メニュー
DAG-based ADK 2.x Workflow template using START and edge tuples. Python. Use for multi-step pipelines with branching logic.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | graph-workflow-python |
| description | DAG-based ADK 2.x Workflow template using START and edge tuples. Python. Use for multi-step pipelines with branching logic. |
DAG-based workflow using ADK 2.x Workflow — nodes represent agents or functions, edges define transitions, route maps gate branching.
| File | Purpose |
|---|---|
workflow.py | Main workflow: agents, tools, graph assembly |
agents.py | Agent definitions (validator, processor, etc.) |
tools.py | Tool definitions with Pydantic schemas |
requirements.txt | Dependencies |
Workflow — workflow containerNode(id, agent) — workflow stepEdge(source, target, condition) — transitionCondition(lambda state: bool) — branching gateADK 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.