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ARAVINDAN20
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deep-research

Coordinate six-phase literature reviews into structured notes, a paper database, and a final report.

4|1|Updated Apr 8, 2026
Search Tags:#research#synthesis#notes#phases#reproducibility#literature-review#paper-database
One-click install
npx skills add https://github.com/ARAVINDAN20/Claude-Research-Paper-OS --skill deep-research-aravindan20
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/ARAVINDAN20/Claude-Research-Paper-OS/tree/main/.claude/skills/agent-research-skills/skills/deep-research
Command: npx skills add https://github.com/ARAVINDAN20/Claude-Research-Paper-OS --skill deep-research-aravindan20

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyMuPDF, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Manual literature reviews are time-consuming, fragmented, and prone to bias. This skill coordinates a rigorous, repeatable six-phase workflow that yields structured notes, a searchable paper database, and a synthesized final report, enabling faster topic understanding and reproducible insights.

Core Features & Use Cases

  • Enforces a strict six-phase execution with phase-gate checks to ensure completeness.
  • Generates per-paper notes and a centralized paper database for easy access and traceability.
  • Automatically compiles a final synthesized report, linking insights across phases and papers.
  • Integrates with optional resources (scripts, references, assets) to support reproducible workflows.
  • Use cases include topic discovery, literature landscape analyses, grant/proposal preparation, and methodological benchmarking across domains.

Quick Start

Activate this skill and provide a topic to initiate a six-phase literature review that outputs phased notes, a paper database, and a final synthesized report.

Frequently Asked Questions about deep-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I conduct a systematic literature review to reduce manual bias and save time?▼

You start a systematic literature review by providing a topic, which triggers a six-phase workflow that generates per-paper notes, a searchable paper database, and a final synthesized report linking insights across all phases.

What is the best way to structure notes and build a paper database during a literature scoping process?▼

Yes, structured literature reviews support methodological benchmarking across research domains by applying a rigorous six-phase process that yields a synthesized final report comparing insights and methods from a curated paper database.

Do I need PyMuPDF to extract and process references for a reproducible literature synthesis?▼

Yes, PyMuPDF is required to process PDF references and assets, supporting the reproducible workflows needed to generate phase-specific artifacts and compiled synthesized reports during your literature review.

Can I use this systematic review workflow for grant proposal preparation and topic discovery?▼

Yes, you can use this workflow for grant proposal preparation and topic discovery, as it applies rigorous phase-gate checks to map the literature landscape and compile insights across diverse research domains.

What are the limitations of manual literature reviews compared to a phased approach?▼

Manual literature reviews are time-consuming, fragmented, and prone to bias, whereas a phased approach enforces completeness through phase gates and automatically compiles linked insights into a final synthesized report.

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