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academic-deep-research

Conduct multi-cycle research with evidence hierarchy and APA 7th citations.

203|27|Updated Apr 19, 2026
Search Tags:#web search#literature review#research planning#deep research#evidence synthesis#apa citations
One-click install
npx skills add https://github.com/franklee16/academic-research-skills --skill academic-deep-research-franklee16
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: academic-deep-research
Source: https://github.com/franklee16/academic-research-skills/tree/main/literature-review/academic-deep-research
Command: npx skills add https://github.com/franklee16/academic-research-skills --skill academic-deep-research-franklee16

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the problem of receiving “deep research” outputs that are hard to audit or reproduce by producing a transparent, evidence-backed investigation process with explicit user checkpoints.

Core Features & Use Cases

  • Mandated multi-cycle research per theme: Runs at least two full research cycles for each major theme (broad landscape discovery and then a targeted deep dive).
  • User-controlled stop points: Requires you to approve the scope first, then approve the research plan, and finally review the completed report.
  • Evidence hierarchy and confidence labels: Uses an evidence hierarchy (e.g., meta-analyses > RCTs > observational > expert opinion) and assigns confidence annotations to conclusions.
  • Academic-grade writing and citations: Produces flowing narrative prose with APA 7th-style citations integrated into the argument, including methodology, limitations, and references.
  • Self-contained, reproducible workflow: Uses native OpenClaw tools (such as web_search, web_fetch, and sessions_spawn) rather than relying on opaque external API wrappers.

Quick Start

Ask for a deep, exhaustive literature-style analysis by saying: Deep research on "the effects of X on Y", ensuring APA 7th citations, explicit evidence hierarchy, and three approval checkpoints.

Frequently Asked Questions about academic-deep-research

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

FAQPage Schema
How do I get verifiable evidence synthesis with APA 7th citations for a literature review?▼

Verifiable evidence synthesis with APA 7th citations is produced by running multi-cycle deep research that applies an evidence hierarchy, confidence annotations, and explicit user approval checkpoints to ensure reproducible academic reports.

What is multi-cycle deep research and how does it improve complex topic investigations?▼

Multi-cycle deep research mandates at least two full research cycles per theme, starting with broad landscape discovery followed by a targeted deep dive, ensuring exhaustive investigation and transparent evidence synthesis for complex topics.

How do I conduct reproducible deep research without opaque external API wrappers?▼

Reproducible deep research without opaque external API wrappers is achieved by using native web search and web fetch tools to generate transparent, evidence-structured academic reports rather than black-box summaries.

Can I control the research scope and plan before the deep dive generates a final report?▼

You can control the research scope and plan through three explicit user stop points, requiring you to approve the initial scope, then the research plan, and finally review the completed academic report.

Does this deep research approach assign confidence labels based on an evidence hierarchy?▼

This deep research approach assigns confidence labels based on a strict evidence hierarchy, ranking sources from meta-analyses and RCTs down to expert opinion to annotate the reliability of synthesized conclusions.

What are the limitations of using automated literature reviews for competitive intelligence?▼

Automated literature reviews for competitive intelligence require mandatory user approvals at three stop points and rely on transparent web search tools, meaning the process is interactive rather than fully autonomous and depends on native tool availability.

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