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.
Autonomous research loop inspired by karpathy/autoresearch. Iteratively modify a target file (config, prompt, code), run an experiment with a fixed evaluation metric, keep improvements, discard regressions, and log everything to a TSV. Use when optimizing prompts, tuning hyperparameters, evolving agent configurations, or running overnight autonomous improvement loops.
Expert BigQuery data analysis using MCP tools. Perform schema discovery, data profiling, distribution analysis, anomaly detection, time series exploration, data quality assessment, and ad-hoc analytical queries against any BigQuery dataset. Use when the user asks to analyze, explore, profile, investigate, or query data in BigQuery, or asks questions that can be answered by querying a BigQuery dataset (e.g., 'how many users signed up last week?', 'show me the distribution of order values', 'find anomalies in the revenue data', 'what does the customers table look like?').