academic-deep-research

Orchestrate multi-cycle literature reviews with explicit checkpoints and APA-style citations.

Updated Feb 6, 2026
One-click install
npx skills add https://github.com/Nutopia13/geo-intelligence-vault --skill academic-deep-research
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: academic-deep-research
Source: https://github.com/Nutopia13/geo-intelligence-vault/tree/main/skills/academic-deep-research
Command: npx skills add https://github.com/Nutopia13/geo-intelligence-vault --skill academic-deep-research

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Academic Deep Research skill provides a transparent, reproducible framework for exhaustive investigations using native OpenClaw tools. It ensures methodology visibility, mandated checkpoints, and a strict narrative workflow from planning to final reporting.

Core Features & Use Cases

  • Mandated two-cycle research per theme with explicit analysis between tool uses to surface evolving understanding and contradictions.
  • Self-contained research workflow that leverages web_search, web_fetch, sessions_spawn, memory_search, memory_get, and structured narrative outputs to produce academically rigorous results.
  • Ideal for literature reviews, competitive intelligence with source verification, and complex topics requiring multi-source synthesis with explicit traceability.

Quick Start

Use the academic-deep-research skill to initiate a structured literature review. Example: /research "current state of AI coding assistants" followed by planning and two-cycle execution.

Frequently Asked Questions about academic-deep-research

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

FAQPage Schema
How do I conduct a reproducible literature review with proper source verification?

To conduct a reproducible literature review, use a mandated multi-cycle workflow with explicit checkpoints between tool uses. This ensures methodology visibility, source verification, and traceability from planning to final reporting.

What is the best way to structure a multi-source synthesis for complex academic topics?

The best way to structure multi-source synthesis is applying a mandated two-cycle research process per theme. This surfaces evolving understanding and contradictions through explicit analysis phases before generating APA-style citations.

Can I generate APA-style citations and apply evidence hierarchies automatically during deep research?

Yes, you can generate APA-style citations and apply evidence hierarchies by running structured narrative workflows. The process mandates traceable references and on-demand scripts throughout the exhaustive investigation phases.

Does this deep research workflow support competitive intelligence and regulatory analyses?

Yes, this deep research workflow supports competitive intelligence and regulatory analyses. It handles complex topics requiring multi-source synthesis with explicit traceability, ensuring strict phase-based methodology and reproducible results.

How to start an exhaustive investigation using web search and memory tools?

Start an exhaustive investigation by initiating a structured planning phase, then execute two research cycles using web_search, web_fetch, and memory_search. Explicit analysis between tool uses ensures transparent, reproducible outputs.

When should I not use a mandated two-cycle research approach for literature reviews?

You should not use a mandated two-cycle research approach for simple, single-source queries lacking complex synthesis requirements. It is designed for exhaustive investigations where methodology visibility, traceability, and reproducibility are essential.