Paper Decompose elevates reading research papers and financial reports from a basic summary to a structural four-layer extraction from an advisor's perspective.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install paper-decompose
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install paper-decompose using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Paper Decompose?
Paper Decompose is a powerful cognitive framework skill designed to structurally extract insight from research papers, industry whitepapers, and financial reports. Moving beyond standard summaries that only recap generic data points, this skill transforms reading into an investment-driven, strategic analysis tool.
By leveraging the specialized four-layer model, the tool exposes hidden conceptual gaps, defines the exact incremental innovation of an author, maps structural changes through descriptive ASCII diagrams, and yields actionable cross-domain inspiration for downstream application and decision-making.
Paper Decompose Use Cases
- Analyzing technical English academic papers from arXiv to extract methodology breakthroughs.
- Structural decomposition of Chinese financial research reports and long WeChat industry analysis articles.
- Deconstructing industrial whitepapers to assess market trends and technology transformations.
- Conducting due diligence on technical papers to inform investment strategies and hold/sell recommendations.
How Paper Decompose Works
- Input Type Identification: Recognizes input document format (PDF, text, URL) and chooses the appropriate processing mode.
- Layer 1 (Gap) Extraction: Identifies the core problem and maps mainstream solutions alongside their explicit shortcomings.
- Layer 2 (Delta) Extraction: Formulates the primary technical or logical breakthrough down to a precise, structured single-sentence innovation statement.
- Layer 3 (Napkin Sketch) Generation: Generates a side-by-side ASCII comparison flow diagram detailing old versus new architecture flows.
- Layer 4 (Inspiration) Synthesis: Creates cross-domain mappings and inversion concepts to make the local logic globally actionable.
- Investment Impact Assessment: Evaluates market fundamentals, valuation parameters, and sentiment to issue a definitive portfolio hold or trade recommendation.
- Output Validation: Ensures all four structural layers are fully populated and that the ASCII schema passes complete layout checks.
Paper Decompose Setup
To integrate this capability into your pipeline, register the skill inside your agent framework configuration directory:
# Install downstream preprocessors and dependencies
npm install @openclaw/info-preprocessor @openclaw/pdf-extractor
# Clone and register the paper decompose skill module
openclaw skill install openclaw-skills/paper-decompose
Paper Decompose Data Schema & Taxonomy
| Layer | Schema Element | Description |
|---|---|---|
| Layer 1: Gap | core_problem | prior_shortcomings |
The primary vulnerability targeted and legacy constraints. |
| Layer 2: Delta | innovation_statement | metrics |
Structural formulation of what was shifted, plus accuracy/speed metrics. |
| Layer 3: Sketch | ascii_flowchart |
Strict side-by-side plain text visual tracking of legacy vs modern systems. |
| Layer 4: Inspiration | inversion_logic | cross_domain_map |
Reversing core mechanisms and porting frameworks to disparate fields. |
| Investment Add-on | portfolio_action |
Financial advisory evaluation including valuation indicators and position updates. |
Paper Decompose Advanced Features
- Strategic Investment Assessment: Evaluates the target papers against micro and macro economic dimensions to generate automated trade alerts (Add, Reduce, Hold, Observe).
- ASCII Anchor Visualizer: Enforces side-by-side text diagrams to visualize architecture pivots directly in pure markdown layouts.
- Multi-Agent Flow Interoperability: Seamlessly strings outputs out-of-the-box into downstream writing agents, content creators, or automated quantitative dashboards.
- Heuristic Quality Gates: Fails pipeline executions safely if any of the core logical layers return unpopulated data, guaranteeing top-tier outputs.
SKILL.md
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