academic-deep-research

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

Updated Feb 6, 2026

Search Tags:#transparency #reproducibility #literature-review #OpenClaw #evidence-hierarchy #exhaustive #APA-citations

Ready to run

npx skills add https://github.com/Nutopia13/geo-intelligence-vault --skill academic-deep-research

💬 Prompt for ChatGPT / Claude Chat▼

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.