# Academic Research > Deep academic research using free open-access sources — Semantic Scholar, OpenAlex, arXiv, PubMed Central, and CORE. Searches across databases, retrieves papers, extracts key findings, and produces a well-organized research report with full citations. - Skill: `joshuashepherd/academic-research` (Agent Skill) - Install (CLI): `npx skillmds@latest add joshuashepherd/academic-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/joshuashepherd/academic-research/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: JoshuaShepherd (https://skillmd.com/u/joshuashepherd) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/joshuashepherd/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 `` elements containing title, summary, authors, links - The `` 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: | Field | Description | |-------|-------------| | `title` | Full paper title | | `authors` | All authors with affiliations where available | | `year` | Publication year | | `journal` | Journal or conference name | | `doi` | DOI if available | | `citation_count` | Total citations | | `influential_citations` | Influential citation count (from Semantic Scholar) | | `abstract` | Full abstract | | `tldr` | AI summary if available | | `key_findings` | 3-5 bullet points of main findings/arguments (extracted from abstract or full text) | | `methodology` | Research method used | | `relevance` | Why this paper matters to our research question | | `open_access_url` | Direct link to free full text | | `source_db` | Which database(s) found this paper | ### 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:** ```markdown # 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.