decompose-mcp
Decompose any text into classified semantic units — authority, risk, attention, entities.
Setup & Installation
Install command
clawhub install echology-io/decompose-mcpIf the CLI is not installed:
Install command
npx clawhub@latest install echology-io/decompose-mcpOr install with OpenClaw CLI:
Install command
openclaw skills install echology-io/decompose-mcpor paste the repo link into your assistant's chat
Install command
https://github.com/openclaw/skills/tree/main/skills/echology-io/decompose-mcpWhat This Skill Does
Decomposes text or URLs into classified semantic units with authority levels, risk categories, attention scores, and entity extraction. No LLM or API calls involved. Runs locally with deterministic output.
Unlike LLM-based parsing, it produces identical output on repeated runs, costs nothing, works offline, and processes over 1,000 characters per millisecond.
When to Use It
- Pre-filtering a 200-page construction spec before sending to an LLM
- Extracting all OSHA and ASTM references from a safety policy
- Identifying mandatory vs. permissive clauses in a vendor contract
- Routing high-attention compliance sections to specialized review
- Building structured training datasets from raw regulatory documents
View original SKILL.md file
# Decompose
Decompose any text or URL into classified semantic units. Each unit gets authority level, risk category, attention score, entity extraction, and irreducibility flags. No LLM required. Deterministic. Runs locally.
## Setup
### 1. Install
```bash
pip install decompose-mcp
```
### 2. Configure MCP Server
Add to your OpenClaw MCP config:
```json
{
"mcpServers": {
"decompose": {
"command": "python3",
"args": ["-m", "decompose", "--serve"]
}
}
}
```
### 3. Verify
```bash
python3 -m decompose --text "The contractor shall provide all materials per ASTM C150-20."
```
## Available Tools
### `decompose_text`
Decompose any text into classified semantic units.
**Parameters:**
- `text` (required) — The text to decompose
- `compact` (optional, default: false) — Omit zero-value fields for smaller output
- `chunk_size` (optional, default: 2000) — Max characters per unit
**Example prompt:** "Decompose this spec and tell me which sections are mandatory"
**Returns:** JSON with `units` array. Each unit contains:
- `authority` — mandatory, prohibitive, directive, permissive, conditional, informational
- `risk` — safety_critical, security, compliance, financial, contractual, advisory, informational
- `attention` — 0.0 to 10.0 priority score
- `actionable` — whether someone needs to act on this
- `irreducible` — whether content must be preserved verbatim
- `entities` — referenced standards and codes (ASTM, ASCE, IBC, OSHA, etc.)
- `dates` — extracted date references
- `financial` — extracted dollar amounts and percentages
- `heading_path` — document structure hierarchy
### `decompose_url`
Fetch a URL and decompose its content. Handles HTML, Markdown, and plain text.
**Parameters:**
- `url` (required) — URL to fetch and decompose
- `compact` (optional, default: false) — Omit zero-value fields
**Example prompt:** "Decompose https://spec.example.com/transport and show me the security requirements"
## What It Detects
- **Authority levels** — RFC 2119 keywords: "shall" = mandatory, "should" = directive, "may" = permissive
- **Risk categories** — safety-critical, security, compliance, financial, contractual
- **Attention scoring** — authority weight x risk multiplier, 0-10 scale
- **Standards references** — ASTM, ASCE, IBC, OSHA, ACI, AISC, AWS, ISO, EN
- **Financial values** — dollar amounts, percentages, retainage, liquidated damages
- **Dates** — deadlines, milestones, notice periods
- **Irreducibility** — legal mandates, threshold values, formulas that cannot be paraphrased
## Use Cases
- Pre-process documents before sending to your LLM — save 60-80% of context window
- Classify specs, contracts, policies, regulations by obligation level
- Extract standards references and compliance requirements
- Route high-attention content to specialized analysis chains
- Build structured training data from raw documents
## Performance
- ~14ms average per document on Apple Silicon
- 1,000+ chars/ms throughput
- Zero API calls, zero cost, works offline
- Deterministic — same input always produces same output
## Security & Trust
**Text classification is fully local.** The `decompose_text` tool performs all processing in-process with no network I/O. No data leaves your machine.
**URL fetching performs outbound HTTP requests.** The `decompose_url` tool fetches the target URL, which necessarily involves network I/O to the specified host. This is why the skill declares the `network` permission in `claw.json`. If you do not need URL fetching, you can use `decompose_text` exclusively with no network access required.
**SSRF protection.** URL fetching blocks private/internal IP ranges before connecting: `0.0.0.0/8`, `10.0.0.0/8`, `100.64.0.0/10`, `127.0.0.0/8`, `169.254.0.0/16`, `172.16.0.0/12`, `192.168.0.0/16`, `::1/128`, `fc00::/7`, `fe80::/10`. The implementation resolves the hostname via DNS *before* connecting and checks all returned addresses against the blocklist. See [`src/decompose/mcp_server.py` lines 19-49](https://github.com/echology-io/decompose/blob/main/src/decompose/mcp_server.py#L19-L49).
**No API keys or credentials required.** No external services are contacted except when using `decompose_url` to fetch user-specified URLs.
**Source code is fully auditable.** The complete source is published at [github.com/echology-io/decompose](https://github.com/echology-io/decompose). The PyPI package is built from this repo via GitHub Actions ([`publish.yml`](https://github.com/echology-io/decompose/blob/main/.github/workflows/publish.yml)) using PyPI Trusted Publishers (OIDC), so the published artifact is traceable to a specific commit.
## Resources
- [Source Code (GitHub)](https://github.com/echology-io/decompose) — full source, auditable
- [PyPI](https://pypi.org/project/decompose-mcp/) — published via Trusted Publishers
- [Documentation](https://echology.io/decompose)
- [Blog: When Regex Beats an LLM](https://echology.io/blog/regex-beats-llm)
- [Blog: Why Your Agent Needs a Cognitive Primitive](https://echology.io/blog/cognitive-primitive)
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Pre-filtering a 200-page construction spec before sending to an LLM
- 1Pre-filtering a 200-page construction spec before sending to an LLM
- 2Extracting all OSHA and ASTM references from a safety policy
- 3Identifying mandatory vs. permissive clauses in a vendor contract
- 4Routing high-attention compliance sections to specialized review
- 5Building structured training datasets from raw regulatory documents
Decompose any text into classified semantic units — authority, risk, attention, entities.
Security Audits
These signals reflect official OpenClaw status values. A Suspicious status means the skill should be used with extra caution.