pup
claudecodexDatadog API CLI with 49 command groups, 300+ subcommands. Skills and domain agents for monitoring, logs, APM, security, and infrastructure.
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claudecodex
arize-phoenix
Connect to your Phoenix instance to debug, evaluate, and improve LLM applications.
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Plugin arize-phoenix from marketplace otelyssey in using-system/otelyssey. Commands read from the repo's manifest — today.
This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.
What it installs
Skills (5)
phoenix-cliDebug LLM applications using the Phoenix CLI (`px`, from @arizeai/phoenix-cli). Fetch traces, spans, and sessions, annotate them, analyze errors, measure how often a failure or pattern occurs across traces, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use
phoenix-error-analysisFind out what is going wrong in LLM or agent traffic by reading sampled Phoenix traces, spans, or sessions, writing free-form notes (open coding), then grouping the notes into a few narrow annotations, one per failure dimension with a small label set and counts, that pick eval targets and fix priori
phoenix-evalsBuild, run, and validate evaluators for AI/LLM applications with Phoenix, in Python or TypeScript (including Vercel AI SDK apps) — code-based checks and LLM-as-a-judge, run over exported traces or dataframes or as Phoenix experiments, gate them in CI, and validate judges against human-labeled data.
phoenix-harborConfigure and interpret the Phoenix plugin for Harbor agent evaluations. Use when adding `arize-phoenix` to Harbor jobs, choosing ATIF tracing, mapping Harbor tasks and rewards to Phoenix experiments, comparing agents or models, resuming jobs, or troubleshooting Harbor records in Phoenix.
phoenix-tracingOpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
MCP servers (1)
phoenixruns: ./scripts/phoenix-mcp
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