Decompose

Deterministic text classification for AI agents. Decompose turns any text into classified, structured semantic units — instantly. No LLM. No setup. One function call.

The contractor shall provide all materials per ASTM C150-20. Maximum load shall not exceed 500 psf per ASCE 7-22. Notice to proceed within 14 calendar days of contract execution. Retainage of 10% applies to all payments. For general background, the project is located in Denver, CO...

[ { "text": "The contractor shall provide all materials per ASTM C150-20.", "authority": "mandatory", "risk": "compliance", "type": "requirement", "irreducible": true, "attention": 8.0, "entities": ["ASTM C150-20"] }, { "text": "Maximum load shall not exceed 500 psf per ASCE 7-22.", "authority": "prohibitive", "risk": "safety_critical", "type": "constraint", "irreducible": true, "attention": 10.0, "entities": ["ASCE 7-22"] } ]

Every unit classified. Every standard extracted. Every risk scored. Your agent knows what matters.

Add to your agent's MCP config (Claude Code, Cursor, Windsurf, etc.):

{ "mcpServers": { "decompose": { "command": "uvx", "args": ["decompose-mcp", "--serve"] } } }

- decompose_text— decompose any text
- decompose_url— fetch a URL and decompose its content

Install the skill from ClawHub or configure directly:

{ "mcpServers": { "decompose": { "command": "python3", "args": ["-m", "decompose", "--serve"] } } }

Or install the skill:clawdhub install decompose-mcp

# Pipe text cat spec.txt | decompose --pretty # Inline decompose --text "The contractor shall provide all materials per ASTM C150-20." # Compact output (smaller JSON) cat document.md | decompose --compact

from decompose import decompose_text, filter_for_llm result = decompose_text("The contractor shall provide all materials per ASTM C150-20.") for unit in result["units"]: print(f"[{unit['authority']}] [{unit['risk']}] {unit['text'][:60]}...") # Pre-filter for LLM context — keep only high-value units filtered = filter_for_llm(result, max_tokens=4000) print(f"{filtered['meta']['reduction_pct']}% token reduction") llm_input = filtered["text"] # Ready for your LLM

Decompose is not the destination. It's the step before the LLM that most developers skip — not because it's hard, but because nobody showed them it exists. Documents have structure. That structure is classifiable. And classification should happen before reasoning.

Without: document → chunk → embed → retrieve → LLM → answer (100% of tokens) With: document → decompose → filter/route → LLM → answer (20-40% of tokens)

filter_for_llm()keeps mandatory, safety-critical, financial, and compliance units — drops boilerplate before it reaches your LLM or vector store.

from decompose import decompose_text, filter_for_llm result = decompose_text(open("contract.md").read()) filtered = filter_for_llm(result, max_tokens=4000) # filtered["text"] = high-value units only, ready for LLM # filtered["meta"]["reduction_pct"] = how much was dropped (typically 60-80%) # Or use the units directly for embedding for unit in filtered["units"]: embed_and_store(unit["text"], metadata={ "authority": unit["authority"], "risk": unit["risk"], "attention": unit["attention"], })

Safety-critical content goes to one chain. Financial content goes to another. Boilerplate gets skipped.

from decompose import decompose_text result = decompose_text(spec_text) for unit in result["units"]: if unit["risk"] == "safety_critical": safety_chain.process(unit) # Full analysis + human review elif unit["risk"] == "financial": audit_chain.process(unit) # Flag for finance team elif unit["attention"] < 0.5: pass # Skip boilerplate else: general_chain.process(unit) # Standard LLM analysis

from decompose import decompose_text result = decompose_text(spec_text) total = len(result["units"]) high = [u for u in result["units"] if u["attention"] >= 1.0] print(f"{len(high)}/{total} units need LLM analysis") print(f"{100 - len(high)  100 // total}% token reduction")

The LLM is whatyour agent*uses. Decompose makes whatever model you're running work better.

Decompose is built byEchologyand extracted fromAECai, a document intelligence platform for Architecture, Engineering, and Construction firms. The classification patterns, entity extraction, and irreducibility detection are battle-tested against thousands of real AEC documents — specs, contracts, RFIs, inspection reports, pay applications.

The same chunking and entity extraction patterns that classify engineering specs also structure the Bible.Open Scripture Intelligenceuses Decompose's Markdown-aware chunker and regex entity extraction to transform 31,100 verses into a knowledge graph with 344,799 cross-reference edges and semantic embeddings — proving the methodology is domain-agnostic.

- When Regex Beats an LLM— Decompose classifies the MCP spec in 3.78ms
- Why Your Agent Needs a Cognitive Primitive— attention scoring, irreducibility, and routing
- What "Simulation-Aware" Actually Means— the architecture behind AECai

65+ AI tools as an MCP server. Research, write, code, scrape, translate, analyze, agent memory, workflows. Pay per call from $0.006.