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 biothings-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.
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基于 SOC 职业分类
| name | biothings-api |
| description | Query gene, variant, and drug annotations via BioThings APIs |
| metadata | {"openclaw":{"emoji":"🧪","category":"domains","subcategory":"biomedical","keywords":["gene annotation","variant annotation","drug data","BioThings","mygene","myvariant","bioinformatics"],"source":"https://biothings.io"}} |
BioThings is a family of high-performance biomedical annotation APIs developed at the Scripps Research Institute. The suite provides unified, up-to-date access to gene, variant, and chemical/drug annotations aggregated from dozens of authoritative sources. Three primary services cover the core entities in translational research:
All three share identical query syntax, require no authentication, and return JSON. Free for academic and commercial use.
No authentication or API keys are required. All endpoints are open-access.
# No API key needed — just query directly
curl "https://mygene.info/v3/query?q=BRCA1&size=1"
GET https://mygene.info/v3/query?q={query}&size={n}
Query by gene symbol, name, Entrez ID, Ensembl ID, or keyword. Supports boolean operators (AND, OR, NOT) and field-specific queries like symbol:CDK2.
curl -s "https://mygene.info/v3/query?q=BRCA1&size=1"
Response:
{
"took": 178,
"total": 13223,
"hits": [
{
"_id": "672",
"_score": 145.6796,
"entrezgene": "672",
"name": "BRCA1 DNA repair associated",
"symbol": "BRCA1",
"taxid": 9606
}
]
}
GET https://mygene.info/v3/gene/{entrez_id}
Returns comprehensive annotations for a single gene. Use the fields parameter to select specific data sources.
# Full annotation (large response)
curl -s "https://mygene.info/v3/gene/1017"
# Selective fields
curl -s "https://mygene.info/v3/gene/1017?fields=symbol,name,summary,genomic_pos,go"
Response (key fields for CDK2, Entrez ID 1017):
{
"_id": "1017",
"symbol": "CDK2",
"name": "cyclin dependent kinase 2",
"HGNC": "1771",
"MIM": "116953",
"AllianceGenome": "1771",
"taxid": 9606,
"type_of_gene": "protein-coding"
}
The full response includes accessions, Gene Ontology terms, pathway memberships (KEGG, Reactome, WikiPathways), protein domains (InterPro, Pfam), homology data, and genomic coordinates.
GET https://myvariant.info/v1/query?q={query}&size={n}
Query by rsID, HGVS notation (e.g., chr7:g.140453136A>T), gene symbol, or ClinVar significance. Returns aggregated annotations from 15+ sources.
curl -s "https://myvariant.info/v1/query?q=rs58991260&size=1"
Response (truncated):
{
"took": 20,
"total": 1,
"hits": [
{
"_id": "chr1:g.218631822G>A",
"_score": 21.382616,
"dbsnp": {
"rsid": "rs58991260",
"vartype": "snv",
"ref": "G",
"alt": "A",
"chrom": "1"
},
"cadd": {
"phred": 1.679,
"consequence": "INTERGENIC",
"chrom":
GET https://myvariant.info/v1/variant/{hgvs_id}
curl -s "https://myvariant.info/v1/variant/chr1:g.218631822G>A?fields=dbsnp,cadd,clinvar"
GET https://mychem.info/v1/query?q={query}&size={n}
Query by drug name, NDC code, InChIKey, or active ingredient. Aggregates data from FDA NDC, DrugBank, ChEMBL, PubChem, SIDER, and more.
curl -s "https://mychem.info/v1/query?q=aspirin&size=1"
Response (truncated):
{
"took": 82,
"total": 248,
"hits": [
{
"_id": "0615-8613",
"_score": 13.657401,
"ndc": {
"substancename": "ASPIRIN",
"nonproprietaryname": "Aspirin",
"proprietaryname": "Adult Low Dose Aspirin",
"active_numerator_strength": "81",
"active_ingred_unit": "mg/1",
"dosageformname": "TABLET, DELAYED RELEASE",
"routename": "ORAL",
"producttypename": "HUMAN OTC DRUG",
GET https://mychem.info/v1/chem/{id}
curl -s "https://mychem.info/v1/chem/CHEMBL25?fields=drugbank,chembl,pubchem"
All BioThings APIs share the same query engine. Key features:
| Feature | Syntax | Example |
|---|---|---|
| Field-specific | field:value | symbol:TP53 |
| Boolean | AND, OR, NOT | BRCA1 AND cancer |
| Wildcard | * | CDK* |
| Range | [min TO max] | exac.af:[0.01 TO 0.05] |
| Pagination | size, from | size=20&from=40 |
| Field selection | fields | fields=symbol,name,go |
| Sorting | sort | sort=_score:desc |
| Batch POST | POST with ids | Up to 1000 IDs per request |
import requests, time
MYGENE = "https://mygene.info/v3"
MYVARIANT = "https://myvariant.info/v1"
MYCHEM = "https://mychem.info/v1"
def search_gene(symbol):
resp = requests.get(f"{MYGENE}/query",
params={"q": f"symbol:{symbol}", "size": 1, "species": "human"})
resp.raise_for_status()
hits = resp.json().get("hits", [])
return hits[0] if hits else {}
def search_variants(gene_symbol, size=5):
resp = requests.get(f"{MYVARIANT}/query",
params={"q": f"clinvar.gene.symbol:{gene_symbol}",
"fields": "dbsnp.rsid,clinvar.rcv.clinical_significance,cadd.phred",
"size": size})
resp.raise_for_status()
return resp.json().get("hits", [])
def search_drug(name):
resp = requests.get(f"{MYCHEM}/query",
params={"q": name, "size": 1,
"fields": "ndc.substancename,ndc.pharm_classes"})
resp.raise_for_status()
hits = resp.json().get(, [])
hits[] hits {}
gene = search_gene()
()
time.sleep()
variants = search_variants(, size=)
v variants:
rsid = v.get(, {}).get(, v.get())
()
time.sleep()
drug = search_drug()
()