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SkillsMP a collecté 39 skills depuis Arize-ai/phoenix. Ouvrez un skill pour examiner sa source et ses détails.
Affichage de 39 skills collectés sur 39.
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…
Langue du texte source : anglais
Audit documentation gaps across the Phoenix repo by analyzing recent commits to main (default: last 7 days). Use this skill whenever the user asks to find undocumented features, identify docs gaps, audit what shipped without docs, check which recent changes…
Langue du texte source : anglais
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 js/app/ directory. Triggers on any frontend task — new components, UI…
Langue du texte source : anglais
Generates onboarding code snippets for Phoenix tracing integrations and wires them into the project onboarding UI. Produces install dependencies and implementation sections for SDKs like OpenAI, LangChain, Vercel AI SDK, and others. Supports Python and…
Langue du texte source : anglais
Write Playwright E2E tests for the Phoenix AI observability platform. Use when creating, updating, or debugging Playwright tests, or when the user asks about testing UI features, writing E2E tests, or automating browser interactions for Phoenix.
Langue du texte source : anglais
Screenshot a running Phoenix feature and attach images to a GitHub PR. Builds the frontend, starts Phoenix with env vars, uses agent-browser to capture screenshots, uploads to GCS, and updates the PR body.
Langue du texte source : anglais
Write, extend, and debug PXI Playwright E2E tests for Phoenix. Use when adding PXI agent frontend specs, authoring LLM-as-judge rubrics, asserting PXI tool use, persisting PXI test runs as Phoenix experiments, or debugging PXI E2E failures.
Langue du texte source : anglais
Create Phoenix release documentation grounded in actual code changes. Use this skill whenever the user asks to write release notes, document a release, update release documentation, or mentions undocumented releases. Also trigger when the user wants to update…
Langue du texte source : anglais
REST API development for Phoenix. Use when adding, modifying, or reviewing endpoints in src/phoenix/server/api/routers/v1/.
Langue du texte source : anglais
Audit recent changes to Phoenix's user-facing surfaces (Python clients, TypeScript clients, CLI, REST/GraphQL APIs) and patch the three external-facing agent skills — `phoenix-tracing`, `phoenix-cli`, and `phoenix-evals` — so they stay in sync with what…
Langue du texte source : anglais
TypeScript conventions and patterns for any TypeScript code in the Phoenix monorepo — including js/packages/, js/app/, and any other TS directories. Use this skill whenever writing, reviewing, or modifying TypeScript code — new functions, types, exports,…
Langue du texte source : anglais
Migrate or upgrade TypeScript tooling in the Phoenix monorepo. Use when upgrading TypeScript versions, switching tools (ESLint to oxlint, Prettier to oxfmt), upgrading bundlers (Vite, esbuild), or making major dependency upgrades. Triggers on requests to…
Langue du texte source : anglais
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…
Langue du texte source : anglais
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Langue du texte source : anglais
Open-source AI observability platform for tracing, evaluating, and improving LLM applications with OpenTelemetry integration
Langue du texte source : anglais
Write efficient GraphQL queries against the Phoenix API. Load this skill in two cases: (1) before composing any non-trivial GraphQL query yourself for data analysis (via the `phoenix-gql` bash command) — it contains schema entrypoints and patterns that…
Langue du texte source : anglais
Build and run evaluators for AI/LLM applications using Phoenix.
Langue du texte source : anglais
Manage GitHub issues, labels, project boards, sprint operations, and roadmap health for the Arize-ai/phoenix repository. Use when filing roadmap issues, triaging bugs, applying labels, running sprint close-out and rollover, auditing board hygiene, checking…
Langue du texte source : anglais
Write effective, consistent annotations on LLM/agent spans and traces, and coach the user on annotation practice. Load this whenever you are about to record structured feedback with the `batch_span_annotate` tool, or when the user asks how to annotate, label,…
Langue du texte source : anglais
Design and implementation guide for the Phoenix CLI (`px`). Covers the noun-verb command structure, dual-audience design (humans and coding agents), Commander.js patterns, configuration resolution, output formats, exit codes, and conventions for adding or…
Langue du texte source : anglais
Maintaining packages/phoenix-sqlean, the vendored fork of nalgeon/sqlean.py published as arize-phoenix-sqlean. Use when bumping the bundled SQLite, sqlean, or xxHash versions, changing its wheel matrix, or touching its publish path. Trigger on any change…
Langue du texte source : anglais
Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output. Trigger when the user wants to create a new evaluator, improve an existing one's logic or rubric, choose labels, or decide what to measure on a dataset or experiment.…
Langue du texte source : anglais
Manage stacked branches and pull requests with the gh-stack GitHub CLI extension. Use when the user wants to create, push, rebase, sync, navigate, or view stacks of dependent PRs. Triggers on tasks involving stacked diffs, dependent pull requests, branch…
Langue du texte source : anglais
Create a new built-in classification evaluator for Phoenix evals. Use this skill whenever the user asks to create a new eval, build a new metric, add a new builtin evaluator, create an LLM-as-a-judge metric, or add a new classification evaluator to Phoenix.
Langue du texte source : anglais
Open-code Phoenix spans with PXI-owned notes, recover those notes for axial coding, and promote stable categories into structured annotations. Load this when analyzing spans to discover failure patterns before a taxonomy exists.
Langue du texte source : anglais
Development guide for the @arizeai/phoenix-client TypeScript SDK — run and resume experiments, manage OpenTelemetry tracer providers with stack-based attach/detach, and write vitest unit and integration tests. Use when adding features to phoenix-client,…
Langue du texte source : anglais
Diagnose failure modes by systematically investigating traces. Trigger when the user explicitly asks for cross-trace diagnosis: "what's going wrong?", "were there errors?", "debug this", "where is my agent struggling?". Do NOT trigger on: (1) advice questions…
Langue du texte source : anglais
Run, read, and compare dataset-backed experiments to find evidence that a prompt or pipeline is improving. Trigger when the user wants to iterate over a dataset with experiments, compare experiment runs, read experiment quality/latency/cost, or decide whether…
Langue du texte source : anglais
Author, edit, or iterate on prompts in the Phoenix prompt playground, including running experiments over a dataset. Load before any playground tool call, including single-shot prompt rewrites.
Langue du texte source : anglais
Maintain the bundled TypeScript package docs that ship inside Phoenix npm packages. Use this skill whenever adding or updating docs for `@arizeai/phoenix-client`, `@arizeai/phoenix-evals`, or `@arizeai/phoenix-otel`, when changing the Mintlify package-doc…
Langue du texte source : anglais
Understand what a Phoenix dataset is and reason well about its examples, outputs, splits, and how it feeds evaluators and experiments. Load this whenever a dataset is in view or the user asks what a dataset is, how splits work, what an output "means", or how…
Langue du texte source : anglais
Generate synthetic evaluation datasets for the PXI eval harness (evals/pxi/). Use whenever the user asks to create, author, draft, expand, or audit an eval dataset for a PXI tool, skill, or behavior — including phrases like "write evals for <tool>", "test PXI…
Langue du texte source : anglais
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…
Langue du texte source : anglais
Backend development guide for the Phoenix AI observability platform (Strawberry GraphQL, SQLAlchemy async, FastAPI). Use this skill when writing or modifying Python server code in the phoenix repo — adding mutations, types, migrations, or tests. Trigger on…
Langue du texte source : anglais
Maintain the Phoenix llms.txt documentation index at docs/phoenix/llms.txt — the machine-readable docs map used by AI agents and the `px docs fetch` CLI. Use this skill whenever adding, auditing, or reorganizing llms.txt entries. Trigger when the user…
Langue du texte source : anglais
Bump the next release-please version for a Phoenix Python package (arize-phoenix, arize-phoenix-client, arize-phoenix-evals, arize-phoenix-otel) by opening a PR with a Release-As commit footer. Use this skill when the user asks to "set the release-please…
Langue du texte source : anglais
Guide for the phoenix-otel TypeScript package — OTel registration, stack-based global provider management, and provider lifecycle.
Langue du texte source : anglais
Build and maintain documentation sites with Mintlify. Use when creating docs pages, configuring navigation, adding components, or setting up API references.
Langue du texte source : anglais
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js…
Langue du texte source : anglais