Results for “trial”

Investigates slow responses, tool failures, and guardrail rejections by querying agent trace logs and performance metrics.

`analysis-agent`/`task-agent`/`review-agent`: use when logic, rules, invariants, branches, edges, or failure paths need isolated tests; skip without a unit-test decision.

Debug agent execution by querying trace and metric tables, analyzing tool calls, durations, errors, and performance trends.

Use with analysis-agent or task-agent for task-local transaction, isolation, and conflict decisions. Do not use without a transaction decision or as task owner.

Find the earliest decisive failure in an agent run trace and propose an evidence-backed targeted repair. Load when a run failed, results are wrong, or the user asks what went wrong in an agent session. Also triggers on "localize the fault", "first incorrect step", "debug this run", "trace attribution", "why did the agent fail", or after run-trace captures errors. Pairs with debug-and-fix for code defects and dynamic-routing for plan faults.

`analysis-agent`/`task-agent`/`review-agent`: use for recurrence guards on known defects, incidents, or escaped failures; skip speculative risk without a prior failure mechanism.

`analysis-agent`/`task-agent`/`review-agent`: use for provider-consumer compatibility of APIs, events, schemas, or behavior; skip without independent consumer/version risk.

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.

Build a throwaway prototype to flush out a design before committing to it. Routes between two branches — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route. Use when the user wants to prototype, sanity-check a data model or state machine, mock up a UI, explore design options, or says "prototype this", "let me play with it", "try a few designs".

Process large ML datasets in parallel across CPU or GPU clusters, with streaming execution, multi-format I/O, and integration with Ray Train, PyTorch, and TensorFlow for batch inference and preprocessing pipelines.

Guides the agent to reference official MathWorks Documentation and Help. Determine correct function syntax and workflows from user guides when deeper context is needed. Minimize iterations and repetitive trial and error. Use this skill to: Identify correct syntax and configuration details. Retrieve relevant, version-specific (or release-specific) information from official documentation. Consult user guides when conceptual or workflow context is needed. Apply best practices.