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- brycewang-stanford/Auto-Empirical-Research-Skills
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- 2026年4月3日 02:07
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安装方式
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
检查来源文件
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
菜单
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
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 clinicaltrials-api-v2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。
正在显示 SKILL.md
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
| name | clinicaltrials-api-v2 |
| description | Search and analyze clinical trials via the ClinicalTrials.gov v2 API |
| metadata | {"openclaw":{"emoji":"🏥","category":"domains","subcategory":"biomedical","keywords":["clinical trials","ClinicalTrials.gov","medical research","study design","FDA","drug trials"],"source":"https://clinicaltrials.gov/data-api/about-api"}} |
ClinicalTrials.gov is the world's largest clinical trial registry, maintained by the U.S. National Library of Medicine (NLM) at NIH. It contains over 576,000 study records from 220+ countries covering interventional trials, observational studies, and expanded access programs. The v2 API provides structured JSON access with field-level filtering, cursor-based pagination, and statistics endpoints.
Key v2 improvements over the legacy API: JSON-native responses, sparse field selection via the fields parameter, nextPageToken pagination, and dedicated statistics endpoints. Study data is organized into protocolSection (sponsor-submitted) and derivedSection (NLM-computed).
No authentication required. All endpoints are publicly accessible without API keys or registration. Users should comply with NCBI usage policies and maintain reasonable request rates.
GET https://clinicaltrials.gov/api/v2/studies| Parameter | Type | Required | Description |
|---|---|---|---|
| query.term | string | No | Free-text search across all fields |
| query.cond | string | No | Condition or disease filter |
| query.intr | string | No | Intervention or treatment filter |
| query.spons | string | No | Sponsor or collaborator filter |
| filter.overallStatus | string | No | RECRUITING, COMPLETED, ACTIVE_NOT_RECRUITING, etc. |
| filter.phase | string | No | EARLY_PHASE1, PHASE1, PHASE2, PHASE3, PHASE4, NA |
| filter.geo | string | No | Geographic filter (distance(lat,lng,dist)) |
| fields | string | No | Comma-separated fields for sparse response |
| sort | string | No | Sort field and direction (e.g., LastUpdatePostDate:desc) |
| pageSize | int | No | Results per page (default 10, max 1000) |
| pageToken | string | No | Cursor token for next page |
| format | string | No | json (default) or csv |
curl "https://clinicaltrials.gov/api/v2/studies?query.cond=diabetes&query.intr=metformin&pageSize=1&fields=NCTId,BriefTitle,OverallStatus"
{
"studies": [{
"protocolSection": {
"identificationModule": {
"nctId": "NCT06649773",
"briefTitle": "The Experiment of Noiiglutide Injection in Type 2 Diabetes Patients"
},
"statusModule": { "overallStatus": "ACTIVE_NOT_RECRUITING" }
}
}],
"nextPageToken": "ZVNj7o2Elu8o3lpo..."
}
Full responses include protocolSection with: identificationModule (NCT ID, titles, organization), statusModule (status, dates), descriptionModule (summary), conditionsModule, designModule (type, phases, enrollment), armsInterventionsModule, eligibilityModule (criteria, sex, age), outcomesModule, and contactsLocationsModule.
GET https://clinicaltrials.gov/api/v2/studies/{nctId}curl "https://clinicaltrials.gov/api/v2/studies/NCT04280705?fields=NCTId,BriefTitle,OverallStatus,Phase,LeadSponsorName,EnrollmentCount,Condition,InterventionName"
{
"protocolSection": {
"identificationModule": {
"nctId": "NCT04280705",
"briefTitle": "Adaptive COVID-19 Treatment Trial (ACTT)"
},
"statusModule": {
"overallStatus": "COMPLETED",
"startDateStruct": { "date": "2020-02-21" },
"completionDateStruct": { "date": "2020-05-21" }
},
"sponsorCollaboratorsModule": {
"leadSponsor": { "name": "National Institute of Allergy and Infectious Diseases (NIAID)" }
},
"conditionsModule": {
GET https://clinicaltrials.gov/api/v2/stats/sizecurl "https://clinicaltrials.gov/api/v2/stats/size"
{
"totalStudies": 576554,
"averageSizeBytes": 17186,
"largestStudies": [
{ "id": "NCT02723955", "sizeBytes": 3596689 },
{ "id": "NCT03688620", "sizeBytes": 2865033 }
]
}
GET https://clinicaltrials.gov/api/v2/stats/fieldValues/{fieldName}curl "https://clinicaltrials.gov/api/v2/stats/fieldValues/Phase"
{
"type": "ENUM",
"piece": "Phase",
"field": "protocolSection.designModule.phases",
"missingStudiesCount": 136632,
"topValues": [
{ "value": "NA", "studiesCount": 222829 },
{ "value": "PHASE2", "studiesCount": 87478 },
{ "value": "PHASE1", "studiesCount": 63716 },
{ "value": "PHASE3", "studiesCount": 48700
No formal rate limits are published for the v2 API. Follow NCBI usage guidelines: stay under 3 requests/second without an API key, up to 10/second with one. For bulk data access, use the AACT relational database (https://aact.ctti-clinicaltrials.org/) or downloadable flat files rather than paginating through the full API.
query.cond + query.intr + filter.overallStatus=COMPLETED to build PRISMA-compliant trial inventories. Paginate with nextPageToken to collect all records, then extract outcomes and enrollment for quantitative synthesis.stats/fieldValues to map phase distributions, sponsor concentration, and geographic spread for a therapeutic area -- useful for identifying evidence gaps in grant proposals.RECRUITING status and filter.geo to find active enrollment opportunities. Automate periodic queries for new trials in your domain.import requests, time
def collect_trials(condition, intervention, status="COMPLETED"):
base = "https://clinicaltrials.gov/api/v2/studies"
studies, token = [], None
while True:
params = {
"query.cond": condition, "query.intr": intervention,
"filter.overallStatus": status, "pageSize": 100,
"fields": "NCTId,BriefTitle,Phase,EnrollmentCount,CompletionDate",
}
if token:
params["pageToken"] = token
data = requests.get(base, params=params).json()
studies.extend(data.get("studies", []))
token = data.get("nextPageToken")
if not token:
break
time.sleep(0.34)
return studies
trials = collect_trials("type 2 diabetes", "metformin")
print(f"Collected {len(trials)} completed metformin T2D trials")
import requests
from collections import Counter
params = {"query.cond": "Alzheimer's Disease", "pageSize": 100,
"fields": "NCTId,Phase,LeadSponsorName"}
data = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json()
phases, sponsors = Counter(), Counter()
for s in data["studies"]:
p = s["protocolSection"]
for ph in p.get("designModule", {}).get("phases", []):
phases[ph] += 1
sponsors[p.get("sponsorCollaboratorsModule", {})
.get("leadSponsor", {}).get("name", "Unknown")] += 1
for ph, n in phases.most_common():
print(f"{ph}: {n}")