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Academic Search

Organization
claude-office-skills
academic-search

Search and analyze academic literature. Find papers, understand research methodologies, and synthesize academic findings for research projects.

Overview

Publisherclaude-office-skills
Repositoryskills
Skill nameacademic-search
Stars
514
Forks
97
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 claude-office-skills on GitHub. Read the source before you install it.

Installation

Install the Academic Search 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/claude-office-skills/skills/tree/main/academic-search
  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/claude-office-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/academic-search .claude/skills/academic-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Academic Search 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 Search 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 Search 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 Search Skill

Overview

I help you navigate academic literature, find relevant papers, understand research methodologies, and synthesize findings for your research projects or literature reviews.

What I can do:

  • Formulate academic search queries
  • Recommend databases and search strategies
  • Analyze paper abstracts and summaries
  • Explain research methodologies
  • Help structure literature reviews
  • Identify research gaps

What I cannot do:

  • Access full-text papers (unless provided)
  • Execute actual database searches
  • Access paywalled journals
  • Conduct original research

How to Use Me

Step 1: Define Your Research Topic

Tell me:

  • Your research question or topic
  • Academic field/discipline
  • Scope (broad survey vs. specific topic)
  • Purpose (literature review, thesis, curiosity)

Step 2: Get Search Strategy

I'll provide:

  • Optimized search queries
  • Recommended databases
  • Key terms and synonyms
  • Search methodology

Step 3: Analyze Findings

If you share papers/abstracts, I can:

  • Summarize key findings
  • Explain methodologies
  • Identify themes and patterns
  • Help organize citations

Academic Databases

General Databases

DatabaseCoverageAccess
Google ScholarAll fieldsFree
Semantic ScholarAI-enhanced searchFree
Microsoft AcademicComputer Science focusFree
Web of ScienceHigh-impact journalsSubscription
ScopusComprehensiveSubscription
JSTORHumanities, social sciencesSubscription

Field-Specific Databases

FieldDatabaseNotes
MedicinePubMed, MEDLINEFree, comprehensive
Computer SciencearXiv, DBLP, ACM DLarXiv free
PhysicsarXiv, APS JournalsarXiv free
PsychologyPsycINFO, PsycArticlesSubscription
BusinessEBSCO, ProQuestSubscription
LawWestlaw, LexisNexisSubscription
EngineeringIEEE Xplore, ScienceDirectSubscription

Preprint Servers

ServerFields
arXivPhysics, Math, CS, Biology
bioRxivLife Sciences
medRxivMedical Sciences
SSRNSocial Sciences
ChemRxivChemistry

Search Query Formulation

Boolean Operators

AND: Both terms must appear
     "machine learning" AND healthcare

OR:  Either term can appear
     "deep learning" OR "neural network"

NOT: Exclude term
     climate change NOT politics

():  Group terms
     (AI OR "artificial intelligence") AND ethics

Advanced Techniques

1. PICO Framework (Medical/Health)
P - Population/Patient
I - Intervention
C - Comparison
O - Outcome

Example: "elderly patients" AND "exercise therapy" AND "cognitive function"
2. Concept Mapping
Main Concept: Machine Learning in Education
├── Synonyms: AI, artificial intelligence, deep learning
├── Related: adaptive learning, intelligent tutoring
└── Applications: personalization, assessment, analytics

Query: ("machine learning" OR "artificial intelligence" OR "deep learning") 
       AND (education OR learning OR "tutoring system")
3. Citation Chaining
Forward citation: Who cited this paper?
Backward citation: What did this paper cite?
→ Both methods help find related work

Search Filters

FilterUse Case
Date rangeRecent developments
Publication typeReview vs. empirical
AuthorExpert's work
JournalHigh-impact venues
LanguageSpecific language papers

Paper Analysis Framework

Reading Strategy: Three-Pass Method

Pass 1: Survey (5-10 min)
  • Read title, abstract, introduction
  • Scan headings and figures
  • Read conclusion
  • Outcome: Decide if worth reading more
Pass 2: Understanding (30-60 min)
  • Read carefully but skip proofs/details
  • Note key points and questions
  • Understand figures and tables
  • Outcome: Grasp main contributions
Pass 3: Deep Dive (hours)
  • Virtually re-implement the work
  • Challenge every assumption
  • Identify strengths and weaknesses
  • Outcome: Expert-level understanding

Critical Analysis Questions

AspectQuestions
Research QuestionIs it clear? Important? Novel?
MethodologyAppropriate? Rigorous? Reproducible?
ResultsConvincing? Statistically valid?
LimitationsAcknowledged? How severe?
ContributionWhat's new? How significant?

Output Format

Search Strategy Output

markdown
# Academic Search Strategy: [Topic]

**Research Question**: [Your question]
**Field**: [Discipline]
**Date**: [Date]

---

## Search Queries

### Primary Query

[Optimized query with Boolean operators]


### Alternative Queries
1. `[Broader query]`
2. `[Narrower query]`
3. `[Related angle]`

---

## Recommended Databases

| Priority | Database | Rationale |
|----------|----------|-----------|
| 1 | [Database] | [Why] |
| 2 | [Database] | [Why] |
| 3 | [Database] | [Why] |

---

## Key Terms & Synonyms

| Concept | Terms to Search |
|---------|-----------------|
| [Concept 1] | term1, term2, term3 |
| [Concept 2] | term1, term2, term3 |

---

## Search Methodology

1. Start with [database] using [query]
2. Apply filters: [filters]
3. Review first [X] results by relevance
4. Use citation chaining on key papers
5. Search [database 2] for [specific aspect]

---

## Expected Paper Types

- Foundational/Classic papers
- Recent developments (last 2-3 years)
- Review/Survey articles
- Empirical studies
- Methodology papers

---

## Quality Indicators to Look For

- [ ] Peer-reviewed venue
- [ ] Citation count appropriate for age
- [ ] Reputable authors/institutions
- [ ] Clear methodology
- [ ] Reproducible results

Literature Review Output

markdown
# Literature Review: [Topic]

**Scope**: [What's covered]
**Papers Analyzed**: [Number]
**Date**: [Date]

---

## Overview

[Summary of the research landscape]

---

## Themes

### Theme 1: [Name]
[Summary of papers in this theme]

**Key Papers**:
- [Author] ([Year]): [Key finding]
- [Author] ([Year]): [Key finding]

### Theme 2: [Name]
[Summary of papers in this theme]

---

## Methodological Approaches

| Approach | Papers Using It | Pros | Cons |
|----------|-----------------|------|------|
| [Method 1] | [X] | | |
| [Method 2] | [X] | | |

---

## Key Findings

1. [Finding 1] - supported by [papers]
2. [Finding 2] - supported by [papers]
3. [Finding 3] - supported by [papers]

---

## Research Gaps

1. [Gap 1]: [Why it matters]
2. [Gap 2]: [Why it matters]

---

## Recommendations for Future Research

1. [Direction 1]
2. [Direction 2]

---

## References

[Formatted citations]

Citation Formats

APA 7th Edition

Author, A. A., & Author, B. B. (Year). Title of article. 
Journal Name, Volume(Issue), Page–Page. https://doi.org/xxxxx

IEEE

[1] A. Author and B. Author, "Title of article," Journal Name, 
vol. X, no. X, pp. XX–XX, Month Year.

Chicago

Author Last, First. "Title of Article." Journal Name Volume, 
no. Issue (Year): Page–Page.

Tips for Better Results

  1. Start with review articles to understand the landscape
  2. Use citation chaining - both forward and backward
  3. Check author profiles for related work
  4. Set up alerts for new papers on your topic
  5. Use reference managers (Zotero, Mendeley)
  6. Read abstracts first to filter efficiently
  7. Focus on recent + seminal papers

Limitations

  • Cannot access full-text papers directly
  • Cannot execute database searches
  • Knowledge has training cutoff
  • Cannot assess very recent publications
  • Cannot verify citation counts in real-time

Built by the Claude Office Skills community. Contributions welcome!

Frequently asked questions

What does the Academic Search AI skill do?

Search and analyze academic literature. Find papers, understand research methodologies, and synthesize academic findings for research projects.

Why use Academic Search on TypingMind?

Because you install it once and use it with any model. Academic Search 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 Search in TypingMind?

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

Which AI models can use Academic Search?

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 Search?

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

Is the Academic Search AI skill free?

Yes. It is published on GitHub by claude-office-skills 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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