docker
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phoenix-observability
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, monitoring production AI systems, or setting up observability infrastructure for agentic systems. **PROACTIVE ACTIVATION**: Auto-invoke when implementing observability/tracing for LLM agents, setting up evaluation pipelines, or configuring OpenTelemetry instrumentation. **DETECTION**: Check for arize-phoenix imports, OpenTelemetry setup, or observability-related code. **USE CASES**: Debugging LLM apps, running evaluations, monitoring production systems, setting up tracing infrastructure, instrumenting agent frameworks, tracing custom agents with decorators (@tracer.agent, @tracer.chain, @tracer.tool).
8debug
Systematic bug investigation using a root-cause-first methodology. Use PROACTIVELY when encountering any bug, test failure, unexpected behavior, or performance problem—before proposing any fix. **PROACTIVE ACTIVATION**: Invoke immediately when the user shares an error message, stack trace, failing test, or says something is 'broken', 'not working', or 'acting weird'. **USE CASES**: Reproducing errors, tracing failures in multi-component systems, debugging flaky tests, diagnosing performance regressions, investigating unexpected output.
1release
Orchestrates a complete software release: pre-flight checks, changelog update, version bump, git tag, and GitHub release. Use when cutting a release, tagging a version, or publishing a new version of the project. **PROACTIVE ACTIVATION**: Invoke when user says 'release', 'cut a release', 'tag a release', 'ship v1.x', 'publish version', or '/release'. **USE CASES**: Tagging main after a sprint, releasing a library version, preparing a versioned deployment.
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