Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Access earth and environmental science datasets via PANGAEA API
.claude/skills/brycewang-stanford-pangaea-data-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 59% | 0% |
PANGAEA is the world's leading data repository for earth and environmental sciences, hosting 400K+ datasets with 20B+ data points. It archives research data from oceanography, paleoclimatology, geology, ecology, and atmospheric science. Each dataset has a DOI and is linked to the originating publication. The API provides search, metadata retrieval, and data download. Free, no authentication required.
bash# Search datasets by keyword curl "https://www.pangaea.de/advanced/search.php?q=ocean+temperature&count=20&type=json" # Search with geographic bounding box curl "https://www.pangaea.de/advanced/search.php?\ q=sediment+core&minlat=-60&maxlat=-30&minlon=-180&maxlon=180&type=json" # Filter by parameter (measurement type) curl "https://www.pangaea.de/advanced/search.php?\ q=carbon+dioxide¶m=Atmospheric+CO2&type=json" # Filter by date range curl "https://www.pangaea.de/advanced/search.php?\ q=Arctic+ice&mindate=2020-01-01&maxdate=2026-12-31&type=json"
bash# Full-text search via Elasticsearch curl -X POST "https://ws.pangaea.de/es/pangaea/panmd/_search" \ -H "Content-Type: application/json" \ -d '{ "query": { "bool": { "must": [ {"match": {"citation.title": "ocean temperature"}} ], "filter": [ {"range": {"citation.year": {"gte": 2020}}} ] } }, "size": 20 }'
bash# Get dataset metadata curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=metainfo_json" # Download dataset as tab-delimited text curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=textfile" # Download as CSV curl "https://doi.pangaea.de/10.1594/PANGAEA.123456?format=csv"
bash# List records curl "https://ws.pangaea.de/oai/provider?verb=ListRecords&metadataPrefix=oai_dc" # Get specific record curl "https://ws.pangaea.de/oai/provider?verb=GetRecord&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.123456&metadataPrefix=oai_dc"
| Parameter | Description | Example | |-----------|-------------|---------| | q | Search query | q=coral+reef+bleaching | | count | Results per page | count=50 | | offset | Pagination offset | offset=20 | | minlat/maxlat | Latitude bounds | -90 to 90 | | minlon/maxlon | Longitude bounds | -180 to 180 | | mindate/maxdate | Temporal filter | 2020-01-01 | | param | Parameter/measurement | Temperature | | topic | Topic filter | Atmosphere, Biosphere | | type | Response format | json, xml |
pythonimport requests import pandas as pd from io import StringIO SEARCH_URL = "https://www.pangaea.de/advanced/search.php" ES_URL = "https://ws.pangaea.de/es/pangaea/panmd/_search" def search_pangaea(query: str, count: int = 20, bbox: dict = None) -> list: """Search PANGAEA for earth science datasets.""" params = {"q": query, "count": count, "type": "json"} if bbox: params.update({ "minlat": bbox.get("south", -90), "maxlat": bbox.get("north", 90), "minlon": bbox.get("west", -180), "maxlon": bbox.get("east", 180), }) resp = requests.get(SEARCH_URL, params=params, timeout=30) resp.raise_for_status() data = resp.json() results = [] for item in data.get("results", []): results.append({ "doi": item.get("URI", ""), "title": item.get("citation", ""), "year": item.get("year"), "size": item.get("size"), "parameters": item.get("params", []), "score": item.get("score"), }) return results def download_dataset(doi: str) -> pd.DataFrame: """Download a PANGAEA dataset as a pandas DataFrame.""" url = f"https://doi.pangaea.de/{doi}?format=textfile" resp = requests.get(url, timeout=60) resp.raise_for_status() lines = resp.text.split("\n") header_end = next( (i for i, line in enumerate(lines) if line.startswith("*/")), -1, ) data_text = "\n".join(lines[header_end + 1:]) return pd.read_csv(StringIO(data_text), sep="\t") def search_by_location(query: str, lat: float, lon: float, radius_deg: float = 5.0) -> list: """Search datasets near a geographic location.""" bbox = { "south": lat - radius_deg, "north": lat + radius_deg, "west": lon - radius_deg, "east": lon + radius_deg, } return search_pangaea(query, bbox=bbox) # Example: find ocean temperature datasets datasets = search_pangaea("sea surface temperature", count=5) for ds in datasets: print(f"[{ds['year']}] {ds['title'][:80]}...") print(f" DOI: {ds['doi']} | Size: {ds['size']}") # Example: download a specific dataset # df = download_dataset("10.1594/PANGAEA.123456") # print(df.head()) # Example: find Arctic research data arctic = search_by_location("permafrost", lat=70, lon=25) for ds in arctic[:3]: print(f"{ds['title'][:80]}...")
| Topic | Coverage | |-------|----------| | Oceans | Temperature, salinity, currents, chemistry | | Paleoclimate | Ice cores, sediment cores, tree rings | | Atmosphere | CO2, aerosols, weather observations | | Lithosphere | Geology, tectonics, geochemistry | | Biosphere | Biodiversity, ecology, marine biology | | Cryosphere | Sea ice, glaciers, permafrost |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 17,435 | 11,519 | -34% | 1 | 1 | 0% | 3,234 | 2,539 | -21% | 0 | 0 | — |
case-02 | fail→pass | 18,198 | 12,512 | -31% | 1 | 1 | 0% | 3,352 | 4,322 | +29% | 0 | 0 | — |
case-03 | fail→pass | 10,382 | 5,264 | -49% | 1 | 1 | 0% | 1,817 | 2,954 | +63% | 0 | 0 | — |
case-04 | pass→pass | 13,381 | 11,403 | -15% | 1 | 1 | 0% | 2,202 | 3,742 | +70% | 0 | 0 | — |
case-05 | pass→pass | 11,620 | 8,592 | -26% | 1 | 1 | 0% | 1,721 | 3,307 | +92% | 0 | 0 | — |
case-06 | pass→pass | 7,961 | 5,835 | -27% | 1 | 1 | 0% | 1,303 | 2,855 | +119% | 0 | 0 | — |
case-07 | fail→pass | 6,941 | 2,446 | -65% | 1 | 1 | 0% | 1,291 | 2,353 | +82% | 0 | 0 | — |
case-08 | fail→pass | 9,558 | 4,276 | -55% | 1 | 1 | 0% | 1,726 | 2,742 | +59% | 0 | 0 | — |
case-09 | fail→pass | 12,089 | 4,490 | -63% | 1 | 1 | 0% | 2,033 | 2,763 | +36% | 0 | 0 | — |
case-10 | fail→pass | 14,490 | 3,431 | -76% | 1 | 1 | 0% | 2,673 | 2,758 | +3% | 0 | 0 | — |
case-11 | pass→pass | 14,236 | 9,518 | -33% | 1 | 1 | 0% | 2,598 | 3,749 | +44% | 0 | 0 | — |
case-12 | pass→pass | 7,514 | 3,157 | -58% | 1 | 1 | 0% | 1,467 | 2,511 | +71% | 0 | 0 | — |
case-13 | pass→pass | 15,495 | 3,419 | -78% | 1 | 1 | 0% | 2,599 | 2,537 | -2% | 0 | 0 | — |
case-14 | fail→pass | 21,495 | 6,507 | -70% | 1 | 1 | 0% | 3,829 | 3,213 | -16% | 0 | 0 | — |
case-15 | fail→pass | 7,123 | 5,549 | -22% | 1 | 1 | 0% | 1,377 | 2,734 | +99% | 0 | 0 | — |
case-16 | fail→pass | 9,715 | 2,496 | -74% | 1 | 1 | 0% | 1,803 | 2,356 | +31% | 0 | 0 | — |
case-17 | pass→pass | 16,246 | 13,953 | -14% | 1 | 1 | 0% | 2,632 | 4,303 | +63% | 0 | 0 | — |
case-18 | fail→pass | 6,599 | 2,419 | -63% | 1 | 1 | 0% | 1,110 | 2,351 | +112% | 0 | 0 | — |
case-19 | fail→pass | 12,218 | 2,255 | -82% | 1 | 1 | 0% | 1,019 | 2,342 | +130% | 0 | 0 | — |
case-20 | fail→pass | 8,780 | 2,388 | -73% | 1 | 1 | 0% | 1,562 | 2,288 | +46% | 0 | 0 | — |
case-21 | fail→pass | 8,131 | 4,915 | -40% | 1 | 1 | 0% | 1,360 | 2,733 | +101% | 0 | 0 | — |
case-22 | pass→pass | 11,169 | 2,448 | -78% | 1 | 1 | 0% | 1,531 | 2,250 | +47% | 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. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +64 percentage points is the difference between those two pass rates over the 21 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.