Arize-ai

Development guide for the Phoenix PXI agent. Use when modifying PXI-specific frontend or backend behavior, extending PXI tool wiring, updating PXI runtime capabilities, or changing the PXI agent request/dispatch flow. Start here for PXI-specific workflows, then read the relevant resource file for the layer you are changing.

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.

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/.

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 ("what should I do?"), (2) statistical questions ("what's the average latency?"), (3) summarize requests, (4) trace filtering ("show me traces with errors"), (5) vague questions ("is there a problem?"), (6) unrelated requests.

Author, edit, or iterate on prompts in the Phoenix prompt playground. Load before any playground tool call, including single-shot prompt rewrites.

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.

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

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 behavior", "synthetic dataset for PXI", "cover this tool with eval examples", or "find gaps in our PXI eval coverage". Inspects whichever evaluators currently live under evals/pxi/evaluators/ at use time and pauses to recommend a new evaluator if the behavior under test can't be scored by what already exists.

Refresh the list of agent frameworks supported by Arize tracing and diff against what's already in the repo. Pulls live data from https://arize.com/docs/llms.txt and produces a clean to-do list. Part of the rosetta-add-framework flow; can also be invoked standalone to answer "what frameworks are left to add?"

Add a new agent framework to the Rosetta Stone repo — researches the framework, builds all three observability tiers (no-observability, phoenix, ax), tests each, runs a Playwright smoke against the UI, updates README + TODO, and raises a PR. Trigger when the user asks to "add the <framework> framework", "implement <framework>", "wire up <framework>", or similar. The framework must be one of the Arize-supported agent frameworks (see TODO below).

Build a single tier (no-observability, phoenix, or ax) for a new framework. Clones the closest existing tier, swaps in framework-specific agent.py / tools.py / requirements.txt, and (for observability tiers) adds tracing.py + main.py wiring + eval-harness scripts. Part of the rosetta-add-framework flow; can be invoked standalone to rebuild a single tier from scratch.

Run a public-flow Playwright smoke test against a freshly-built framework tier's Next.js frontend. Covers home page rendering + product browsing — the parts that don't require X/Twitter OAuth. The Playwright project (package.json, config, tests) lives inside this skill directory and is checked into the repo. Part of the rosetta-add-framework flow.

Finalise a newly-added framework — updates the README's supported-frameworks table, directory tree, and per-framework "what differs" section, marks off the framework in the orchestrator skill's embedded TODO, commits per tier, and raises a PR. Part of the rosetta-add-framework flow.

Test a freshly-built tier for a new framework — boots the backend, smoke-tests the chat endpoint, runs synthetic requests, and (for phoenix) runs the eval harness. Verifies traces land in the right project. Part of the rosetta-add-framework flow; can be invoked standalone after a build to validate.

Record a full Wonder Toys demo by running a canned 3-turn conversation (search dragons → buy plushie → ship), then opening Arize AX in Safari and screenshotting the session view plus every trace in it. Use when the user asks to "capture a demo", "record screenshots of an Arize session", "demo the agent flow", or any similar phrasing. macOS only — uses AppleScript and `screencapture`.

Tear down a Rosetta Stone test project — deletes the platform project on Arize AX or Phoenix, removes the .env.test-local overlay, and kills any leftover dev server / ChromaDB processes started by the run. Idempotent and safe to call multiple times. Use the --keep flag at the orchestrator level to skip this entirely. Part of the rosetta-test e2e flow; can also be invoked standalone to clean up after a crashed run.

Manages Arize users, organizations, spaces, projects, roles, role bindings, resource restrictions, and API keys via the ax CLI. Use for enterprise admin workflows: inviting and offboarding users, onboarding new teams, creating custom roles for SAML/SSO mappings, assigning roles to users, restricting project-level access, and managing service keys for multi-tenant architectures. Covers ax users, ax organizations, ax spaces, ax projects, ax roles, ax role-bindings, and ax api-keys.

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

Handles LLM-as-judge and code evaluator workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, code evaluator, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

INVOKE THIS SKILL for Arize Prompt Hub and `ax prompts` workflows: author or import templates and save (Workflows A–B), label/promote (C), or list/get/edit/delete/duplicate (D). Use when the user mentions ax prompts, Prompt Hub, creating/editing/saving a prompt, `{variable}` placeholders, or production/staging labels. For improving prompt text using traces or eval scores, use arize-prompt-optimization. For running experiments, use arize-experiment.

Set up and configure Arize tracing for OpenAI Codex CLI sessions. Use when users want to set up Codex tracing, configure Arize AX or Phoenix for Codex, enable/disable tracing, or troubleshoot Codex tracing issues. Triggers on "set up codex tracing", "configure Arize for Codex", "configure Phoenix for Codex", "enable codex tracing", "setup-codex-tracing", or any request about connecting Codex to Arize or Phoenix for observability.

Set up and configure Arize tracing for Claude Code sessions or Agent SDK applications. Use when users want to set up tracing, configure Arize AX or Phoenix, create a new Arize project, get an API key, enable/disable tracing, or troubleshoot tracing issues. Triggers on "set up tracing", "configure Arize", "configure Phoenix", "enable tracing", "setup-claude-code-tracing", "create Arize project", "get Arize API key", "agent sdk tracing", or any request about connecting Claude Code or the Agent SDK to Arize or Phoenix for observability.

Set up and configure Arize tracing for GitHub Copilot sessions. Use when users want to set up tracing, configure Arize AX or Phoenix for Copilot, enable/disable tracing, or troubleshoot tracing issues. Triggers on "set up copilot tracing", "configure Arize for Copilot", "configure Phoenix for Copilot", "enable copilot tracing", "setup-copilot-tracing", or any request about connecting GitHub Copilot to Arize or Phoenix for observability.

Set up and configure Arize tracing for Cursor IDE sessions. Use when users want to set up tracing, configure Arize AX or Phoenix for Cursor, enable/disable tracing, or troubleshoot tracing issues. Triggers on "set up cursor tracing", "configure Arize for Cursor", "configure Phoenix for Cursor", "enable cursor tracing", "setup-cursor-tracing", or any request about connecting Cursor to Arize or Phoenix for observability.

Set up and configure Arize tracing for Gemini CLI sessions. Use when users want to set up tracing, configure Arize AX or Phoenix for Gemini, enable/disable tracing, or troubleshoot tracing issues. Triggers on "set up gemini tracing", "configure Arize for Gemini", "configure Phoenix for Gemini", "enable gemini tracing", "setup-gemini-tracing", or any request about connecting Gemini CLI to Arize or Phoenix for observability.

Set up and configure Arize tracing for Kiro CLI sessions. Use when users want to set up Kiro tracing, configure Arize AX or Phoenix for Kiro, enable/disable tracing, choose or set a default traced agent, or troubleshoot Kiro tracing issues. Triggers on "set up kiro tracing", "configure Arize for Kiro", "configure Phoenix for Kiro", "enable kiro tracing", "setup-kiro-tracing", "kiro agent tracing", or any request about connecting Kiro CLI to Arize or Phoenix for observability.

Run, interpret, and iterate on the OpenInference GenAI conformance MVP at python/openinference-instrumentation/scripts/conformance/. Use when the user mentions GenAI conformance, OTel GenAI semantic conventions, Weaver registry live-check, the dual-write conversion (`_genai_conversion.py`, `enable_genai_semconv`), `gen_ai.*` attribute coverage, or asks to add new providers / scenarios to the conformance harness.

Investigate and propose fixes for Python canary cron failures in the openinference repo. Use when the user mentions Python canary failures, Python cron failures, or when the auto-fix CI job reports Python instrumentation canary issues.

Keep hand-written docs/ documentation in JS packages accurate and up to date with their source code. Use this skill whenever: (1) source files in a JS package that has a docs/ folder are modified — especially exports, function signatures, types, or public API changes, (2) the user asks to "update docs", "sync docs", "check if docs are accurate", "review the documentation", or similar, (3) new exports or features are added to a JS package and the docs need to reflect them. Also trigger when the user mentions documentation drift, stale examples, or missing API coverage in any JS package under js/packages/.

Review Java OpenInference instrumentation code for correctness and completeness. Use this skill when reviewing a Java instrumentor package — whether it's a new instrumentor, a PR that modifies one, or when the user asks to audit/review/check an existing instrumentor's code quality. Trigger on phrases like "review the instrumentor", "check the Java code", "audit the package", "is this instrumentor correct", or any request to validate an OpenInference Java instrumentation package against project standards.

Review Python OpenInference instrumentation code for correctness and completeness. Use this skill when reviewing a Python instrumentor package — whether it's a new instrumentor, a PR that modifies one, or when the user asks to audit/review/check an existing instrumentor's code quality. Trigger on phrases like "review the instrumentor", "check the code", "audit the package", "is this instrumentor correct", or any request to validate an OpenInference Python instrumentation package against project standards.

Set up and configure Arize tracing for Claude Code sessions or Agent SDK applications. Use when users want to set up tracing, configure Arize AX or Phoenix, create a new Arize project, get an API key, enable/disable tracing, or troubleshoot tracing issues. Triggers on "set up tracing", "configure Arize", "configure Phoenix", "enable tracing", "setup-claude-code-tracing", "create Arize project", "get Arize API key", "agent sdk tracing", or any request about connecting Claude Code or the Agent SDK to Arize or Phoenix for observability.

Manage datasets in Arize AI using the ax CLI. Use when users want to list datasets, get dataset details, create new datasets, delete datasets, export dataset data, or work with dataset examples. Triggers on "list datasets", "create dataset", "ax datasets", "export dataset", "delete dataset", or any request about managing Arize datasets via CLI.

Manage projects in Arize AI using the ax CLI. Use when users want to list projects, get project details, create new projects, delete projects, or organize work within Arize spaces. Triggers on "list projects", "create project", "ax projects", "delete project", or any request about managing Arize projects via CLI.

Build and run evaluators for AI/LLM applications using Phoenix.

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

Use when testing an already-running Claude collector by sending a live marker trace to its configured OTLP endpoint without starting a second collector.

Use when validating Claude Code telemetry setup for this collector, especially to confirm Claude emits OTLP data to a local file before testing the configured upstream destination.