用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill gbif-api命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
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| name | gbif-api |
| description | Global biodiversity data API for species occurrences and datasets |
| metadata | {"openclaw":{"emoji":"🔍","category":"domains","subcategory":"ecology","keywords":["ecology","biodiversity","taxonomy","environmental science"],"source":"https://www.gbif.org/developer/summary"}} |
The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure funded by governments worldwide, aimed at providing open access to biodiversity data. GBIF aggregates hundreds of millions of species occurrence records from natural history collections, citizen science platforms, monitoring networks, and published literature across the globe.
The GBIF API provides programmatic access to this vast repository of biodiversity data. Researchers can search for species occurrences by taxonomy, geography, time period, and dataset. The API also supports taxonomic name matching, dataset discovery, and species profile lookups. It serves as a foundational resource for ecological research, conservation planning, biogeography, and environmental impact assessments.
Ecologists, conservation biologists, biogeographers, and environmental scientists rely on the GBIF API to retrieve georeferenced occurrence data for species distribution modeling, climate change impact analysis, invasive species tracking, and biodiversity hotspot identification. The data is freely available under open data licenses.
No authentication required for read access. The GBIF API is publicly accessible without any API key or token. All search and retrieval endpoints are open. User authentication is only required for data publishing operations (creating datasets and uploading occurrences), which requires a GBIF account.
Search for georeferenced biodiversity observation and specimen records across all GBIF-indexed datasets.
GET https://api.gbif.org/v1/occurrence/search| Parameter | Type | Required | Description |
|---|---|---|---|
| q | string | No | Full-text search query |
| taxonKey | int | No | GBIF backbone taxonomy key |
| scientificName | string | No | Scientific name to filter by |
| country | string | No | ISO 3166-1 alpha-2 country code |
| hasCoordinate | bool | No | Filter for georeferenced records only |
| year | string | No | Year or range (e.g., 2020,2024) |
| limit | int | No | Number of results (default 20, max 300) |
| offset | int | No | Pagination offset |
curl "https://api.gbif.org/v1/occurrence/search?scientificName=Panthera+tigris&hasCoordinate=true&limit=10"
count (total matches), results array with key, scientificName, decimalLatitude, decimalLongitude, country, basisOfRecord, eventDate, datasetKey, publishingOrgKey, and media links.Match a species name against the GBIF backbone taxonomy to resolve canonical names and get taxonomy keys.
GET https://api.gbif.org/v1/species/match| Parameter | Type | Required | Description |
|---|---|---|---|
| name | string | Yes | Scientific name to match |
| kingdom | string | No | Kingdom filter for disambiguation |
| strict | bool | No | If true, only return exact matches |
curl "https://api.gbif.org/v1/species/match?name=Homo+sapiens"
usageKey, scientificName, canonicalName, rank, status, kingdom, phylum, class, order, family, genus, species, confidence score, and matchType.Search for and retrieve metadata about GBIF-indexed datasets from publishers worldwide.
GET https://api.gbif.org/v1/dataset| Parameter | Type | Required | Description |
|---|---|---|---|
| q | string | No | Full-text search query |
| type | string | No | Dataset type: OCCURRENCE, CHECKLIST, etc. |
| publishingOrg | string | No | Publishing organization UUID |
| limit | int | No | Number of results (default 20, max 1000) |
| offset | int | No | Pagination offset |
curl "https://api.gbif.org/v1/dataset?q=bird+monitoring&type=OCCURRENCE&limit=5"
count, results array with key, title, description, type, publishingOrganizationKey, license, recordCount, and endpoints.No formal rate limits are enforced on the GBIF API. However, GBIF recommends responsible use patterns. Large data downloads (millions of records) should use the asynchronous download API at https://api.gbif.org/v1/occurrence/download/request rather than paginating through the search endpoint. The search endpoint is limited to 100,000 records maximum per query via pagination.
Retrieve georeferenced occurrence data for species distribution modeling:
import requests
params = {
"taxonKey": 2480498, # Panthera tigris
"hasCoordinate": True,
"limit": 300
}
resp = requests.get("https://api.gbif.org/v1/occurrence/search", params=params)
data = resp.json()
coordinates = [(r["decimalLongitude"], r["decimalLatitude"])
for r in data["results"]
if "decimalLongitude" in r and "decimalLatitude" in r]
print(f"Retrieved {len(coordinates)} georeferenced occurrences of {data['results'][0]['scientificName']}")
Resolve a list of species names against the GBIF backbone taxonomy:
import requests
names = ["Homo sapiens", "Canis lupus", "Quercus robur", "Drosophila melanogaster"]
for name in names:
resp = requests.get("https://api.gbif.org/v1/species/match", params={"name": name})
match = resp.json()
print(f"{name} -> {match['canonicalName']} (key: {match['usageKey']}, confidence: {match['confidence']})")
For large-scale analyses requiring millions of records, use the asynchronous download API:
curl -X POST "https://api.gbif.org/v1/occurrence/download/request" \
-H "Content-Type: application/json" \
-u username:password \
-d '{"creator":"username","predicate":{"type":"equals","key":"TAXON_KEY","value":"2480498"}}'