Academic Research

Conduct deep academic research on: $ARGUMENTS

$ARGUMENTS can include:

• A research topic or question (e.g. the relationship between organizational structure and innovation)
• A discipline filter (e.g. --field neuroscience, --field theology, --field organizational-theory)
• A date range (e.g. --since 2020)
• An output path override (default: _research/academic/)
• A depth flag (e.g. --depth quick for 5-10 papers, --depth deep for 20-30+)
• Empty — ask the user what they want to research

Purpose

This skill performs live academic research across the best free, open-access scholarly databases available. It searches multiple sources, retrieves abstracts and (where available) full text, synthesizes findings, and produces a structured research report with proper citations.

What it produces:

• _research/academic/[topic-slug]/README.md — executive summary and key findings
• _research/academic/[topic-slug]/literature-review.md — organized synthesis of the literature
• _research/academic/[topic-slug]/paper-profiles.md — individual paper summaries with metadata
• _research/academic/[topic-slug]/citation-network.md — key citation relationships and influential papers
• _research/academic/[topic-slug]/source-index.md — all searches run, URLs fetched, retrieval status

Source Databases

1. Semantic Scholar (Primary)

What it is: AI-powered academic search engine from the Allen Institute for AI. Covers 200M+ papers across all fields. Provides abstracts, citation counts, influential citation flags, open-access PDF links, and TLDR summaries.

API Endpoint: https://api.semanticscholar.org/graph/v1/

Key API routes:

# Search for papers by keyword
GET https://api.semanticscholar.org/graph/v1/paper/search?query={query}&limit=20&fields=title,abstract,year,citationCount,influentialCitationCount,openAccessPdf,authors,url,tldr,publicationTypes,journal

# Get a specific paper with full details
GET https://api.semanticscholar.org/graph/v1/paper/{paper_id}?fields=title,abstract,year,citationCount,influentialCitationCount,openAccessPdf,authors,references,citations,url,tldr,journal,publicationTypes,fieldsOfStudy

# Get a paper's references
GET https://api.semanticscholar.org/graph/v1/paper/{paper_id}/references?fields=title,abstract,year,citationCount,authors,url&limit=50

# Get a paper's citations
GET https://api.semanticscholar.org/graph/v1/paper/{paper_id}/citations?fields=title,abstract,year,citationCount,authors,url&limit=50

# Search by author
GET https://api.semanticscholar.org/graph/v1/author/search?query={name}&fields=name,paperCount,citationCount,hIndex

Usage notes:

• No API key required for basic use (rate-limited to ~100 requests/5 min)
• The influentialCitationCount field distinguishes papers that are merely cited from those that are deeply engaged with — prioritize high influential citation counts
• The tldr field provides AI-generated paper summaries — very useful for rapid screening
• The openAccessPdf field links directly to free full text when available
• Use fieldsOfStudy to filter by discipline
• Pagination: use offset parameter for additional results

2. OpenAlex (Comprehensive Metadata)

What it is: Free, open catalog of 250M+ scholarly works. Successor to Microsoft Academic Graph. Rich metadata including institutional affiliations, concepts, citation data, and open-access status.

API Endpoint: https://api.openalex.org/

Key API routes:

# Search works (papers)
GET https://api.openalex.org/works?search={query}&per_page=25&sort=cited_by_count:desc

# Filter by publication year
GET https://api.openalex.org/works?search={query}&filter=publication_year:2020-2025&per_page=25

# Filter by open access
GET https://api.openalex.org/works?search={query}&filter=is_oa:true&per_page=25

# Get a specific work by DOI
GET https://api.openalex.org/works/doi:{doi}

# Search authors
GET https://api.openalex.org/authors?search={name}

# Search concepts/topics
GET https://api.openalex.org/concepts?search={concept}

# Get institution details
GET https://api.openalex.org/institutions?search={name}

Usage notes:

• Completely free, no API key needed (polite pool: add mailto parameter for better rate limits)
• Excellent for citation count data and understanding a paper's impact
• The concepts field maps papers to a hierarchical topic taxonomy — useful for finding related work
• Use filter=is_oa:true to limit results to open-access papers with available full text
• Supports complex filters: filter=publication_year:>2019,concepts.id:C41008148 (computer science)
• Returns open_access.oa_url for direct links to free full text

3. arXiv (Preprints — STEM Fields)

What it is: The preprint server for physics, mathematics, computer science, quantitative biology, statistics, electrical engineering, and economics. All papers are freely available in full text.

API Endpoint: http://export.arxiv.org/api/query

Key API routes:

# Search by keyword
GET http://export.arxiv.org/api/query?search_query=all:{query}&start=0&max_results=20&sortBy=relevance

# Search within title
GET http://export.arxiv.org/api/query?search_query=ti:{query}&max_results=20

# Search within abstract
GET http://export.arxiv.org/api/query?search_query=abs:{query}&max_results=20

# Search by author
GET http://export.arxiv.org/api/query?search_query=au:{author_name}&max_results=20

# Search by category (e.g., cs.AI, cs.CL, physics.soc-ph)
GET http://export.arxiv.org/api/query?search_query=cat:{category}+AND+all:{query}&max_results=20

# Combine with date range
GET http://export.arxiv.org/api/query?search_query=all:{query}+AND+submittedDate:[202001010000+TO+202512312359]&max_results=20

Usage notes:

• Completely free, no API key needed
• Returns Atom XML — parse for <entry> elements containing title, summary, authors, links
• The <link> with title="pdf" provides direct PDF link
• Every paper on arXiv has full text available — this is the richest free source for STEM
• Common categories: cs.AI (AI), cs.CL (NLP), cs.LG (Machine Learning), stat.ML, q-bio, econ
• arXiv papers are preprints — note they may not be peer-reviewed
• Also search via web: https://arxiv.org/search/?query={query}&searchtype=all

4. PubMed Central (Biomedical & Life Sciences)

What it is: NIH's free full-text archive of biomedical and life sciences literature. Millions of articles from peer-reviewed journals.

API Endpoint: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/

Key API routes:

# Search PubMed for article IDs
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term={query}&retmax=20&sort=relevance&retmode=json

# Search PMC (full-text subset) for article IDs
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pmc&term={query}&retmax=20&sort=relevance&retmode=json

# Fetch article summaries by ID
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id={id1,id2,id3}&retmode=json

# Fetch full abstract
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id={id}&rettype=abstract&retmode=text

# Fetch full text from PMC
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=PMC{id}&rettype=full&retmode=xml

# Search with date filter
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term={query}&mindate=2020/01/01&maxdate=2025/12/31&datetype=pdat&retmode=json

Usage notes:

• Free, no API key needed (rate-limited to 3 requests/second without key, 10/sec with key)
• Two databases: pubmed (citations + abstracts) and pmc (full-text open access)
• Always search both — PMC has full text but fewer articles; PubMed has broader coverage
• Use MeSH terms for precise medical/biological searches (e.g., "organizational innovation"[MeSH])
• PMC full-text articles can be retrieved as XML and parsed for sections
• Also searchable via web: https://pubmed.ncbi.nlm.nih.gov/?term={query}

5. CORE (Global Open Access Aggregator)

What it is: Aggregates open-access research papers from repositories and journals worldwide. 200M+ papers, many with full text.

Web Search Endpoint: https://core.ac.uk/search?q={query}

Usage notes:

• Use via web search/fetch — search site:core.ac.uk {query} or fetch search results page
• Aggregates from institutional repositories, so often has papers not found elsewhere
• Good for humanities, social sciences, and non-US/UK research
• Full text often available as PDF download
• Particularly useful when Semantic Scholar and OpenAlex return limited results for a niche topic

Research Process

Phase 1 — Scope & Strategy

Before searching, establish:

1. Core research question — What specific question(s) are we trying to answer?
2. Discipline(s) — Which fields are relevant? This determines which databases to prioritize.
3. Date range — How far back should we look? Default to last 10 years unless topic requires historical depth.
4. Depth — Quick survey (5-10 key papers) or deep review (20-30+ papers)?
5. Search terms — Generate 3-5 search query variations, including:
   • Direct terminology
   • Synonyms and alternative phrasings
   • Related concepts that might yield relevant results
   • Key author names if known

State the strategy before executing.

Phase 2 — Multi-Source Search

Run searches across all relevant databases. For each database:

1. Execute 2-3 query variations to maximize coverage
2. Sort by relevance and citation count to surface the most impactful work
3. Record every search query and result count for the source index
4. Screen results — read titles and abstracts, flag the most relevant papers

Priority order for most topics:

1. Semantic Scholar (broadest, best AI-powered relevance)
2. OpenAlex (best metadata, citation data)
3. arXiv (if STEM-related — full text available)
4. PubMed/PMC (if biomedical/health-related)
5. CORE (for coverage gaps)

For humanities/theology/social science topics:

1. Semantic Scholar
2. OpenAlex
3. CORE (stronger in these fields)
4. Web search for specific journal archives (JSTOR open, etc.)

Phase 3 — Paper Profiling

For each selected paper, extract:

Phase 4 — Literature Synthesis

Organize findings into a structured literature review:

1. Thematic organization — Group papers by sub-theme or argument, not chronologically
2. Consensus and debate — Where do researchers agree? Where do they disagree?
3. Methodological patterns — What approaches dominate? What's missing?
4. Evolution of thinking — How has the field's understanding changed over time?
5. Key gaps — What questions remain unanswered?
6. Seminal works — Which papers are most-cited and foundational?

Phase 5 — Citation Network Analysis

Map the citation relationships:

1. Most-cited papers in our result set — these are the foundational works
2. Citation clusters — groups of papers that cite each other heavily (research communities)
3. Bridge papers — papers that connect different research communities or disciplines
4. Recent high-impact papers — newer papers with rapid citation accumulation
5. Recommended further reading — highly-cited papers in reference lists that we didn't capture

Phase 6 — Output Generation

Write all output files with clean markdown formatting.

README.md structure:

# Academic Research: [Topic]
**Date:** [YYYY-MM-DD]
**Research question:** [question]
**Scope:** [fields, date range, depth]
**Papers reviewed:** [N]

## Key Findings
[3-5 top-level findings with supporting citations]

## Research Landscape
[Brief overview of the state of research on this topic]

## Files in This Report
- [literature-review.md](literature-review.md) — Thematic synthesis
- [paper-profiles.md](paper-profiles.md) — Individual paper summaries
- [citation-network.md](citation-network.md) — Citation analysis
- [source-index.md](source-index.md) — All searches and sources

Key Rules

• Live sources only — All paper data must come from actual API calls or web fetches, not parametric memory. If a database is unavailable, note it and try alternatives.
• No fabricated citations — Never invent paper titles, authors, DOIs, or citation counts. Every paper in the report must trace to a real search result.
• Abstract ≠ full text — When synthesizing, be clear about whether conclusions come from abstracts or full-text reading. Most synthesis will be abstract-based; flag when full text was available and read.
• Citation counts are relative — A paper with 50 citations in a niche field may be more significant than one with 500 in a broad field. Note field context.
• Preprint caveat — arXiv papers are preprints. Always note when a source is not peer-reviewed.
• Source index is mandatory — Every search query, API call, and URL fetch must be logged in source-index.md with result counts and retrieval status.
• Deduplicate across databases — The same paper will appear in multiple databases. Deduplicate by DOI or title match. Note which databases found each paper.