Denario
davila7/claude-code-templates
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication.
Official agent skill
Biomarker Multi Agent Discovery
by aws-samples in aws-samples/amazon-bedrock-agents-healthcare-lifesciences
A skill your agent uses when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and…
Install Biomarker Multi Agent Discovery
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences biomarker-multi-agent-discovery --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/biomarker-multi-agent-discovery .claude/skills/biomarker-multi-agent-discovery && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "biomarker-multi-agent-discovery" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/skills/biomarker-multi-agent-discovery into .claude/skills/biomarker-multi-agent-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biomarker-multi-agent-discovery", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/skills/biomarker-multi-agent-discoveryType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences biomarker-multi-agent-discovery --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/biomarker-multi-agent-discovery .agents/skills/biomarker-multi-agent-discovery && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "biomarker-multi-agent-discovery" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/skills/biomarker-multi-agent-discovery into .agents/skills/biomarker-multi-agent-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biomarker-multi-agent-discovery", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences biomarker-multi-agent-discovery --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/biomarker-multi-agent-discovery .cursor/skills/biomarker-multi-agent-discovery && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "biomarker-multi-agent-discovery" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/skills/biomarker-multi-agent-discovery into .cursor/skills/biomarker-multi-agent-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biomarker-multi-agent-discovery", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git --path skills/biomarker-multi-agent-discovery--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences biomarker-multi-agent-discovery --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/biomarker-multi-agent-discovery .gemini/skills/biomarker-multi-agent-discovery && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "biomarker-multi-agent-discovery" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/skills/biomarker-multi-agent-discovery into .gemini/skills/biomarker-multi-agent-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biomarker-multi-agent-discovery", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences biomarker-multi-agent-discoveryInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/biomarker-multi-agent-discovery .github/skills/biomarker-multi-agent-discovery && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "biomarker-multi-agent-discovery" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/skills/biomarker-multi-agent-discovery into .github/skills/biomarker-multi-agent-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biomarker-multi-agent-discovery", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences biomarker-multi-agent-discovery --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/biomarker-multi-agent-discovery .opencode/skills/biomarker-multi-agent-discovery && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "biomarker-multi-agent-discovery" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/skills/biomarker-multi-agent-discovery into .opencode/skills/biomarker-multi-agent-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "biomarker-multi-agent-discovery", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
biomarker-multi-agent-discovery- GitHub stars
- 274
- Token cost
- ~1.4k tokens
- SKILL.md length
- 453 words
- Files
- 2 (incl. references)
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- MIT-0
At a glance
A skill your agent uses when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and…
- Works in 5 steps: Classify the user query → Execute database queries first → Feed results to downstream agents → …
- Orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries
- SKILL.md covers When to use this skill, Architecture: Agents-as-Tools…, Orchestration Workflow and Tool Dispatch Reference, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Biomarker Multi Agent Discovery is an agent skill from aws-samples/amazon-bedrock-agents-healthcare-lifesciences, published by the product's own GitHub organization. Use when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and clinical evidence synthesis to produce ranked biomarker panels.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files.
It sits in Agent Workflows, covering Multi-agent orchestration and Literature review. The licence is MIT-0.
When your agent uses it
- Orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries
- Pathway analysis
- Literature review
- Statistical modeling
Example prompts
- “/biomarker-multi-agent-discovery”
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 9960565. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Biomarker Multi Agent Discovery loads about 1.4k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 453 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from aws-samples/amazon-bedrock-agents-healthcare-lifesciences at commit 9960565, republished under its MIT-0 licence (© aws-samples). 453 words, ~1,383 tokens.
Download SKILL.mdSave it as
.claude/skills/biomarker-multi-agent-discovery/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- biomarker-multi-agent-discovery
- description
- Use when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and clinical evidence synthesis to produce ranked biomarker panels.
Biomarker Multi-Agent Discovery
When to use this skill
- Complex biomarker discovery requiring multiple analysis modalities
- Coordinating database queries with statistical survival analysis
- Synthesizing findings from literature, pathways, and clinical data into a biomarker panel
- Questions that span multiple sub-domains (e.g., "find best biomarker for survival in chemo patients and show evidence")
Architecture: Agents-as-Tools Pattern
The orchestrator dispatches to specialized sub-agents, each wrapped as a tool:
Orchestrator (Supervisor)
|-- biomarker_database_analyst_agent -> SQL queries on clinical genomic data
|-- clinical_evidence_research_agent -> PubMed + Knowledge Base search
|-- statistician_agent -> Survival regression, Kaplan-Meier plots
|-- medical_imaging_agent -> Radiomics biomarker extractionCross-agent data sharing uses AgentCore Memory: the database agent stores query results, and downstream agents (statistician) retrieve them automatically.
Orchestration Workflow
Step 1: Classify the user query
Map the question to required sub-agents:
| Query type | Agents needed | Sequence |
|---|---|---|
| Demographics / counts | Database analyst only | Single call |
| Literature evidence | Clinical evidence researcher only | Single call |
| Statistical analysis (p-values, survival) | Database analyst -> Statistician | Sequential |
| Imaging biomarkers | Database analyst -> Medical imaging | Sequential |
| Comprehensive discovery | All agents | Multi-step |
| Pathway interpretation | Database analyst -> Literature | Sequential |
Step 2: Execute database queries first
For any analysis requiring patient data:
- Call
biomarker_database_analyst_agentwith the data retrieval question - Results are automatically stored in shared memory
- Include required columns: survival_status, survival_duration, biomarker expression values
Example dispatch:
Query: "What are the top 5 biomarkers with overall survival for chemo patients?"
-> Database agent: "Query all records including survival status, survival duration in years, and gene expression values for patients where chemotherapy = 'Yes'"Step 3: Feed results to downstream agents
For statistical analysis:
-> Statistician agent: "Fit a survival regression model on the query results"The statistician retrieves data from memory automatically. No S3 path needed.
For visualization:
-> Statistician agent: "Generate a bar chart of the top 5 biomarkers by p-value"
-> Statistician agent: "Plot Kaplan-Meier curve for GDF15 with threshold 10"For literature validation:
-> Clinical evidence researcher: "Search PubMed for evidence on GDF15 as a biomarker in NSCLC"Step 4: Synthesize findings into ranked biomarker panel
Combine outputs from all agents into a consolidated report:
Biomarker Panel Report
=====================
1. [Gene] - p-value: X, HR: Y
- Pathway: [from pathway analysis]
- Literature: [N publications supporting]
- Clinical significance: [interpretation]
2. [Gene] - p-value: X, HR: Y
...Ranking criteria (in priority order):
- Statistical significance (lowest p-value from Cox regression)
- Clinical significance (hazard ratio magnitude)
- Pathway relevance (membership in disease-associated pathways)
- Literature support (number of supporting publications)
- Biological plausibility (protein function matches disease mechanism)
Show full SKILL.md (186 more words)Show less
Step 5: Generate actionable recommendations
For each top biomarker, provide:
- Measurement method (IHC, RNA-seq, blood test)
- Patient stratification threshold (expression cutoff)
- Potential clinical utility (prognostic vs. predictive vs. diagnostic)
- Next validation steps (cohort size, assay development)
Tool Dispatch Reference
| Agent | Tools | Input | Output |
|---|---|---|---|
| Database Analyst | get_schema, query_redshift, refine_sql | Data question | Query results (auto-stored in memory) |
| Clinical Evidence | query_pubmed, retrieve | Evidence question | Literature summary with citations |
| Statistician | run_code, plot_kaplan_meier, fit_survival_regression | Analysis request | Regression, charts (S3 paths), p-values |
| Medical Imaging | compute_imaging_biomarker, analyze_imaging_biomarker | Patient IDs | Radiomics features (sphericity, elongation) |
Example Multi-Step Sequences
"Find best biomarker for survival in chemo patients with visualization":
- Database agent -> Statistician (Cox regression) -> Statistician (Kaplan-Meier) -> Literature -> Synthesize
"Compare imaging biomarkers for patients with lowest GDF15":
- Database agent (find patients) -> Medical imaging (compute + visualize) -> Synthesize
Conventions
- Always explain the multi-step plan to the user before executing
- Present results from each agent separately, then provide consolidated summary
- Include S3 paths for any generated charts or images
- When agents fail, explain which step failed and what alternatives exist
- Medical/statistical concepts must be explained in accessible language
- Memory events expire after 3 days -- no manual cleanup needed
© aws-samples, MIT-0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file (references) in skills/biomarker-multi-agent-discovery of aws-samples/amazon-bedrock-agents-healthcare-lifesciences.
- SKILL.md
- references/.gitkeep
Open the folder on GitHubat commit 9960565
Compare with similar skills
Biomarker Multi Agent Discovery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Biomarker Multi Agent Discovery this skillaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | — | ~1.4k | Automated safety check: Pass | MIT-0 | |
| Denariodavila7/claude-code-templates | 32k | 8 repos | ~1.5k | Automated safety check: Notes | MIT | |
| Literature Survey Generatorbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~2.7k | Automated safety check: Notes | Custom licence | |
| Med Researcher Guidewentorai/research-plugins | 298 | 1 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Discoverbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~2.2k | Automated safety check: Pass | Custom licence | |
| Research Town Guidewentorai/research-plugins | 298 | 1 repos | ~2.7k | Automated safety check: Pass | MIT |
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Literature Survey Generator
brycewang-stanford/Auto-Empirical-Research-Skills
Generate a complete academic literature survey from scratch using multi-agent orchestration.
Med Researcher Guide
wentorai/research-plugins
Multi-agent system for biomedical literature review and synthesis
Discover
brycewang-stanford/Auto-Empirical-Research-Skills
Discovery phase combining research interviews, literature search, data discovery, and ideation.
Research Town Guide
wentorai/research-plugins
Simulate human research communities with multi-agent AI collaboration
Paperclip
lamm-mit/scienceclaw
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
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Questions about Biomarker Multi Agent Discovery
What does Biomarker Multi Agent Discovery do?
A skill your agent uses when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and…. Biomarker Multi Agent Discovery is an agent skill from aws-samples/amazon-bedrock-agents-healthcare-lifesciences, published by the product's own GitHub organization. Use when orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries, pathway analysis, literature review, statistical modeling, and clinical evidence synthesis to produce ranked biomarker panels.
When should I use Biomarker Multi Agent Discovery?
Biomarker Multi Agent Discovery fits situations like: orchestrating a multi-agent biomarker discovery workflow that requires coordinating database queries; pathway analysis; literature review; statistical modeling.
How do I install Biomarker Multi Agent Discovery in Claude Code?
Run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a claude-code`. Or copy the skill folder (skills/biomarker-multi-agent-discovery in aws-samples/amazon-bedrock-agents-healthcare-lifesciences) into .claude/skills/biomarker-multi-agent-discovery in your project. Claude Code loads it when a task matches its description.
How do I install Biomarker Multi Agent Discovery in Codex?
Run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a codex`. Or copy the skill folder (skills/biomarker-multi-agent-discovery in aws-samples/amazon-bedrock-agents-healthcare-lifesciences) into .agents/skills/biomarker-multi-agent-discovery in your project. Codex loads it when a task matches its description.
Can I use Biomarker Multi Agent Discovery in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/biomarker-multi-agent-discovery, .gemini/skills/biomarker-multi-agent-discovery, .github/skills/biomarker-multi-agent-discovery and .opencode/skills/biomarker-multi-agent-discovery in your project.
What does Biomarker Multi Agent Discovery need to run?
SKILL.md names no scripts, command-line tools or credentials: Biomarker Multi Agent Discovery is instructions for the agent only.
Does Biomarker Multi Agent Discovery access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Biomarker Multi Agent Discovery safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Biomarker Multi Agent Discovery use?
Biomarker Multi Agent Discovery is published under the MIT-0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Biomarker Multi Agent Discovery use?
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Biomarker Multi Agent Discovery?
Skills that share tags, products or a category with Biomarker Multi Agent Discovery: Denario (davila7/claude-code-templates, 32k stars), Literature Survey Generator (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Med Researcher Guide (wentorai/research-plugins, 298 stars) and Discover (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Biomarker Multi Agent Discovery?
aws-samples (a GitHub organization, an official publisher) maintains it in aws-samples/amazon-bedrock-agents-healthcare-lifesciences, which has 274 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 1, 2026.
Source: aws-samples/amazon-bedrock-agents-healthcare-lifesciences on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.