A methodical AI research agent providing exhaustive, multi-cycle investigations with APA-compliant documentation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install academic-deep-research
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install academic-deep-research using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Academic Deep Research?
Academic Deep Research is a specialized tool for Openclaw Skills designed to move beyond simple AI responses toward scholarly rigor. It employs a transparent methodology including evidence hierarchies and mandatory research cycles to ensure findings are verified across multiple high-quality sources. This skill is ideal for professionals needing reproducible intelligence rather than black-box summaries.
By integrating natively with Openclaw Skills, it provides a structured approach to literature reviews, competitive analysis, and complex trend reporting. The framework mandates user checkpoints to align research goals before executing deep-dive investigations, ensuring the final narrative report meets high academic standards. It is not just a search tool, but a systematic thinking engine that documents the evolution of understanding through every phase of the research process.
Academic Deep Research Use Cases
- Conducting exhaustive literature reviews for academic or professional papers.
- Performing deep-dive competitive intelligence and market trend analysis.
- Verifying complex claims using a hierarchy of evidence across multiple sources.
- Generating comprehensive state of the industry reports with full APA 7th citations.
- Investigating multi-faceted topics that require parallel research tracks for faster synthesis.
How Academic Deep Research Works
- Phase 1: Initial Engagement involves clarifying the research problem and depth through 2-3 targeted questions to the user.
- Phase 2: The agent presents a detailed Research Execution Plan, including major themes and expected deliverables, for explicit user approval.
- Phase 3: Mandated Research Cycles begin, involving a landscape analysis (Cycle 1) and a deep investigation (Cycle 2) for every identified theme.
- Between tool calls, the agent explicitly documents the evolution of its understanding, addressing contradictions and connecting new findings to previous results.
- Phase 4: A final, cohesive research paper is generated in narrative prose, featuring integrated APA citations and a complete reference list.
Academic Deep Research Setup
To get started with this skill within your Openclaw environment, use the following command:
openclaw install kesslerio/academic-deep-research-clawhub-skill
Ensure your environment has access to the web_search and web_fetch tools, as these are critical for the multi-cycle research methodology defined in this Openclaw Skills package. You can trigger the workflow by using the /research command or asking for an exhaustive analysis.
Academic Deep Research Data Schema & Taxonomy
The skill organizes data through a structured research lifecycle and maintains a transparent evidence trail. This ensures that every claim in the final output is traceable.
| Component | Format | Purpose |
|---|---|---|
| Research Plan | Markdown Table | Outlines themes, tools, and expected outputs for user approval. |
| Evidence Trail | Metadata | Links every conclusion to multiple sources using the Evidence Hierarchy. |
| Final Report | Narrative Prose | APA 7th formatted document with strict prose requirements (no lists). |
| References | APA List | Alphabetized bibliography with DOI/URLs for all cited works. |
| Confidence Annotations | Metadata Tags | [HIGH], [MEDIUM], or [LOW] ratings based on source quality and consistency. |
Academic Deep Research Advanced Features
- Parallel research tracks using sessions_spawn to investigate independent themes simultaneously without cross-contamination.
- Cross-theme knowledge integration to identify meta-patterns and systemic insights across different research tracks.
- Memory search integration via memory_get to leverage and build upon prior research findings stored in your Openclaw Skills history.
- Automated evidence hierarchy validation, prioritizing systematic reviews and randomized controlled trials over secondary reports.
- Comprehensive error handling for empty search results or inaccessible primary sources, including automated confidence level adjustments.
SKILL.md
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