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Research
Skill

/research-deep-literature-review

Use when a developer needs to conduct systematic literature searches, find research papers, extract content from publications, or build evidence summaries from biomedical databases and web sources via the Biomni Research Tools MCP server.

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From plugin
amazon-bedrock-agents-healthcare-lifesciences
27412 skills9 MCP
Install
$ npx -y skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill research-deep-literature-review --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/research-deep-literature-review

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when a developer needs to conduct systematic literature searches, find research papers, extract content from publications, or build evidence summaries from biomedical databases and web sources via the Biomni Research Tools MCP server.

SKILL.md

research-deep-literature-review.SKILL.md
name: research-deep-literature-review
description: Use when a developer needs to conduct systematic literature searches, find research papers, extract content from publications, or build evidence summaries from biomedical databases and web sources via the Biomni Research Tools MCP server.

Deep Literature Review

When to use this skill

  • Developer asks "find papers about X" or "what's the latest research on Y?"
  • Developer needs to build an evidence summary from multiple publications
  • Developer wants to extract specific data from a paper or PDF
  • Developer needs to search across biomedical literature and databases
  • Developer asks about systematic literature review workflows

MCP Server: `biomni-research`

This server provides tools for biomedical database queries including literature-related functionality. Tools are discovered automatically via the MCP protocol.

Quick setup (after deployment):

cd mcp-servers/agentcore-gateway/biomni-research-tools
source get-token.sh
claude mcp add --transport http biomni-research "$BIOMNI_GATEWAY_URL" --header "Authorization: Bearer $BIOMNI_MCP_TOKEN"

Search Strategy

Choosing the Right Approach

| Research Type | Query approach (via biomni-research) | |---------------|--------------------------------------| | Clinical evidence (trials, outcomes) | Query ClinicalTrials database | | Variant significance | Query ClinVar + gnomAD | | Drug safety / regulatory | Query OpenFDA | | Protein biology | Query UniProt + InterPro | | Cancer genomics | Query cBioPortal + GEO | | Pathway mechanisms | Query Reactome + Open Targets |

Effective Query Construction

All tools accept natural language prompts. Be specific:

"Find pathogenic BRCA1 variants associated with breast cancer"
"EGFR T790M resistance mutation frequency in Asian populations"
"CDK4/6 inhibitors clinical trials phase 3 breast cancer"
"TP53 expression in hepatocellular carcinoma datasets"

Literature Review Workflows

Workflow 1: Rapid Evidence Summary

Goal: Quickly assess the state of evidence on a topic.

1. **Broad database search**: Query relevant databases for the topic 2. **Cross-reference**: Check multiple databases for consistency 3. **Extract details**: Use PDF extraction tool for full-text papers 4. **Synthesize**: Combine database evidence with extracted findings

Workflow 2: Competitive Intelligence (Drug Development)

1. **Find clinical trials**: Query ClinicalTrials for "drug/target phase 2 phase 3 results" 2. **Check target biology**: Query Open Targets + UniProt for target validation 3. **Regulatory context**: Query OpenFDA for drug safety signals 4. **Extract details**: Use PDF extraction for advisory briefing documents

Workflow 3: Systematic Literature Search

1. **Define PICO**: Population, Intervention, Comparison, Outcome 2. **Multi-database search**: Query ClinVar + ClinicalTrials + Open Targets 3. **Cross-reference**: Compare findings across databases for consistency 4. **Validate**: Check population frequencies (gnomAD) for genomic findings

Workflow 4: Technology Landscape

1. **Foundational biology**: Query UniProt + Reactome for mechanism 2. **Clinical translation**: Query ClinicalTrials for applications 3. **Cancer data**: Query cBioPortal for mutation/expression profiles 4. **Regulatory status**: Query OpenFDA for approved therapies

Tips

  • **Be specific with organisms**: Always specify "human" to avoid cross-species results
  • **Use standard identifiers**: Gene symbols (BRCA1), UniProt IDs (P38398), RS numbers (rs80357906)
  • **Start broad, narrow down**: First query identifies the entity → follow-up gets specific data
  • **Cross-validate**: If a finding appears across multiple database tools, it's more reliable
  • **Extract strategically**: Only use PDF extraction when you need methods details or specific data points

Combining with Other Skills

Literature review is most powerful when combined with other database queries:

1. **Find variant**: Query ClinVar for "EGFR T790M pathogenic" 2. **Check frequency**: Query gnomAD for "EGFR T790M population frequency" 3. **Check pathways**: Query Reactome for "EGFR signaling cascade" 4. **Find trials**: Query ClinicalTrials for "EGFR T790M osimertinib"

This multi-database feedback loop produces comprehensive evidence packages.

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Ships withamazon-bedrock-agents-healthcare-lifesciences

A library of healthcare and life sciences domain capabilities — reference agents with best practices, reusable skills, and MCP-based tools — serving builders in their IDEs, researchers in Amazon Quick, and developers deploying to AWS.

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