Use when you need to write unit tests for Spring Boot applications — including pure unit tests with @ExtendWith(MockitoExtension.class) for @Service/@Component, slice tests with @WebMvcTest and @MockitoBean for controllers, @JsonTest for JSON serialization, parameterized tests with @CsvSource/@MethodSource, test profiles, and @TestConfiguration. For framework-agnostic Java use @131-java-testing-unit-testing. For integration tests use @322-frameworks-spring-boot-testing-integration-tests. This should trigger for requests such as Review Java code for Spring Boot unit tests; Apply best practices for Spring Boot unit tests in Java code; Write Mockito-first unit tests for Spring Boot services; Avoid unnecessary @SpringBootTest in Spring unit tests; Review Spring Boot slice-free unit test design. Part of Plinth Toolkit

Use when you need to apply functional exception handling best practices in Java — including replacing exception overuse with Optional and VAVR Either types, designing error type hierarchies using sealed classes and enums, implementing monadic error composition pipelines, establishing functional control flow patterns, and reserving exceptions only for truly exceptional system-level failures. This should trigger for requests such as Improve the code with Functional Exception Handling; Apply Functional Exception Handling; Refactor the code with Functional Exception Handling; Model Java errors with Result or Either types; Replace exception-heavy flows with functional error handling. Part of Plinth Toolkit

Orchestrates the complete talk preparation pipeline from raw material to revision sheets, running 6 stages in sequence with human-in-the-loop checkpoints for REX or Concept mode talks. Use when starting a new talk pipeline, resuming a pipeline from a specific stage, or running the full end-to-end preparation workflow.

Start voice calls via the OpenClaw voice-call plugin.

Local text-to-speech via sherpa-onnx (offline, no cloud)

Start voice calls via the OpenClaw voice-call plugin.

Local text-to-speech via sherpa-onnx (offline, no cloud)

Start voice calls via the OpenClaw voice-call plugin.

Local text-to-speech via sherpa-onnx (offline, no cloud)

Operate the local Trackio reporter for Autolab HF Jobs. Use when a reporter or planner needs to inspect scores, active jobs, worker anomalies, duplicate launches, or the overall experiment board.

Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

Create commits, pull requests, and GitHub PR reviews. Use when the user wants to commit changes, open or refresh a pull request, or add/submit PR review comments.

Use when you need to implement or improve Java logging and observability — including selecting SLF4J with Logback/Log4j2, applying proper log levels (ERROR, WARN, INFO, DEBUG, TRACE), parameterized logging, correlation context, secure logging without sensitive data exposure, environment-specific configuration, log aggregation, monitoring, and alerting. This should trigger for requests such as Improve logging; Apply logging; Refactor logging; Add logging support. Part of cursor-rules-java project

Use when you need to apply Java exception handling best practices — including using specific exception types, managing resources with try-with-resources, securing exception messages, preserving error context via exception chaining, validating inputs early with fail-fast principles, handling thread interruption correctly, documenting exceptions with @throws, enforcing logging policy, translating exceptions at API boundaries, managing retries and idempotency, enforcing timeouts, attaching suppressed exceptions, and propagating failures in async/reactive code. This should trigger for requests such as Exception handling; Use try-with-resources in Java code; Create exception chaining in Java code; Apply fail-fast validation in Java code. Part of cursor-rules-java project

Extend video duration using Kling AI continuation. Use when creating longer videos from shorter clips or building sequences. Trigger with phrases like 'klingai extend video', 'kling ai video continuation', 'klingai longer video', 'extend klingai clip'.

Build usage analytics and reporting for Kling AI video generation. Use when tracking patterns, analyzing costs, or building dashboards. Trigger with phrases like 'klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats'.

Production SDK patterns for Kling AI: client wrapper, retry logic, async polling, and error handling. Use when building robust integrations. Trigger with phrases like 'klingai sdk', 'kling ai client', 'klingai patterns', 'kling ai wrapper'.

Production readiness checklist for Kling AI integrations. Use before going live or during deployment review. Trigger with phrases like 'klingai production ready', 'kling ai go live', 'klingai checklist', 'deploy klingai'.

Optimize Kling AI for speed, quality, and cost efficiency. Use when improving generation times or finding optimal settings. Trigger with phrases like 'klingai performance', 'kling ai optimize', 'faster klingai', 'klingai quality settings'.

Implement budget limits, usage alerts, and spending controls for Kling AI. Use when managing costs or preventing overruns. Trigger with phrases like 'klingai cost', 'kling ai budget', 'klingai spending limit', 'video generation costs'.