A professional-grade research agent designed to transform complex academic papers into structured, evidence-backed deep dives and technical blog-ready content.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install paper-deep-dive
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-deep-dive 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 Deep Dive?
Paper Deep Dive is an elite skill designed for researchers, developers, and technical writers who need to move beyond surface-level abstracts. It functions as a sophisticated analytical engine that deconstructs single academic papers by synthesizing information from PDFs, arXiv pages, supplementary materials, and official code repositories. The core mission of this skill within the Openclaw Skills ecosystem is to build a comprehensive mental map for the reader, providing both intuitive explanations and formal technical rigor.
Unlike standard summarizers, this tool prioritizes the research lineage, explaining why a paper matters in the broader context of its field. It provides a high-fidelity breakdown of methodologies, key mechanisms, and experimental validity. By strictly categorizing claims based on the strength of available evidence—ranging from direct proof to unverified assertions—it ensures a level of intellectual honesty and critical depth essential for high-level technical reporting and decision-making.
Paper Deep Dive Use Cases
- Preparing high-quality materials for technical blogs, sharing sessions, or team presentations.
- Deciphering complex mathematical frameworks and architectures in AI and computer science papers.
- Evaluating whether experimental results actually support the authors' primary claims.
- Placing a specific paper within the historical and current research trajectory of a field.
- Preparing for implementation by cross-referencing paper theory with official codebases.
How Paper Deep Dive Works
- The agent extracts foundational metadata including title, authors, year, and specific task type.
- It establishes the problem lineage, identifying what previous work achieved and where the current paper fills the gap.
- It deconstructs the methodology, mapping modules, input/output flows, and critical formulas while highlighting differences from baselines.
- Experimental results are analyzed by mapping specific claims to supporting evidence rather than just reciting tables.
- A critical analysis is performed to identify underlying assumptions, unverified claims, and potential failure scenarios.
Paper Deep Dive Setup
To utilize this skill, ensure your environment is configured to handle Openclaw Skills and provide the necessary reference documents. Access the core functionality via the CLI:
# Initialize the deep dive for a specific paper
openclaw run paper-deep-dive --input path/to/paper.pdf
# Or provide an arXiv link directly
openclaw run paper-deep-dive --url https://arxiv.org/abs/xxxx.xxxx
Paper Deep Dive Data Schema & Taxonomy
The skill utilizes a modular reference system to maintain high output standards. Data is organized according to the following taxonomy:
| Reference Component | Purpose |
|---|---|
quickref.md |
Self-checking and quick verification before final output. |
output-template.md |
Standardized structural framework for full chapters. |
visualization.md |
Mermaid diagram specifications for architectural and flow charts. |
evidence-rules.md |
Strict taxonomy for evidence labeling (e.g., Direct Evidence vs. Inference). |
paper-types.md |
Customizes analysis focus based on whether the paper is theoretical, empirical, or system-based. |
Paper Deep Dive Advanced Features
- Multi-mode output: Choose between Standard, Deep Dive (blog-grade), and Restricted (abstract-only) analysis based on data availability.
- Evidence Labeling: Automatically flags claims as Paper Assertion, Direct Evidence, Partial Evidence, Evidence-based Inference, or Unverified.
- Visual Integration: Generates Mermaid diagrams for research lineage, methodology architecture, and flow processes.
- Code-Theory Alignment: Cross-references claims in the text with implementation details found in official code repositories.
- Critical Risk Assessment: Identifies benchmark dependencies, hyperparameter sensitivity, and fairness risks.
SKILL.md
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