deep-research

Orchestrates a 13-agent pipeline for rigorous academic research and APA 7.0 report generation.

Updated Jul 5, 2026
One-click install
npx skills add https://github.com/huaibovip/research-marketplace --skill deep-research-huaibovip
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: deep-research
Source: https://github.com/huaibovip/research-marketplace/tree/main/plugins/academic-research-skills/skills/deep-research
Command: npx skills add https://github.com/huaibovip/research-marketplace --skill deep-research-huaibovip

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Conducting rigorous academic research requires coordinating question formulation, systematic literature search, source verification, synthesis, and structured reporting — a process that is slow, error-prone, and hard to keep methodologically consistent when done manually. ## Core Features & Use Cases - 13-Agent Research Pipeline: Coordinates specialized agents across 6 phases (scoping, investigation, analysis, composition, review, revision) with devil's advocate checkpoints and ethics review. - 8 Operational Modes: Choose from full research, quick brief, paper review, literature review, fact-check, three-way WHY/HOW/WHAT literature scan, Socratic guided research dialogue, and PRISMA 2020 systematic review with optional meta-analysis. - Source Integrity Verification: Cross-index citation triangulation (Semantic Scholar, OpenAlex, Crossref, arXiv) plus contamination signals, retraction status checks, and predatory journal screening. - Use Case: Ask it to research the impact of AI on higher education quality assurance, and it produces a verified annotated bibliography, cross-source synthesis, and a full APA 7.0 report that passed editorial, ethics, and adversarial review. ## Quick Start Ask the agent to research a specific topic, for example: research the impact of AI on higher education quality assurance.

Frequently Asked Questions about deep-research

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

FAQPage Schema
How do I run a deep research pipeline on a topic?

Provide a clear research topic or question, such as "research the impact of AI on higher education." The pipeline runs six phases from scoping through revision, producing an APA 7.0 report with verified sources. Use quick mode for a 500-1500 word brief instead.

What is the difference between lit-review, three-way-scan, and systematic-review modes?

Three-way-scan gives a fast WHY/HOW/WHAT comparison of a paper shortlist. Lit-review produces an annotated bibliography plus thematic synthesis. Systematic-review runs a full PRISMA 2020 pipeline with risk of bias assessment, meta-analysis, and GRADE tables.

How does the skill verify that cited sources actually exist?

It triangulates each citation across Semantic Scholar, OpenAlex, Crossref, and arXiv APIs, flags preprints published after the 2024 LLM inflection point, checks retraction status, and screens for predatory journals. Degraded API lookups are recorded as omissions rather than false negatives.

When should I use Socratic mode instead of full research mode?

Use Socratic mode when you have a vague interest but no answerable research question. A mentor agent guides you through five questioning layers to converge on a focused question. It never generates candidate questions unless you explicitly request them.

Can the research output be handed off to paper writing?

Yes. The research question brief, methodology blueprint, annotated bibliography, and synthesis report transfer to the academic-paper skill, whose intake agent skips redundant steps. A preregistration sidecar artifact can also be carried byte-for-byte into the writing phase.

What happens when the pipeline finds a critical flaw in my research?

Devil's advocate checkpoints at three stages can block progression on critical-severity issues like fatal logical flaws. Ethics review stops once to confirm critical integrity concerns such as fabrication or missing AI disclosure, and revision loops are capped at two iterations.