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収集済み skill 42 件中 40 件を表示しています。
Set up, write, and publish documentation sites with Mintlify, and work on existing Mintlify projects. Use when the user wants to create a docs site for a repo or product, turn a docs/ folder or README into a hosted docs site, document an API from an OpenAPI…
原文の言語: 英語
Open-source AI observability platform for tracing, evaluating, and improving LLM applications with OpenTelemetry integration
原文の言語: 英語
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…
原文の言語: 英語
Run open-source triage on Arize-ai/phoenix issues — classify type, check sufficiency, apply component and complexity labels, flag work that already shipped, investigate complex bugs, tidy formatting, then gate against a contributor policy such as "good…
原文の言語: 英語
Create a new Harbor task for evaluating agents. Use when the user wants to scaffold, build, or design a new task, benchmark problem, or eval. Guides through instruction writing, environment setup, verifier design (pytest vs Reward Kit vs custom), and solution…
原文の言語: 英語
Manages stacked PRs and splits multi-part work into reviewable branches with gh-stack. Use for stack creation, viewing, edits, push, submit, sync, rebase, merge, or checkout; when asked to split or isolate work for review; whenever a user mentions a stack,…
原文の言語: 英語
Write Harbor task verifiers using Reward Kit. Use when creating or editing a task's tests/ directory, adding grading criteria, setting up LLM/agent judges, or designing verifiers that produce a reward score.
原文の言語: 英語
Build, 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…
原文の言語: 英語
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
原文の言語: 英語
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…
原文の言語: 英語
Debug 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…
原文の言語: 英語
Creates and manages isolated cloud sandboxes (secure code execution environments with dedicated runtimes) on the Daytona platform. Use when a task needs an isolated runtime, sandbox, secure compute, or Daytona SDK/API/CLI operations. Covers Python,…
原文の言語: 英語
Add a new built-in (pre-built) LLM classification evaluator to the Phoenix evals libraries, and optionally promote it to a server-side dataset evaluator. Use when working in the Phoenix repo and asked to add a new built-in eval, pre-built metric, builtin…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
Configure 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…
原文の言語: 英語
Writes and maintains the body of a pull request opened by a coding agent on Arize-ai/phoenix, including a fixed "How this PR was made" section that reports steering, abandoned approaches, and verification gaps so a reviewer can pick a review depth. Use when…
原文の言語: 英語
Screenshot a running Phoenix feature and attach images to a GitHub PR. Builds the frontend, starts Phoenix with env vars, captures browser screenshots, and attaches them to the PR body with `gh pr edit --attach`.
原文の言語: 英語
Conventions for creating, modifying, and reviewing production-faithful Storybook stories in the Phoenix frontend (js/app/stories, js/app/.storybook). Covers sidebar taxonomy and titles, the tag vocabulary, entry shape, option grids, component audits,…
原文の言語: 英語
Use when working with Harbor's `harbor exec` CLI workflow: compiling files, directories, or globs into Harbor tasks; running map jobs; configuring artifacts and existence-only verification; using map-reduce; writing or reviewing ExecConfig YAML/JSON/TOML; or…
原文の言語: 英語
Find 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…
原文の言語: 英語
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…
原文の言語: 英語
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.…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
REST API development for Phoenix. Use when adding, modifying, or reviewing endpoints in src/phoenix/server/api/routers/v1/.
原文の言語: 英語
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…
原文の言語: 英語
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,…
原文の言語: 英語
Author, edit, or iterate on prompts in the Phoenix prompt playground, including running experiments over a dataset. Load before any playground `ui.*` operation call, including single-shot prompt rewrites.
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
このプロンプトをコピーして、使用中の AI アシスタントに送ってください。
Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/Arize-ai/phoenix.README を読み込めませんでした。リポジトリの Skill 一覧は下で引き続き確認できます。