academic-researcher

shubhamsaboo/awesome-llm-apps · updated Apr 8, 2026

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$npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill academic-researcher
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summary

Comprehensive academic research assistant for literature reviews, paper analysis, and scholarly writing.

  • Provides structured paper analysis framework covering research questions, methodology, findings, implications, and limitations
  • Supports multiple citation formats (APA 7th, MLA 9th, Chicago 17th) with ready-to-use templates
  • Includes literature review structure template with sections for theoretical frameworks, thematic synthesis, research gaps, and conclusions
  • Enforces academic
skill.md

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

## 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:

## 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.

how to use academic-researcher

How to use academic-researcher on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add academic-researcher
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill academic-researcher

The skills CLI fetches academic-researcher from GitHub repository shubhamsaboo/awesome-llm-apps and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/academic-researcher

Reload or restart Cursor to activate academic-researcher. Access the skill through slash commands (e.g., /academic-researcher) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.661 reviews
  • Olivia Haddad· Dec 24, 2024

    academic-researcher is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Hana Dixit· Dec 20, 2024

    Useful defaults in academic-researcher — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Olivia Farah· Dec 16, 2024

    Useful defaults in academic-researcher — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Pratham Ware· Dec 12, 2024

    I recommend academic-researcher for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Hiroshi Mensah· Dec 12, 2024

    academic-researcher has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Omar Reddy· Nov 15, 2024

    academic-researcher reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Carlos Verma· Nov 11, 2024

    I recommend academic-researcher for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Jin Smith· Nov 7, 2024

    I recommend academic-researcher for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yash Thakker· Nov 3, 2024

    Useful defaults in academic-researcher — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Soo Ndlovu· Nov 3, 2024

    Solid pick for teams standardizing on skills: academic-researcher is focused, and the summary matches what you get after install.

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