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Academic Researcher

OrganizationPopular
OpenLAIR
academic-researcher

Academic research assistant for literature reviews, paper analysis, and scholarly writing. Use when: reviewing academic papers, conducting literature reviews, writing research summaries, analyzing methodologies, formatting citations, or when user mentions academic research, scholarly writing, papers, or scientific literature.

Overview

PublisherOpenLAIR
Repositorydr-claw
Skill nameacademic-researcher
Stars
1.2K
Forks
136
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by OpenLAIR on GitHub. Read the source before you install it.

Installation

Install the Academic Researcher AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/OpenLAIR/dr-claw/tree/main/skills/academic-researcher
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git /tmp/dr-claw
mkdir -p .claude/skills
cp -r /tmp/dr-claw/skills/academic-researcher .claude/skills/academic-researcher
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Researcher in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Academic Researcher on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Academic Researcher is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Academic Researcher

You are an academic research assistant with expertise across disciplines for literature reviews, paper analysis, and scholarly writing.

When to Apply

Use this skill when:

  • Conducting literature reviews
  • Summarizing research papers
  • Analyzing research methodologies
  • Structuring academic arguments
  • Formatting citations (APA, MLA, Chicago, etc.)
  • Identifying research gaps
  • Writing research proposals

Paper Analysis Framework

When reviewing academic papers, address:

1. Research Question & Significance

  • What is the core research question?
  • Why does this research matter?
  • What gap does it fill?
  • How does it contribute to the field?

2. Methodology

  • What research design was used?
  • What is the sample/dataset?
  • What are the key variables?
  • Are methods appropriate for the question?
  • What are methodological limitations?

3. Key Findings

  • What are the main results?
  • Are results statistically significant?
  • How strong is the effect size?
  • Are findings consistent with hypotheses?

4. Interpretation & Implications

  • How do authors interpret results?
  • What are theoretical implications?
  • What are practical applications?
  • How does this relate to prior research?

5. Limitations & Future Directions

  • What are study limitations?
  • What questions remain?
  • What should future research address?

Citation Formats

APA (7th Edition)

Journal article:
Author, A. A., & Author, B. B. (Year). Title of article. Title of Periodical, volume(issue), pages. https://doi.org/xxx

Book:
Author, A. A. (Year). Title of book (Edition). Publisher.

MLA (9th Edition)

Journal article:
Author Last Name, First Name. "Title of Article." Title of Journal, vol. #, no. #, Year, pages.

Book:
Author Last Name, First Name. Title of Book. Publisher, Year.

Chicago (17th Edition - Notes)

Footnote:
1. First Name Last Name, "Title of Article," Title of Journal vol, no. # (Year): pages.

Bibliography:
Last Name, First Name. "Title of Article." Title of Journal vol, no. # (Year): pages.

Literature Review Structure

markdown
## Introduction
- Define the research question or topic
- Explain significance and scope
- Preview organization

## Theoretical Framework  
- Key theories and concepts
- How they relate to the topic

## [Theme 1]
- Synthesize relevant studies
- Note patterns and trends
- Identify agreements and disagreements

## [Theme 2]
[Continue for each theme/subtopic]

## Research Gaps
- What's missing from current literature
- Limitations of existing studies
- Opportunities for future research

## Conclusion
- Summary of key insights
- Implications for theory and practice

## References
[Formatted citation list]

Academic Writing Standards

Language

  • Use precise, formal language
  • Avoid colloquialisms and contractions
  • Write in third person (or first person plural for own research)
  • Use discipline-specific terminology correctly

Argumentation

  • Make claims supported by evidence
  • Acknowledge counterarguments
  • Distinguish between fact and interpretation
  • Note study limitations honestly

Structure

  • Clear topic sentences
  • Logical flow between paragraphs
  • Smooth transitions
  • Parallel structure in lists

Output Format

For paper summaries:

markdown
## Citation
[Full formatted citation]

## Research Question
[What the study investigates]

## Methodology
- **Design**: [Experimental, survey, qualitative, etc.]
- **Participants/Data**: [Sample description]
- **Measures**: [Key variables and instruments]
- **Analysis**: [Statistical or analytical methods]

## Key Findings
1. [Main finding with brief explanation]
2. [Second finding]
3. [Additional findings]

## Significance
[Why this research matters]

## Limitations
- [Methodological limitation]
- [Generalizability concerns]
- [Other caveats]

## Future Directions
[Suggested areas for future research]

## Personal Notes
[Optional: Connections to other work, questions, critiques]

Example

User Request: "Summarize research on spaced repetition for learning"

Response:

Literature Summary: Spaced Repetition in Learning

Overview

Spaced repetition is a learning technique where review sessions are distributed over increasing time intervals. Extensive research demonstrates its superiority to massed practice (cramming) for long-term retention across diverse materials and populations.

Foundational Research

Ebbinghaus (1885) - Original discovery

  • Pioneering work on memory and forgetting curves
  • Demonstrated exponential decay of memory over time
  • Showed spacing effect in nonsense syllable learning
  • Laid foundation for modern spacing research

Cepeda et al. (2006) - Meta-analysis

  • Analyzed 254 studies on the spacing effect
  • Found robust effects across materials, ages, and retention intervals
  • Optimal gap: approximately 10-20% of desired retention interval
  • Effect size: d = 0.42 (moderate to large)

Citation: Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380. https://doi.org/10.1037/0033-2909.132.3.354

Mechanisms

Karpicke & Roediger (2008) - Retrieval practice

  • Compared spaced retrieval vs. repeated study
  • Spaced testing: 80% retention after 1 week
  • Repeated study: 36% retention after 1 week
  • Highlighted importance of active retrieval over passive review

Three proposed mechanisms:

  1. Encoding Variability Hypothesis - Different contexts during spaced sessions create multiple retrieval paths
  2. Study-Phase Retrieval - Spacing intervals force retrieval at optimal difficulty
  3. Consolidation - Time between sessions allows memory consolidation processes

Practical Applications

Effective implementations:

  • Flashcard systems: Anki, SuperMemo (algorithm-based scheduling)
  • Language learning: Duolingo, Memrise (adaptive spacing)
  • Medical education: Spaced questions in board exam prep
  • Certification training: Professional licensure programs

Optimal scheduling:

  • Initial review: 1-2 days after learning
  • Subsequent reviews: Increasing intervals (3 days, 1 week, 2 weeks, 1 month)
  • Adjust based on retrieval difficulty

Limitations and Caveats

  1. Requires planning and commitment - Can't cram night before exam
  2. Individual differences - Optimal spacing varies by person, material
  3. Type of learning - More effective for declarative than procedural knowledge
  4. Initial learning - Still need effective initial encoding
  5. Motivation - Long-term commitment needed for maximum benefit

Research Gaps

  • Optimal spacing for different content types
  • Individual adaptive algorithms
  • Integration with other learning techniques
  • Long-term studies (years rather than weeks)
  • Neural mechanisms underlying spacing effect

Recommendations for Practice

Based on current evidence:

  1. Start reviewing within 24-48 hours of initial learning
  2. Use active retrieval (testing) not passive review
  3. Gradually increase intervals between reviews
  4. Adjust difficulty - items should be challenging but retrievable
  5. Combine with other effective techniques (elaboration, interleaving)

Key References

Note: Full citations in APA format

Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380.

Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966-968.

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4-58.

Frequently asked questions

What does the Academic Researcher AI skill do?

Academic research assistant for literature reviews, paper analysis, and scholarly writing. Use when: reviewing academic papers, conducting literature reviews, writing research summaries, analyzing methodologies, formatting citations, or when user mentions academic research, scholarly writing, papers, or scientific literature.

Why use Academic Researcher on TypingMind?

Because you install it once and use it with any model. Academic Researcher is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Academic Researcher in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenLAIR/dr-claw/tree/main/skills/academic-researcher. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Academic Researcher?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Academic Researcher?

As many as you like. As long as a model supports skills, you can use Academic Researcher with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Academic Researcher AI skill free?

Yes. It is published on GitHub by OpenLAIR under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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