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デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill med-researcher-guideコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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.
SOC 職業分類に基づく
SKILL.md を表示中
| name | med-researcher-guide |
| description | Multi-agent system for biomedical literature review and synthesis |
| metadata | {"openclaw":{"emoji":"🏥","category":"domains","subcategory":"biomedical","keywords":["medical research","biomedical agent","clinical literature","PubMed agent","medical AI","evidence synthesis"],"source":"wentor-research-plugins"}} |
Med-Researcher is a multi-agent system designed specifically for biomedical literature review. It orchestrates specialized agents for searching PubMed and other medical databases, extracting structured evidence from clinical papers, and synthesizing findings into evidence-graded summaries. Particularly useful for clinical evidence reviews, drug interaction research, and systematic reviews in medicine.
Query → Planning Agent (decomposes clinical question)
↓
Search Agent (PubMed, PMC, clinical trials)
↓
Extraction Agent (PICO, outcomes, evidence grade)
↓
Synthesis Agent (evidence summary, contradictions)
↓
Report Agent (structured review output)
| Agent | Role |
|---|---|
| Planner | Converts clinical question to PICO format, generates sub-queries |
| Searcher | Queries PubMed, PMC, ClinicalTrials.gov |
| Extractor | Extracts structured data: population, intervention, outcomes |
| Synthesizer | Grades evidence, identifies consensus and contradictions |
| Reporter | Generates formatted review with citations |
from med_researcher import MedResearcher
researcher = MedResearcher(
llm_provider="anthropic",
search_backends=["pubmed", "pmc", "clinical_trials"],
)
# Clinical question
result = researcher.review(
question="What is the comparative efficacy of SGLT2 inhibitors "
"versus GLP-1 receptor agonists for cardiovascular "
"outcomes in type 2 diabetes?",
max_papers=50,
evidence_grading=True,
)
print(result.summary)
print(f"Papers analyzed: {len(result.papers)}")
print(f"Evidence grade: {result.overall_grade}")
# Automatic PICO extraction from clinical question
pico = researcher.extract_pico(
"Does metformin reduce cancer incidence in diabetic patients?"
)
# P: patients with diabetes
# I: metformin treatment
# C: no metformin / other antidiabetics
# O: cancer incidence
# Search with PICO components
result = researcher.review_pico(
population="type 2 diabetes patients",
intervention="metformin",
comparison="placebo or other antidiabetics",
outcome="cancer incidence",
)
# Evidence levels following GRADE methodology
for paper in result.papers:
print(f"{paper.title}")
print(f" Study type: {paper.study_type}") # RCT, cohort, case-control
print(f" Evidence level: {paper.evidence_level}") # High/Moderate/Low/Very Low
print(f" Risk of bias: {paper.bias_risk}")
print(f" Sample size: {paper.sample_size}")
# Aggregate evidence summary
print(f"\nOverall certainty: {result.certainty}")
print(f"Recommendation strength: {result.recommendation}")
researcher = MedResearcher(
search_config={
"pubmed": {
"max_results": 100,
"date_range": ("2020-01-01", "2025-12-31"),
"article_types": ["Clinical Trial", "Meta-Analysis",
"Randomized Controlled Trial"],
},
"clinical_trials": {
"status": ["Completed", "Active"],
"phase": ["Phase 3", "Phase 4"],
},
},
extraction_config={
"fields": ["population", "intervention", "comparator",
"primary_outcome", "secondary_outcomes",
"adverse_events", "sample_size", "follow_up"],
},
)
# Structured evidence table
result.export_evidence_table("evidence_table.csv")
# PRISMA flow diagram data
prisma = result.prisma_flow()
print(f"Identified: {prisma['identified']}")
print(f"Screened: {prisma['screened']}")
print(f"Included: {prisma['included']}")
# Bibliography
result.export_bibtex("references.bib")
# Full report
result.export_report("review.md", format="markdown")