brycewang-stanford
brycewang-stanford/Auto-Empirical-Research-Skills
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。
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npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill med-researcher-r1-guide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 | med-researcher-r1-guide |
| description | Medical deep research agent with reasoning chain analysis |
| metadata | {"openclaw":{"emoji":"🩺","category":"domains","subcategory":"biomedical","keywords":["medical research","deep research","clinical reasoning","PubMed","medical agent","evidence-based"],"source":"https://github.com/AQ-MedAI/MedResearcher-R1"}} |
MedResearcher-R1 is a medical deep research agent that combines clinical reasoning chains with iterative literature search to answer complex medical questions. Unlike general research agents, it is specialized for medical evidence — understanding clinical trial designs, PICO frameworks, evidence hierarchies, and medical terminology. Uses reasoning chain analysis (R1) to decompose clinical questions and systematically gather evidence.
Clinical Question
↓
R1 Reasoning Chain (decompose into sub-questions)
↓
Medical Search Agent
├── PubMed (MeSH terms)
├── ClinicalTrials.gov
├── Cochrane Library
└── WHO ICTRP
↓
Evidence Extraction Agent
├── PICO extraction
├── Study design classification
├── Outcome extraction
└── Risk of bias assessment
↓
Synthesis Agent (evidence grading)
↓
Clinical Answer + Evidence Report
from med_researcher_r1 import MedResearcherR1
researcher = MedResearcherR1(
llm_provider="anthropic",
search_backends=["pubmed", "clinical_trials", "cochrane"],
)
# Complex clinical question
result = researcher.research(
question="In patients with treatment-resistant depression, "
"how does psilocybin-assisted therapy compare to "
"esketamine in terms of remission rates and "
"long-term outcomes?",
evidence_level="systematic", # systematic, rapid, scoping
max_papers=50,
)
print(result.summary)
print(f"\nEvidence quality: {result.evidence_grade}")
print(f"Papers analyzed: {len(result.papers)}")
# Inspect the R1 reasoning chain
for step in result.reasoning_chain:
print(f"\nStep {step.number}: {step.type}")
print(f" Question: {step.question}")
print(f" Strategy: {step.search_strategy}")
print(f" Findings: {step.key_finding}")
print(f" Next: {step.next_action}")
# Example chain:
# Step 1: DECOMPOSE — Split into psilocybin efficacy,
# esketamine efficacy, head-to-head comparisons
# Step 2: SEARCH — PubMed: psilocybin depression RCT
# Step 3: EXTRACT — 3 RCTs found, extract PICO + outcomes
# Step 4: SEARCH — PubMed: esketamine depression outcomes
# Step 5: SYNTHESIZE — Compare evidence, note no direct
# head-to-head trials exist
# Step 6: CONCLUDE — Indirect comparison with caveats
# GRADE methodology for evidence quality
for paper in result.papers[:5]:
print(f"\n{paper.title} ({paper.year})")
print(f" Design: {paper.study_design}")
print(f" Sample: {paper.sample_size}")
print(f" Grade: {paper.evidence_grade}")
print(f" Risk of bias: {paper.risk_of_bias}")
# Aggregate evidence
print(f"\nOverall certainty: {result.certainty}")
# HIGH / MODERATE / LOW / VERY LOW
print(f"Recommendation: {result.recommendation}")
researcher = MedResearcherR1(
search_config={
"pubmed": {
"use_mesh": True,
"date_range": "2019/01/01:2025/12/31",
"article_types": [
"Randomized Controlled Trial",
"Meta-Analysis",
"Systematic Review",
],
},
"clinical_trials": {
"status": ["Completed", "Active, not recruiting"],
"phase": ["Phase 3", "Phase 4"],
},
},
reasoning_config={
"max_chain_length": 10,
"reflection_enabled": True,
"uncertainty_explicit": True,
},
)