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/dare

Orchestrate end-to-end research with literature surveys, gap analysis, and experiment design.

393|34|Updated Feb 10, 2026
Search Tags:#ideation#gap-analysis#ai-research#literature-review#multi-domain#experiment-design#autonomous-surveys
One-click install
npx skills add https://github.com/Pthahnix/De-Anthropocentric-Research-Engine --skill dare
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: /dare
Source: https://github.com/Pthahnix/De-Anthropocentric-Research-Engine/tree/main/skills/dare
Command: npx skills add https://github.com/Pthahnix/De-Anthropocentric-Research-Engine --skill dare

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates end-to-end AI-conducted research orchestration, turning a user's initial direction into autonomous literature surveys, gap analyses, insights, and experimental plans.

Core Features & Use Cases

  • Intake: converts a topic into a structured research brief.
  • Lit-survey: conducts iterative literature surveys across multiple domains to surface relevant papers and data.
  • Gap-analysis: identifies unaddressed opportunities and research gaps.
  • Insight: synthesizes findings into actionable knowledge and potential hypotheses.
  • Round: orchestrates feedback loops, validation, and refinement cycles.
  • Use Case: research teams seeking rapid, autonomous exploration of new domains without manual scoping.

Quick Start

Provide a research topic and allow DARE to autonomously conduct surveys, identify gaps, generate ideas, and design experiments.

Frequently Asked Questions about /dare

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

FAQPage Schema
How do I automate literature surveys and gap analysis for multi-domain research?▼

Automated literature surveys and gap analysis are handled by orchestrating iterative discovery across multiple domains, surfacing relevant papers, and identifying unaddressed research opportunities autonomously.

What is autonomous AI research orchestration and how does it work?▼

Autonomous AI research orchestration converts a topic into a structured brief, then coordinates intake, literature surveys, gap analysis, ideation, and experiment design through automated feedback loops.

Can I use autonomous surveys to generate experiment designs from a research topic?▼

Yes, autonomous surveys synthesize literature findings into actionable insights and hypotheses, directly generating experimental plans without requiring manual research scoping.

Does this approach work for research teams needing rapid exploration of new domains?▼

This approach suits research teams needing rapid exploration by automating end-to-end intake, multi-domain lit-surveys, and round-based refinement cycles without manual intervention.

What are the limitations of autonomous AI-conducted research orchestration?▼

Autonomous AI-conducted research relies on frontmatter-backed meta-strategies and optional references, meaning complex experimental validation still requires human oversight and domain-specific verification.

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