LLMs-Universal-Life-Science-and-Clinical-Skills- google-adk-agents

Build, evaluate, and deploy agents with Google's Agent Development Kit (ADK). Use when you want code-first multi-agent systems, workflow agents, MCP tools, or Google-supported agent deployment paths.

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 "$T/Skills/Agentic_AI/Google_ADK_Agents" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-google-adk-agents && rm -rf "$T"

manifest: Skills/Agentic_AI/Google_ADK_Agents/SKILL.md

source content

Google ADK Agents

Use this skill when you want Google's agent framework primitives instead of retrofitting agent behavior onto a generic app framework.

Workflow

Pick the runtime language first: Python is the default, but ADK also supports TypeScript, Go, and Java.
Decide whether the problem is best modeled as an LLM agent, a workflow agent, or a multi-agent system.
Choose the tool surface: function tools, MCP tools, OpenAPI tools, or native integrations.
Define evaluation and runtime strategy before deployment, not after.
Validate locally, then promote to managed runtime or production infra only after passing a small eval set.

Guardrails

Keep agent boundaries explicit; avoid one giant catch-all agent.
Prefer documented ADK primitives over undocumented framework internals.
Record the model/provider abstraction used so the workflow stays reproducible.
Treat third-party integrations as optional until auth, quotas, and failure modes are documented.

Output Requirements

State the ADK runtime and language.
State the agent topology (single, workflow, or multi-agent).
State the evaluation plan and one production risk.