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Get Started Free →Search PLOS open access journals with full-text Solr-powered API
.claude/skills/brycewang-stanford-plos-open-access-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 71% | 0% |
PLOS (Public Library of Science) publishes 7 peer-reviewed open access journals covering biology, medicine, genetics, and more. The Search API provides Solr-powered full-text search across all PLOS content — 350K+ articles, all freely available under CC-BY licenses. No authentication required. Particularly valuable for biomedical and life sciences systematic reviews.
https://api.plos.org/searchbash# Basic keyword search curl "https://api.plos.org/search?q=CRISPR+gene+editing&rows=20&wt=json" # Search in specific fields curl "https://api.plos.org/search?q=title:\"machine learning\"+AND+abstract:biomarker&wt=json" # Filter by journal curl "https://api.plos.org/search?q=microbiome&fq=journal:\"PLOS ONE\"&wt=json" # Filter by date range curl "https://api.plos.org/search?q=COVID-19+vaccine&\ fq=publication_date:[2024-01-01T00:00:00Z TO 2026-12-31T23:59:59Z]&wt=json" # Filter by article type curl "https://api.plos.org/search?q=climate+change&fq=article_type:\"Research Article\"&wt=json" # Return specific fields only curl "https://api.plos.org/search?q=deep+learning&fl=id,title,author,publication_date,score&wt=json"
| Field | Description | Example | |-------|-------------|---------| | title | Article title | title:"attention mechanism" | | abstract | Abstract text | abstract:neural+network | | body | Full text body | body:transformer | | author | Author name | author:"Vaswani" | | subject | Subject area | subject:"Neuroscience" | | journal | Journal name | journal:"PLOS Medicine" |
| Parameter | Description | Default | |-----------|-------------|---------| | q | Solr query (supports AND/OR/NOT) | Required | | fq | Filter query (narrows without affecting score) | None | | fl | Fields to return (comma-separated) | All | | rows | Results per page (max 999) | 10 | | start | Pagination offset | 0 | | sort | Sort order | score desc | | wt | Format: json or xml | xml | | hl | Enable highlighting | false |
| Field | Type | Description | |-------|------|-------------| | id | string | DOI | | title | string | Article title | | author | array | Author names | | abstract | string | Abstract text | | body | string | Full text (large) | | publication_date | date | Publication date | | journal | string | Journal name | | article_type | string | Article type | | subject | array | Subject categories | | score | float | Relevance score |
| Journal | Scope | |---------|-------| | PLOS ONE | Multidisciplinary | | PLOS Biology | Life sciences | | PLOS Medicine | Clinical medicine | | PLOS Genetics | Genetics and genomics | | PLOS Computational Biology | Computational biology | | PLOS Pathogens | Infectious disease | | PLOS Neglected Tropical Diseases | Tropical medicine |
pythonimport requests BASE_URL = "https://api.plos.org/search" def search_plos(query: str, rows: int = 20, journal: str = None, from_date: str = None, fields: str = None) -> list: """Search PLOS open access articles.""" params = { "q": query, "wt": "json", "rows": rows, "fl": fields or "id,title,author,abstract,publication_date,journal,score", } fq_parts = [] if journal: fq_parts.append(f'journal:"{journal}"') if from_date: fq_parts.append( f"publication_date:[{from_date}T00:00:00Z TO NOW]" ) if fq_parts: params["fq"] = " AND ".join(fq_parts) resp = requests.get(BASE_URL, params=params) resp.raise_for_status() data = resp.json() results = [] for doc in data.get("response", {}).get("docs", []): results.append({ "doi": doc.get("id"), "title": doc.get("title"), "authors": doc.get("author", []), "date": doc.get("publication_date", "")[:10], "journal": doc.get("journal"), "abstract": (doc.get("abstract", [""])[0])[:300] if isinstance(doc.get("abstract"), list) else (doc.get("abstract", ""))[:300], }) return results def get_full_text(doi: str) -> str: """Retrieve full text body of a PLOS article.""" params = { "q": f'id:"{doi}"', "fl": "body", "wt": "json", } resp = requests.get(BASE_URL, params=params) resp.raise_for_status() docs = resp.json().get("response", {}).get("docs", []) return docs[0].get("body", "") if docs else "" # Example: search PLOS Computational Biology papers = search_plos( "protein structure prediction", journal="PLOS Computational Biology", from_date="2024-01-01", ) for p in papers: print(f"[{p['date']}] {p['title']}") print(f" DOI: {p['doi']}") # Example: full-text search across all PLOS papers = search_plos("body:reinforcement+learning AND title:robot") for p in papers: print(f"{p['title']} — {p['journal']}")
bash# Phrase proximity search (words within 5 of each other) q=abstract:"machine learning"~5 # Boosted field search q=title:"CRISPR"^2 OR abstract:"CRISPR" # Wildcard search q=title:neuro* # Range query on dates fq=publication_date:[2024-01-01T00:00:00Z TO 2024-12-31T23:59:59Z]
No formal rate limit, but PLOS requests courtesy delays of 1 request per second for bulk operations. No authentication needed.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→pass | 23,333 | 21,829 | -6% | 1 | 1 | 0% | 4,825 | 5,275 | +9% | 0 | 0 | — |
case-03 | pass→pass | 13,882 | 14,775 | +6% | 1 | 1 | 0% | 2,755 | 4,753 | +73% | 0 | 0 | — |
case-01 | fail→pass | 23,993 | 27,457 | +14% | 1 | 1 | 0% | 3,943 | 6,895 | +75% | 0 | 0 | — |
case-04 | pass→pass | 14,065 | 12,491 | -11% | 1 | 1 | 0% | 2,695 | 4,300 | +60% | 0 | 0 | — |
case-05 | pass→pass | 14,283 | 20,707 | +45% | 1 | 1 | 0% | 2,745 | 5,206 | +90% | 0 | 0 | — |
case-06 | pass→pass | 7,429 | 7,035 | -5% | 1 | 1 | 0% | 1,489 | 2,890 | +94% | 0 | 0 | — |
case-07 | fail→fail | 9,652 | 5,983 | -38% | 1 | 1 | 0% | 1,433 | 2,726 | +90% | 0 | 0 | — |
case-08 | pass→pass | 10,948 | 7,926 | -28% | 1 | 1 | 0% | 1,573 | 3,033 | +93% | 0 | 0 | — |
case-09 | fail→pass | 8,319 | 3,430 | -59% | 1 | 1 | 0% | 1,274 | 2,511 | +97% | 0 | 0 | — |
case-10 | fail→pass | 10,550 | 5,142 | -51% | 1 | 1 | 0% | 2,015 | 2,734 | +36% | 0 | 0 | — |
case-11 | pass→pass | 4,830 | 4,805 | -1% | 1 | 1 | 0% | 758 | 2,597 | +243% | 0 | 0 | — |
case-12 | pass→pass | 13,200 | 6,110 | -54% | 1 | 1 | 0% | 2,016 | 2,745 | +36% | 0 | 0 | — |
case-13 | pass→pass | 8,347 | 6,790 | -19% | 1 | 1 | 0% | 1,664 | 3,281 | +97% | 0 | 0 | — |
case-14 | pass→pass | 8,939 | 4,865 | -46% | 1 | 1 | 0% | 1,369 | 2,570 | +88% | 0 | 0 | — |
case-15 | pass→pass | 16,341 | 6,980 | -57% | 1 | 1 | 0% | 2,476 | 3,396 | +37% | 0 | 0 | — |
case-16 | pass→pass | 10,640 | 4,600 | -57% | 1 | 1 | 0% | 1,677 | 2,936 | +75% | 0 | 0 | — |
case-17 | fail→pass | 12,908 | 8,723 | -32% | 1 | 1 | 0% | 2,069 | 3,543 | +71% | 0 | 0 | — |
case-18 | pass→pass | 5,615 | 2,570 | -54% | 1 | 1 | 0% | 1,022 | 2,227 | +118% | 0 | 0 | — |
case-19 | pass→pass | 4,312 | 2,580 | -40% | 1 | 1 | 0% | 796 | 2,253 | +183% | 0 | 0 | — |
case-20 | pass→pass | 5,608 | 2,380 | -58% | 1 | 1 | 0% | 985 | 2,222 | +126% | 0 | 0 | — |
case-21 | fail→pass | 14,809 | 3,547 | -76% | 1 | 1 | 0% | 1,484 | 2,452 | +65% | 0 | 0 | — |
case-22 | pass→pass | 2,948 | 1,889 | -36% | 1 | 1 | 0% | 504 | 2,079 | +313% | 0 | 0 | — |
case-23 | pass→pass | 12,325 | 8,730 | -29% | 1 | 1 | 0% | 2,380 | 3,358 | +41% | 0 | 0 | — |
case-24 | pass→pass | 5,119 | 2,006 | -61% | 1 | 1 | 0% | 853 | 2,226 | +161% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +25 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.