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-- name: Condition-Based Waiting description: Replace arbitrary timeouts with condition polling for reliable async tests when_to_use: when tests have ...
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | condition-based-waiting |
| description | -- name: Condition-Based Waiting description: Replace arbitrary timeouts with condition polling for reliable async tests when_to_use: when tests have ... |
Flaky tests often guess at timing with arbitrary delays. This creates race conditions where tests pass on fast machines but fail under load or in CI.
Core principle: Wait for the actual condition you care about, not a guess about how long it takes.
For advanced configurations, detailed examples, and more information, see references/details.md.
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.
