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-- name: Collision-Zone Thinking description: Force unrelated concepts together to discover emergent properties - "What if we treated X like Y?" when_...
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 | collision-zone-thinking |
| description | -- name: Collision-Zone Thinking description: Force unrelated concepts together to discover emergent properties - "What if we treated X like Y?" when_... |
Revolutionary insights come from forcing unrelated concepts to collide. Treat X like Y and see what emerges.
Core principle: Deliberate metaphor-mixing generates novel solutions.
| Stuck On | Try Treating As | Might Discover |
|---|---|---|
| Code organization | DNA/genetics | Mutation testing, evolutionary algorithms |
| Service architecture | Lego bricks | Composable microservices, plug-and-play |
| Data management | Water flow | Streaming, data lakes, flow-based systems |
| Request handling | Postal mail | Message queues, async processing |
| Error handling | Circuit breakers | Fault isolation, graceful degradation |
Problem: Complex distributed system with cascading failures
Collision: "What if we treated services like electrical circuits?"
Emergent properties:
Where it works: Preventing cascade failures Where it breaks: Circuits don't have retry logic Insight gained: Failure isolation patterns from electrical engineering
Analyze agent session transcripts to find looping behavior, missing capabilities, or token waste, and leverage framework-native concepts and skill-creator patterns to propose optimizations.
Build multi-agent systems using Google ADK with A2A protocol, deployed on Agent Engine. Use when creating agents that communicate via A2A, building multi-tier agent hierarchies, connecting ADK agents with RemoteA2aAgent, exposing agents with to_a2a(), or deploying agent stacks to Vertex AI Agent Engine. Covers leaf agents with tools, functional agents that delegate, orchestrators that route, local testing with uvicorn, and phased cloud deployment.
Build AI agents using Google's Agent Development Kit (ADK). Use when creating LLM agents with tools, building workflow agents (Sequential, Parallel, Loop), composing multi-agent systems, or developing custom agents. Covers agent creation patterns, function tools, agent configuration, session management, and running agents locally with CLI or web interface.
Deploy and manage AI agents on Vertex AI Agent Engine. Use when deploying ADK agents to production, configuring Agent Engine runtime, managing deployed agents, setting up sessions and memory, or integrating with A2A protocol. Covers deployment from agent objects and source files, environment configuration, scaling, sessions, memory bank, and agent management operations.
Sprint-based agile development with parallel agent execution. Use when the user wants to run an agile sprint, plan sprint work, conduct standups, run retrospectives, manage a backlog, estimate story points, track velocity, or coordinate parallel development across multiple agents acting as Scrum team roles (Scrum Master, Product Owner, Tech Lead, Frontend Dev, Backend Dev, QA Engineer). Also use when the user mentions sprints, user stories, acceptance criteria, definition of done, kanban, or SAFe. Triggers on: 'run a sprint', 'sprint planning', 'standup', 'retrospective', 'backlog grooming', 'agile workflow', 'scrum team'.
Clarify requirements before implementing. Use when serious doubts araise.
