phoenix-client-development
The Phoenix Client Development skill provides guidance for building TypeScript features within the Phoenix AI observability platform, covering datasets, experiments, prompts, sessions, spans, and traces. Use this skill when developing new functionality in the Phoenix TypeScript SDK, particularly when working with experiments, OpenTelemetry tracing, or unit and integration tests that require understanding of the codebase conventions and testing framework.
git clone --depth 1 https://github.com/Arize-ai/phoenix /tmp/phoenix-client-development && cp -r /tmp/phoenix-client-development/js/packages/phoenix-client/.agents/skills/phoenix-client-development ~/.claude/skills/phoenix-client-developmentSKILL.md
# Phoenix Client Development TypeScript SDK for Phoenix AI observability: datasets, experiments, prompts, sessions, spans, traces. Read existing code in the directory you're working in before writing new code. ## Rule Files | Rule file | When to read | | ---------------------- | --------------------------------------------------------- | | `rules/experiments.md` | Experiment execution, task runners, evaluator wiring | | `rules/tracing.md` | OpenTelemetry tracer providers, span export, global state | | `rules/testing.md` | Unit tests, integration tests, test fixtures | ## Build and Test ```bash cd js/ pnpm --filter phoenix-client test ``` Tests use **vitest**. Test files live in `test/` named `*.test.ts`.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
Build and maintain documentation sites with Mintlify. Use when
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.
Design system conventions for the Phoenix frontend — layout, dialogs, error display, BEM CSS class naming, and CSS design tokens. Use when building UI, naming CSS classes, creating or consuming tokens, handling errors, or designing dialog interactions in app/src/.
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Build and run evaluators for AI/LLM applications using Phoenix.
Frontend development guidelines for the Phoenix AI observability platform. Use when writing, reviewing, or modifying React components, TypeScript code, styles, or UI features in the app/ directory. Triggers on any frontend task — new components, UI changes, styling, accessibility fixes, form handling, or component refactoring. Also use when the user asks about frontend conventions or component patterns for this project. For design system rules (error display, layout, dialogs, tokens), use the phoenix-design skill.