med-researcher-r1-guide

med-researcher-r1-guide is a skill for Claude Code, Codex from wentorai/research-plugins. It costs 16 tokens per session (1,136 once invoked), scanned A, original, MIT.

A medical research agent that breaks complex clinical questions into smaller questions, searches medical databases, extracts study evidence, and produces a supported summary.

In plain words
What is it for?
Use it for clinical evidence reviews, treatment or drug-interaction research, and systematic reviews using sources such as PubMed, clinical-trial records, and Cochrane reviews.
Why use it?
It helps organize difficult medical literature searches and makes study populations, treatments, outcomes, and evidence quality easier to compare.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it for clinical evidence reviews, treatment or drug-interaction research, and systematic reviews using sources such as PubMed, clinical-trial records, and Cochrane reviews.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wentorai/research-plugins/med-researcher-r1-guide
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add wentorai/research-plugins --skill med-researcher-r1-guide
Clone the repo
git clone --depth 1 https://github.com/wentorai/research-plugins

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for med-researcher-r1-guide

README.md
[![agentmods](https://agentmods.dev/badge/skills/wentorai/research-plugins/med-researcher-r1-guide/github.svg)](https://agentmods.dev/skills/wentorai/research-plugins/med-researcher-r1-guide)
Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/wentorai/research-plugins/med-researcher-r1-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/med-researcher-r1-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,136 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00016 $0.01136
Opus 5 $0.00008 $0.00568
Sonnet 5 $0.00003 $0.00227
Haiku 4.5 $0.00002 $0.00114

Measured 8d ago against content hash e1db1085f8bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

med-researcher-r1-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/domains/biomedical/med-researcher-r1-guide/SKILL.md · 147 lines

How it starts

The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MedResearcher-R1 Guide

Overview

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.

Architecture

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

Usage

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)}")

Reasoning Chain

# 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

Read the full file on GitHub · 147 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 147 lines · 16 tokens per session scan A e1db1085f8bc

Subscribe to this mod's changes

med-researcher-r1-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 1,136 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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