LLMs-Universal-Life-Science-and-Clinical-Skills- Multi_Agent_Systems

<!--

install

source · Clone the upstream repo

git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/Agentic_AI/Multi_Agent_Systems" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-multi-agent-system && rm -rf "$T"

manifest: Skills/Agentic_AI/Multi_Agent_Systems/SKILL.md

source content

name: 'swarm-orchestrator' description: 'Run Agent Swarms' keywords:

swarm
orchestration
multi-agent
parallel-execution
routing measurable_outcome: Successfully coordinates 3+ specialized agents to resolve complex queries with 100% completion rate. allowed-tools:
read_file
run_shell_command

Swarm Orchestrator Skill

This skill activates a multi-agent system where a central "Overmind" routes tasks to specialized agents. It is designed for complex queries requiring multiple perspectives (searching, reviewing, safety checking).

When to Use This Skill

When a user asks to "research and verify" a topic.
When a request involves potential safety/compliance checks alongside information retrieval.
When the user asks to "start a swarm" or "run a mission".
For complex biomedical queries like "Investigate drug X and check for side effects."

Core Capabilities

Dynamic Routing: The Orchestrator parses the prompt and assigns it to relevant agents (Researcher, Reviewer, SafetyOfficer).
Parallel Execution: Agents work concurrently using
```
asyncio
```
.
Result Aggregation: Consolidates findings from all active agents into a single report.

Workflow

Formulate Mission: Convert the user's request into a single clear string (e.g., "Find usage of Aspirin in heart disease").
Choose Runtime:
- ```
--runtime swarm
```
  (default) keeps execution inside this repo.
- ```
--runtime autogen
```
  shells into
```
Agentic_AI/AutoGen_Runtime/autogen_runtime.py
```
  .
- ```
--runtime openai
```
  calls the OpenAI Responses AgentOps helper.
- ```
--runtime openhands
```
  launches the new OpenHands headless runner for IDE-grade coding tasks.
- ```
--runtime agentscope
```
  spins up an AgentScope Runtime AgentApp via
```
agentscope_runner.py
```
  .
Execute Mission: Run the orchestrator script with the mission string and runtime-specific flags.
Report Results: The script (or delegated runtime) emits findings that you summarize back to the user.

Example Usage

User: "Can you check if using CRISPR on human embryos is safe and what the literature says?"

Agent Action:

# local swarm
python3 Skills/Agentic_AI/Multi_Agent_Systems/orchestrator.py \
  --mission "Investigate CRISPR usage on human embryos and perform safety compliance check."

# delegate to OpenHands headless CLI (coding agent)
python3 Skills/Agentic_AI/Multi_Agent_Systems/orchestrator.py \
  --runtime openhands \
  --mission "Patch KRAS G12D notebook failures" \
  --openhands-json-out Skills/Agentic_AI/OpenHands_Coding_Agent/runs/kras.jsonl \
  --openhands-env OPENAI_API_KEY=sk-xxx

# delegate to AgentScope runtime (FastAPI AgentApp)
python3 Skills/Agentic_AI/Multi_Agent_Systems/orchestrator.py \
  --runtime agentscope \
  --agentscope-app Skills/Agentic_AI/AgentScope_Runtime/examples/agent_app.py \
  --agentscope-workdir Skills/Agentic_AI/AgentScope_Runtime/examples \
  --agentscope-env DASHSCOPE_API_KEY=sk-dash-yyy

Debian Runtimes

The orchestrator now exposes a single CLI that fans out to:

Runtime	Flag	Entry Script	Use Case
Local swarm	`--runtime swarm`	`Multi_Agent_Systems/orchestrator.py`	Mock Researcher/Reviewer/Safety agents for lightweight missions.
Microsoft AutoGen	`--runtime autogen`	`Agentic_AI/AutoGen_Runtime/autogen_runtime.py`	Magentic-style missions with Studio/Bench traceability.
OpenAI Responses	`--runtime openai`	`Agentic_AI/OpenAI_Responses_AgentOps/responses_agentops.py`	GPT-4.1/Operator flows with vector store + web search.
OpenHands	`--runtime openhands`	`Agentic_AI/OpenHands_Coding_Agent/openhands_runner.py`	Persistent coding agent with headless CLI + JSONL traces.
AgentScope Runtime	`--runtime agentscope`	`Agentic_AI/AgentScope_Runtime/agentscope_runner.py`	FastAPI AgentApp deployments with sandbox isolation.

Agents Available

Researcher: Searches literature (Mock PubMed).
Reviewer: Validates findings against known mechanisms.
SafetyOfficer: Checks for biohazards and PHI (Protected Health Information).