Recursive Decomposition

Long inputs rot. Details get missed, distant parts get glued together by guesswork, and the reasoning drifts. The RLM paper calls it context rot. Do not load the whole input into the window. Treat it as an environment you query: size it, narrow it, split it, hand independent parts to sub-agents, verify on a small window, synthesise in code. 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

Fit is not the test. Dense work can rot inside a million-token window. In the paper, OOLONG-Pairs is 32k tokens and GPT-5 scores 0.1% F1; RLM(depth=1) reaches 58.0% (arXiv:2512.24601, Table 1). 30k and 50k below are harness caps.

Situation	Approach
One file, one function, or a single needle	Read directly
Linear aggregate or list-everything, and completeness matters	Decompose (this skill)
Pairwise, quadratic, or multi-hop across scattered sources	Decompose, even under 30k tokens
10+ files or 50k+ tokens	Decompose
Under 30k tokens and a localised 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 at depth 1: one sub-agent per independent batch, self-contained brief (files, question, output schema); run one parallel wave. Write the batch count first. If more batches remain, run the next wave after the merge. Sub-agents answer; they do not spawn sub-agents.
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
• MUST convert PDFs and Office files to markdown before reasoning. Prefer local anydoc (npx -y @firecrawl/anydoc FILE -o .firecrawl/FILE.md) from the Firecrawl convert-documents-to-markdown skill. Use cloud firecrawl parse for OCR (--ocr hosted / firecrawl parse), -Q, or -S. Never dump the binary into context. Cloud parse caps at 50 MB and about 1 credit per PDF page; split first if larger.
• 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 keep recursion depth at 1: sub-agents answer; they MUST NOT launch sub-agents
• MUST write the batch count before launch; one parallel wave, then merge; further waves only after that merge
• 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
• NEVER raise depth because the merge looks thin; re-read the disagreement instead

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 (pdfinfo if present)
Read	the file reader with an offset and a limit, or sed -n 'START,ENDp'
PDF or Office file	anydoc first (npx -y @firecrawl/anydoc FILE -o .firecrawl/out.md), then grep the markdown. Cloud firecrawl parse for OCR, -Q, or -S. Else pdfinfo and a split. Never a full binary read
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.

Large PDFs

Glob *.pdf, size bytes and page count (pdfinfo). Over 100 pages or 30 MB: do not ingest. Convert with anydoc to .firecrawl/ (Firecrawl skill convert-documents-to-markdown), then grep and chunk the markdown. If anydoc exits 3 (scanned pages), rerun with --ocr hosted or firecrawl parse (Firecrawl skill firecrawl-parse, 50 MB / ~1 credit per page). Batch by document, depth 1, spot-check titles against PDF metadata.

A one-page convert-to-markdown job is not this skill. Hand it to anydoc or firecrawl parse alone.

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. In the paper (arXiv:2512.24601, Table 1 and Figure 1), RLM(GPT-5, depth=1) scored 91.3% on BrowseComp-Plus (6 to 11M tokens) at $0.99 average, versus 70.5% for compaction. On OOLONG it beat GPT-5 by 28.4%. On OOLONG-Pairs it reached 58.0% F1 against a 0.1% base. Figure 1 scales inputs from 2^13 to 2^20 tokens. Median cost stays comparable to the base model; averages can rise on outlier trajectories. Thresholds: references/cost-analysis.md.

Anti-patterns

Anti-pattern	Fix
Reading everything first "to get context"	Size, filter, then read by range
Dumping a PDF binary into the window	Parse to markdown, then grep and chunk
Decomposing a single needle or one-file lookup	Read directly
Sub-agents without the question or the schema	Self-contained briefs
Sub-agents that spawn sub-agents	Depth 1: they answer, they do not delegate
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