菜单
ADK prompt engineering expert covering agent instructions, few-shot examples, system prompts, and cost optimization strategies. Use when designing agent personalities, optimizing prompt tokens, or implementing few-shot learning patterns.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
ADK Agent-to-Agent (A2A) protocol expert covering remote agent communication, agent cards, HTTP-based delegation, and distributed agent systems. Use when building multi-service agent architectures, implementing remote agent calls, or designing agent networks.
ADK memory + embeddings across databases. Generate multimodal embeddings with gemini-embedding-2, persist agent sessions/memory to Cloud SQL, Postgres, Firestore, or SQLite, and store/retrieve vectors for RAG (pgvector, Firestore vector). Use when choosing a session/memory backend, wiring a database SessionService or MemoryService, generating embeddings, or building a vector store for retrieval on an ADK agent.
Migrate ADK agents from 1.x to 2.3. Covers breaking API changes (graph Workflow vs GraphAgent, McpToolset, BuiltInCodeExecutor, ctx.resume_inputs, agent modes), renamed imports, deploy CLI changes, model/thinking/embedding updates, and session/state incompatibility. Use when upgrading an ADK 1.x codebase to 2.3, auditing code for deprecated 1.x APIs, or resolving 2.3 import/attribute errors.
ADK model routing, thinking/effort levels, and fallback methodology for production backends. Covers the current Gemini 3.x model catalog (gemini-3.5-flash, gemini-3.1-flash-lite, gemini-3.1-pro-preview, gemini-3.1-flash-live-preview, gemini-embedding-2), thinking_level effort tiers (minimal/low/medium/high), deprecation of thinking_budget and sampling params, and primary→fallback chains. Use when selecting a model, tuning thinking/effort, routing by task complexity/cost, or designing model fallback for an ADK agent.
Central hub for building, testing, and iterating on ADK agents. Trigger this skill when the user wants to create a new agent, configure modes (task, single-turn), or build graph-based workflows.
DAG-based ADK workflow in Go using graph pattern. Use for Go backends needing multi-step agent pipelines.
| name | adk-prompts |
| description | ADK prompt engineering expert covering agent instructions, few-shot examples, system prompts, and cost optimization strategies. Use when designing agent personalities, optimizing prompt tokens, or implementing few-shot learning patterns. |
You are a senior AI engineer specializing in prompt engineering for Google ADK agents.
../../docs/python-adk-2.md for current ADK 2.x model and output-schema constraints.../adk-agents/references/config.md for instruction patterns../adk-agents/references/models.md for model-specific prompting| Strategy | Potential Savings |
|---|---|
| Model Selection | 50-70% |
| Response Caching | 30-50% |
| Batch Processing | 15-25% |
| Temperature Tuning | 10-15% |
from google.adk import Agent
# Agent with structured instructions
agent = Agent(
name="travel_planner",
model="gemini-3.5-flash",
instruction="""You are a helpful assistant specializing in travel planning.
## Capabilities
- Search for flights and hotels
- Create detailed itineraries
- Provide local recommendations
## Guidelines
- Always confirm dates and destinations
- Suggest budget-friendly options first
- Include safety information for international travel
""",
tools=[search_flights, book_hotel, get_recommendations]
)