Recursive Decomposition

Long inputs degrade model quality: details get missed, distant parts get linked by guesswork, reasoning drifts. The RLM paper calls it context rot. Instead of loading everything into the context window, treat the input as an environment you query with tools: size it, narrow it, split it, delegate independent parts to sub-agents, verify on small windows, and merge results programmatically. Based on Recursive Language Models (Zhang, Kraska, Khattab, 2025).

How to use

• /recursive-decomposition: apply the protocol below to the current task.
• /recursive-decomposition <path or question>: size that input first, then run the protocol on it.

When it applies

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

Protocol

1. Size the input before reading anything: count files (glob, find), lines (wc -l), bytes (ls -lh), pages for PDFs.
2. Filter the search space with searches (content search, file patterns, keywords, file types) before opening any file. Chain filters: file type, then keyword, then meaning.
3. Chunk what remains: natural units (functions, classes, sections), line ranges, or keyword partitions. Batches of 5 to 10 files.
4. Recurse: give each independent batch to a sub-agent with a self-contained brief (files, question, output schema); run batches in parallel.
5. Verify the synthesised answer on a smaller window: extract the minimal evidence and re-check it; settle disagreements with a targeted re-read.
6. Synthesise programmatically: aggregate the structured results, deduplicate, categorise, then write the answer with file and line references.

Rules

• MUST size the input before reading it
• MUST search before reading a directory; NEVER list a tree recursively as a substitute for search
• MUST read large files by line range: over 2,000 lines or 50 KB never in one read; PDFs over 100 pages or 30 MB by metadata or split
• NEVER load more than 5 files into the main context without a written batch plan
• MUST give every sub-agent its own context: the files, the question, the output schema
• MUST spot-check the synthesised result against the sources before answering
• SHOULD read definitions first (grep -n "function") and bodies later; tables of contents and abstracts before full text
• NEVER run the same query over the same content in several sub-agents; partition once into disjoint batches

Tools, agent-agnostic

Need	Use
Find files	the file search or glob tool, or find
Find content	the content search or grep tool, never a full read
Size	wc -l, ls -lh, page count
Read	the file reader with an offset and a limit, or sed -n 'START,ENDp'
Delegate	the sub-agent or task tool, one brief per batch
Aggregate	a scratch file or structured notes, then one final pass

Tool names differ between agents (Claude Code, Codex, Cursor, Gemini CLI); map the row to your agent's equivalent.

Patterns

Codebase analysis

"Find all error handling patterns." Glob the source files, grep catch|throw|Error|except, batch the matches by module (5 to 10 files), one sub-agent per batch with a fixed report schema, merge into a categorised summary with file references. Worked example: references/codebase-analysis.md.

Multi-document question answering

"What features are planned across all PRDs?" Glob the documents, size them, define an extraction schema (name, priority, status, quarter), one sub-agent per document group, deduplicate and categorise, spot-check three entries against the sources. Worked example: references/document-aggregation.md.

Aggregation

"Summarise all TODO comments." Grep TODO|FIXME|HACK, group by module, extract context and priority per group, produce a prioritised list.

Long output

Split the output into sections, generate each independently, store intermediate results in a file, stitch them with a coherence pass.

Cost and quality

Decomposition spends coordination tokens and keeps quality: in the RLM paper, RLM runs were about 3x cheaper than summarisation baselines and scaled from 2^14 to 2^18 tokens with higher accuracy on multi-hop tasks. Thresholds and break-even: references/cost-analysis.md.

Anti-patterns

Anti-pattern	Fix
Reading everything first "to get context"	Size, filter, then read by range
Decomposing a five-file task	Read directly
Sub-agents without the question or the schema	Self-contained briefs
Trusting the merged answer	Spot-check on a small window
Re-querying the same content in several sub-agents	Partition once, disjoint batches

References

• references/rlm-strategies.md: decomposition strategies from the paper
• references/cost-analysis.md: when to decompose, break-even thresholds
• references/codebase-analysis.md: worked example, error handling across a codebase
• references/document-aggregation.md: worked example, feature extraction across PRDs
• Paper: Recursive Language Models, Zhang, Kraska, Khattab, arXiv:2512.24601