claude Code, Codex, Cursor and compatible agents, based on the Recursive Language Models research

Install · Usage · How it works · Structure · Acknowledgments

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The problem

Large codebases, dozens of documents, long reports: as the context grows, models miss details, link distant parts by guesswork and lose accuracy. The Recursive Language Models paper calls it context rot.

What it does

When a task spans 10+ files or 50k+ tokens, the skill makes the agent treat the input as an environment to query instead of text to swallow:

1. Size the input before reading anything.
2. Filter the search space with searches, not reads.
3. Chunk what remains into batches of 5 to 10 files or natural units.
4. Recurse with one sub-agent per batch, each with a self-contained brief.
5. Verify the merged answer on a small window against the sources.
6. Synthesise programmatically, with file and line references.

Tested on the Anthropic Cookbook (196 files): 142 files scanned, 18 with API calls, 8 patterns and 4 anti-patterns reported with file:line references.

Install

With the skills CLI:

npx skills add massimodeluisa/recursive-decomposition-skill

Add -g for a user-level install, -a claude-code (or another agent) to target one agent.

As a Claude Code plugin:

claude plugin marketplace add massimodeluisa/recursive-decomposition-skill
claude plugin install recursive-decomposition@recursive-decomposition-skill

Manual: copy skills/recursive-decomposition into ~/.claude/skills/ (or your agent's skills directory) and restart the agent.

Usage

• /recursive-decomposition applies the protocol to the current task.
• /recursive-decomposition src/ sizes that input first, then runs the protocol.

The skill also activates on its own for prompts like:

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 issues across all Python files

How it works

Situation	Approach
10+ files, 50k+ tokens, or a multi-hop question across scattered sources	Decompose
30k to 50k tokens	Decompose when completeness matters; otherwise read directly
Under 30k tokens, one file, or a localized answer	Read directly

Results reported in the paper:

Task	Direct model	With RLM
Multi-hop QA (6 to 11M tokens)	70%	91%
Linear aggregation	baseline	+28 to 33%
Quadratic reasoning	under 0.1%	58%
Context scaling	2^14 tokens	2^18 tokens

RLM runs were about 3x cheaper than summarisation baselines.

Repository structure

recursive-decomposition-skill/
├── .claude-plugin/          plugin.json, marketplace.json (the repo is the plugin)
├── .github/                 bash validator and CI workflow
├── skills/recursive-decomposition/
│   ├── SKILL.md             protocol, rules, patterns
│   └── references/          rlm-strategies, cost-analysis, codebase-analysis, document-aggregation
├── assets/                  social preview, logo (light and dark)
├── AGENTS.md · CONVENTIONS.md · CONTRIBUTING.md · CHANGELOG.md
└── LICENSE

Acknowledgments

This skill is based on the Recursive Language Models paper. Thanks to the authors:

Alex L. Zhang MIT CSAIL

Tim Kraska MIT

Omar Khattab MIT CSAIL, creator of DSPy

Recursive Language Models, Alex L. Zhang, Tim Kraska, Omar Khattab, arXiv:2512.24601, December 2025. Abstract · PDF

This skill is an independent project and is not affiliated with the authors or MIT.

Author

Massimo De Luisa: massimo.deluisa.bio

License

MIT, see LICENSE.