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Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D…
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.
$ npx -y skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill research-deep-literature-review --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/research-deep-literature-reviewContext 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.
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.
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"
| 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 |
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"
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
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
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
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
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.
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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