deep-search

Consolidate scholarly literature into a structured findings.md with a companion literature-map using PaperCLI retrieval.

11|1|Updated Feb 23, 2026

Search Tags:#pdf #research #systematic #evidence #literature #findings #papercli

Ready to run

npx skills add https://github.com/jimezsa/opencolab --skill deep-search-jimezsa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-search
Source: https://github.com/jimezsa/opencolab/tree/main/projects/SKILLS/deep-search
Command: npx skills add https://github.com/jimezsa/opencolab --skill deep-search-jimezsa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Consolidates scattered scholarly literature into an evidence-grounded, end-to-end synthesis.

Core Features & Use Cases

Iterative multi-wave retrieval using PaperCLI to locate, download, and read relevant PDFs.
Equation-level analysis and structured extraction to produce a findings.md and a literature-map.
Companion literature-map diagram generation to visualize method families and evidence connections.
Use Case: For a topic like "deep learning in biology," run a full literature sweep and generate a defensible, cite-backed report.

Quick Start

Run a multi-wave literature search with PaperCLI on your topic and generate a findings.md report with a validated literature-map.

Frequently Asked Questions about deep-search

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

FAQPage Schema

What is a systematic literature review and how does evidence-backed synthesis work?▼

A systematic literature review consolidates scattered scholarly papers into an evidence-grounded synthesis. Evidence-backed synthesis works by performing multi-wave retrieval, deep reading PDFs, and structured cross-paper analysis to produce a defensible report.

How do I extract mathematical equations and key concepts from research PDFs?▼

To extract mathematical equations and key concepts from research PDFs, use a deep search process that downloads full texts and applies equation-level analysis. This yields structured findings and a literature-map diagram visualizing method families and evidence connections.

Does this literature discovery process require PaperCLI for PDF retrieval?▼

Yes, this literature discovery process requires PaperCLI as its retrieval backbone. PaperCLI is used to locate, download, and read relevant PDFs during the iterative multi-wave search phase before extracting findings and generating the literature-map.

Can I generate a literature map to visualize method families and evidence connections?▼

Yes, you can generate a companion literature-map diagram to visualize method families and evidence connections. This diagram is produced alongside a findings.md report after completing the structured cross-paper analysis of downloaded PDFs.

What is the best way to consolidate scattered scholarly literature into a defensible report?▼

The best way to consolidate scattered scholarly literature into a defensible report is through iterative multi-wave retrieval using PaperCLI. This method downloads PDFs, extracts key concepts and equations, and produces a validated findings.md with a companion literature-map.

When should I not use automated deep search for complex scientific questions?▼

Automated deep search for complex scientific questions is not suitable for simple lookups or non-scholarly sources. This approach is designed specifically for complex scientific queries requiring multi-wave retrieval, deep PDF reading, mathematical extraction, and structured cross-paper analysis.