[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-arize-arize-ai-provider-integration":3,"mdc-vdt09r-key":50,"related-org-arize-arize-ai-provider-integration":3206,"related-repo-arize-arize-ai-provider-integration":3364},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":29,"repoUrl":30,"updatedAt":31,"license":32,"forks":33,"topics":34,"repo":45,"sourceUrl":48,"mdContent":49},"arize-ai-provider-integration","manage Arize AI provider integrations","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"arize","Arize AI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Farize.jpg","Arize-ai",[13,17,20,23,26],{"name":14,"slug":15,"type":16},"Azure","azure","tag",{"name":18,"slug":19,"type":16},"Anthropic","anthropic",{"name":21,"slug":22,"type":16},"LLM","llm",{"name":24,"slug":25,"type":16},"Integrations","integrations",{"name":27,"slug":28,"type":16},"OpenAI","openai",38,"https:\u002F\u002Fgithub.com\u002FArize-ai\u002Farize-skills","2026-07-22T05:37:23.90468",null,5,[35,36,37,8,38,39,40,41,42,43,44],"agent-skills","ai-agents","ai-observability","claude-code","codex","cursor","datasets","experiments","llmops","tracing",{"repoUrl":30,"stars":29,"forks":33,"topics":46,"description":47},[35,36,37,8,38,39,40,41,42,43,44],"Agent skills for Arize — datasets, experiments, and traces via the ax CLI","https:\u002F\u002Fgithub.com\u002FArize-ai\u002Farize-skills\u002Ftree\u002FHEAD\u002Fskills\u002Farize-ai-provider-integration","---\nname: arize-ai-provider-integration\ndescription: 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.\nmetadata:\n  author: arize\n  version: \"1.0\"\ncompatibility: Requires the ax CLI and a configured Arize profile.\n---\n\n# Arize AI Integration Skill\n\n> **`SPACE`** — Most `--space` flags and the `ARIZE_SPACE` env var accept a space **name** (e.g., `my-workspace`) or a base64 space **ID** (e.g., `U3BhY2U6...`). Find yours with `ax spaces list`.\n> **Note:** `ai-integrations create` does **not** accept `--space` — AI integrations are account-scoped. Use `--space` only with `list`, `get`, `update`, and `delete`.\n\n## Concepts\n\n- **AI Integration** = stored LLM provider credentials registered in Arize; used by evaluators to call a judge model and by other Arize features that need to invoke an LLM on your behalf\n- **Provider** = the LLM service backing the integration (e.g., `openAI`, `anthropic`, `awsBedrock`)\n- **Integration ID** = a base64-encoded global identifier for an integration (e.g., `TGxtSW50ZWdyYXRpb246MTI6YUJjRA==`); required for evaluator creation and other downstream operations\n- **Scoping** = visibility rules controlling which spaces or users can use an integration\n- **Auth type** = how Arize authenticates with the provider: `default` (provider API key), `proxy_with_headers` (proxy via custom headers), or `bearer_token` (bearer token auth)\n\n## Prerequisites\n\nProceed directly with the task — run the `ax` command you need. Do NOT check versions, env vars, or profiles upfront.\n\nIf an `ax` command fails, troubleshoot based on the error:\n- `command not found` or version error → see [references\u002Fax-setup.md](references\u002Fax-setup.md)\n- `401 Unauthorized` \u002F missing API key → run `ax profiles show` to inspect the current profile. If the profile is missing or the API key is wrong, follow [references\u002Fax-profiles.md](references\u002Fax-profiles.md) to create\u002Fupdate it. If the user doesn't have their key, direct them to https:\u002F\u002Fapp.arize.com\u002Fadmin > API Keys\n- Space unknown → run `ax spaces list` to pick by name, or ask the user\n- LLM provider call fails (missing provider credentials) → run `ax ai-integrations list --space SPACE` to check for platform-managed credentials. If none exist:\n  - **Preferred:** Give the user the exact `ax ai-integrations create` command from Supported Providers below (reference env var names like `$OPENAI_API_KEY`, never raw values). Ask them to export the provider key in **their own terminal** and run the command there — never paste the key into chat. Do not run the create command yourself unless the var is already exported in **this** terminal session.\n  - **Bedrock\u002FVertex:** use `--provider-metadata` instead of `--api-key` (see Supported Providers below).\n- **Security:** Never read `.env` files or search the filesystem for credentials. Use `ax profiles` for Arize credentials and `ax ai-integrations` for LLM provider keys. Never ask the user to paste secrets into chat. For missing credentials, see [references\u002Fax-profiles.md](references\u002Fax-profiles.md).\n\n---\n\n## List AI Integrations\n\nList all integrations accessible in a space:\n\n```bash\nax ai-integrations list --space SPACE\n```\n\nFilter by name (case-insensitive substring match):\n\n```bash\nax ai-integrations list --space SPACE --name \"openai\"\n```\n\nPaginate large result sets:\n\n```bash\n# Get first page\nax ai-integrations list --space SPACE --limit 20 -o json\n\n# Get next page using cursor from previous response\nax ai-integrations list --space SPACE --limit 20 --cursor CURSOR_TOKEN -o json\n```\n\n**Key flags:**\n\n| Flag | Description |\n|------|-------------|\n| `--space` | Space name or ID to filter integrations |\n| `--name` | Case-insensitive substring filter on integration name |\n| `--limit` | Max results (1–100, default 15) |\n| `--cursor` | Pagination token from a previous response |\n| `-o, --output` | Output format: `table` (default) or `json` |\n\n**Response fields:**\n\n| Field | Description |\n|-------|-------------|\n| `id` | Base64 integration ID — copy this for downstream commands |\n| `name` | Human-readable name |\n| `provider` | LLM provider enum (see Supported Providers below) |\n| `has_api_key` | `true` if credentials are stored |\n| `model_names` | Allowed model list, or `null` if all models are enabled |\n| `enable_default_models` | Whether default models for this provider are allowed |\n| `function_calling_enabled` | Whether tool\u002Ffunction calling is enabled |\n| `auth_type` | Authentication method: `default`, `proxy_with_headers`, or `bearer_token` |\n\n---\n\n## Get a Specific Integration\n\n```bash\nax ai-integrations get NAME_OR_ID\nax ai-integrations get NAME_OR_ID -o json\nax ai-integrations get NAME_OR_ID --space SPACE   # required when using name instead of ID\n```\n\nUse this to inspect an integration's full configuration or to confirm its ID after creation.\n\n---\n\n## Create an AI Integration\n\nBefore creating, always list integrations first — the user may already have a suitable one:\n\n```bash\nax ai-integrations list --space SPACE\n```\n\nIf no suitable integration exists, create one. The required flags depend on the provider.\n\n### OpenAI\n\n```bash\nax ai-integrations create \\\n  --name \"My OpenAI Integration\" \\\n  --provider openAI \\\n  --api-key $OPENAI_API_KEY\n```\n\n### Anthropic\n\n```bash\nax ai-integrations create \\\n  --name \"My Anthropic Integration\" \\\n  --provider anthropic \\\n  --api-key $ANTHROPIC_API_KEY\n```\n\n### Azure OpenAI\n\n```bash\nax ai-integrations create \\\n  --name \"My Azure OpenAI Integration\" \\\n  --provider azureOpenAI \\\n  --api-key $AZURE_OPENAI_API_KEY \\\n  --base-url \"https:\u002F\u002Fmy-resource.openai.azure.com\u002F\"\n```\n\n### AWS Bedrock\n\nAWS Bedrock uses IAM role-based auth. Provide the ARN of the role Arize should assume via `--provider-metadata`:\n\n```bash\nax ai-integrations create \\\n  --name \"My Bedrock Integration\" \\\n  --provider awsBedrock \\\n  --provider-metadata '{\"role_arn\": \"arn:aws:iam::123456789012:role\u002FArizeBedrockRole\"}'\n```\n\n### Vertex AI\n\nVertex AI uses GCP service account credentials. Provide the GCP project and region via `--provider-metadata`:\n\n```bash\nax ai-integrations create \\\n  --name \"My Vertex AI Integration\" \\\n  --provider vertexAI \\\n  --provider-metadata '{\"project_id\": \"my-gcp-project\", \"location\": \"us-central1\"}'\n```\n\n### Gemini\n\n```bash\nax ai-integrations create \\\n  --name \"My Gemini Integration\" \\\n  --provider gemini \\\n  --api-key $GEMINI_API_KEY\n```\n\n### NVIDIA NIM\n\n```bash\nax ai-integrations create \\\n  --name \"My NVIDIA NIM Integration\" \\\n  --provider nvidiaNim \\\n  --api-key $NVIDIA_API_KEY \\\n  --base-url \"https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\"\n```\n\n### Custom (OpenAI-compatible endpoint)\n\n```bash\nax ai-integrations create \\\n  --name \"My Custom Integration\" \\\n  --provider custom \\\n  --base-url \"https:\u002F\u002Fmy-llm-proxy.example.com\u002Fv1\" \\\n  --api-key $CUSTOM_LLM_API_KEY\n```\n\n### Supported Providers\n\n| Provider | Required extra flags |\n|----------|---------------------|\n| `openAI` | `--api-key \u003Ckey>` |\n| `anthropic` | `--api-key \u003Ckey>` |\n| `azureOpenAI` | `--api-key \u003Ckey>`, `--base-url \u003Cazure-endpoint>` |\n| `awsBedrock` | `--provider-metadata '{\"role_arn\": \"\u003Carn>\"}'` |\n| `vertexAI` | `--provider-metadata '{\"project_id\": \"\u003Cgcp-project>\", \"location\": \"\u003Cregion>\"}'` |\n| `gemini` | `--api-key \u003Ckey>` |\n| `nvidiaNim` | `--api-key \u003Ckey>`, `--base-url \u003Cnim-endpoint>` |\n| `custom` | `--base-url \u003Cendpoint>` |\n\n### Optional flags for any provider\n\n| Flag | Description |\n|------|-------------|\n| `--model-name` | Allowed model name (repeat for multiple, e.g. `--model-name gpt-4o --model-name gpt-4o-mini`); omit to allow all models |\n| `--enable-default-models` | Enable the provider's default model list |\n| `--function-calling-enabled` | Enable tool\u002Ffunction calling support |\n| `--auth-type` | Authentication type: `default`, `proxy_with_headers`, or `bearer_token` |\n| `--headers` | Custom headers as JSON object or file path (for proxy auth) |\n| `--provider-metadata` | Provider-specific metadata as JSON object or file path |\n\n### After creation\n\nCapture the returned integration ID (e.g., `TGxtSW50ZWdyYXRpb246MTI6YUJjRA==`) — it is needed for evaluator creation and other downstream commands. If you missed it, retrieve it:\n\n```bash\nax ai-integrations list --space SPACE -o json\n# or by name\u002FID directly:\nax ai-integrations get NAME_OR_ID\n```\n\n---\n\n## Update an AI Integration\n\n`update` is a partial update — only the flags you provide are changed. Omitted fields stay as-is.\n\n```bash\n# Rename\nax ai-integrations update NAME_OR_ID --name \"New Name\"\n\n# Rotate the API key\nax ai-integrations update NAME_OR_ID --api-key $OPENAI_API_KEY\n\n# Change the model list (replaces all existing model names)\nax ai-integrations update NAME_OR_ID --model-name gpt-4o --model-name gpt-4o-mini\n\n# Update base URL (for Azure, custom, or NIM)\nax ai-integrations update NAME_OR_ID --base-url \"https:\u002F\u002Fnew-endpoint.example.com\u002Fv1\"\n\n# Restrict visibility to specific spaces (full replace — lists all spaces that should have access)\nax ai-integrations update NAME_OR_ID \\\n  --scopings '[{\"space_id\": \"SPACE_GLOBAL_ID\", \"scoping_type\": \"include\"}]'\n```\n\nAdd `--space SPACE` when using a name instead of ID. Any flag accepted by `create` can be passed to `update`.\n\n**`--scopings` flag:** Controls which spaces can use this integration. Accepts a JSON array of scoping rules. Replaces all existing scopings on update. Use `ax spaces list -o json` to find space global IDs.\n\n---\n\n## Delete an AI Integration\n\n**Warning:** Deletion is permanent. Evaluators that reference this integration will no longer be able to run.\n\n```bash\nax ai-integrations delete NAME_OR_ID --force\nax ai-integrations delete NAME_OR_ID --space SPACE --force   # required when using name instead of ID\n```\n\nOmit `--force` to get a confirmation prompt instead of deleting immediately.\n\n---\n\n## Troubleshooting\n\n| Problem | Solution |\n|---------|----------|\n| `ax: command not found` | See [references\u002Fax-setup.md](references\u002Fax-setup.md) |\n| `401 Unauthorized` | API key may not have access to this space. Verify key and space ID at https:\u002F\u002Fapp.arize.com\u002Fadmin > API Keys |\n| `No profile found` | Run `ax profiles show --expand`; set `ARIZE_API_KEY` env var or write `~\u002F.arize\u002Fconfig.toml` |\n| `Integration not found` | Verify with `ax ai-integrations list --space SPACE` |\n| `has_api_key: false` after create | Credentials were not saved — re-run `update` with the correct `--api-key` or `--provider-metadata` |\n| Evaluator runs fail with LLM errors | Check integration credentials with `ax ai-integrations get INT_ID`; rotate the API key if needed |\n| `provider` mismatch | Cannot change provider after creation — delete and recreate with the correct provider |\n\n---\n\n## Related Skills\n\n- **arize-evaluator**: Create LLM-as-judge evaluators that use an AI integration → use `arize-evaluator`\n- **arize-experiment**: Run experiments that use evaluators backed by an AI integration → use `arize-experiment`\n\n---\n\n## Save Credentials for Future Use\n\nSee [references\u002Fax-profiles.md](references\u002Fax-profiles.md) § Save Credentials for Future Use.\n",{"data":51,"body":55},{"name":4,"description":6,"metadata":52,"compatibility":54},{"author":8,"version":53},"1.0","Requires the ax CLI and a configured Arize profile.",{"type":56,"children":57},"root",[58,67,208,215,323,329,342,354,542,546,552,557,600,605,656,661,790,798,923,931,1119,1122,1128,1216,1221,1224,1230,1235,1264,1269,1274,1359,1363,1441,1447,1546,1552,1564,1653,1659,1670,1756,1762,1840,1846,1944,1950,2052,2058,2253,2259,2405,2411,2423,2488,2491,2497,2507,2776,2803,2827,2830,2836,2846,2914,2927,2930,2936,3139,3142,3148,3181,3184,3190,3200],{"type":59,"tag":60,"props":61,"children":63},"element","h1",{"id":62},"arize-ai-integration-skill",[64],{"type":65,"value":66},"text","Arize AI Integration Skill",{"type":59,"tag":68,"props":69,"children":70},"blockquote",{},[71],{"type":59,"tag":72,"props":73,"children":74},"p",{},[75,86,88,94,96,102,104,109,111,117,119,124,125,131,133,139,141,146,148,154,156,161,163,168,170,175,177,183,185,191,192,198,200,206],{"type":59,"tag":76,"props":77,"children":78},"strong",{},[79],{"type":59,"tag":80,"props":81,"children":83},"code",{"className":82},[],[84],{"type":65,"value":85},"SPACE",{"type":65,"value":87}," — Most ",{"type":59,"tag":80,"props":89,"children":91},{"className":90},[],[92],{"type":65,"value":93},"--space",{"type":65,"value":95}," flags and the ",{"type":59,"tag":80,"props":97,"children":99},{"className":98},[],[100],{"type":65,"value":101},"ARIZE_SPACE",{"type":65,"value":103}," env var accept a space ",{"type":59,"tag":76,"props":105,"children":106},{},[107],{"type":65,"value":108},"name",{"type":65,"value":110}," (e.g., ",{"type":59,"tag":80,"props":112,"children":114},{"className":113},[],[115],{"type":65,"value":116},"my-workspace",{"type":65,"value":118},") or a base64 space ",{"type":59,"tag":76,"props":120,"children":121},{},[122],{"type":65,"value":123},"ID",{"type":65,"value":110},{"type":59,"tag":80,"props":126,"children":128},{"className":127},[],[129],{"type":65,"value":130},"U3BhY2U6...",{"type":65,"value":132},"). Find yours with ",{"type":59,"tag":80,"props":134,"children":136},{"className":135},[],[137],{"type":65,"value":138},"ax spaces list",{"type":65,"value":140},".\n",{"type":59,"tag":76,"props":142,"children":143},{},[144],{"type":65,"value":145},"Note:",{"type":65,"value":147}," ",{"type":59,"tag":80,"props":149,"children":151},{"className":150},[],[152],{"type":65,"value":153},"ai-integrations create",{"type":65,"value":155}," does ",{"type":59,"tag":76,"props":157,"children":158},{},[159],{"type":65,"value":160},"not",{"type":65,"value":162}," accept ",{"type":59,"tag":80,"props":164,"children":166},{"className":165},[],[167],{"type":65,"value":93},{"type":65,"value":169}," — AI integrations are account-scoped. Use ",{"type":59,"tag":80,"props":171,"children":173},{"className":172},[],[174],{"type":65,"value":93},{"type":65,"value":176}," only with ",{"type":59,"tag":80,"props":178,"children":180},{"className":179},[],[181],{"type":65,"value":182},"list",{"type":65,"value":184},", ",{"type":59,"tag":80,"props":186,"children":188},{"className":187},[],[189],{"type":65,"value":190},"get",{"type":65,"value":184},{"type":59,"tag":80,"props":193,"children":195},{"className":194},[],[196],{"type":65,"value":197},"update",{"type":65,"value":199},", and ",{"type":59,"tag":80,"props":201,"children":203},{"className":202},[],[204],{"type":65,"value":205},"delete",{"type":65,"value":207},".",{"type":59,"tag":209,"props":210,"children":212},"h2",{"id":211},"concepts",[213],{"type":65,"value":214},"Concepts",{"type":59,"tag":216,"props":217,"children":218},"ul",{},[219,230,261,279,289],{"type":59,"tag":220,"props":221,"children":222},"li",{},[223,228],{"type":59,"tag":76,"props":224,"children":225},{},[226],{"type":65,"value":227},"AI Integration",{"type":65,"value":229}," = stored LLM provider credentials registered in Arize; used by evaluators to call a judge model and by other Arize features that need to invoke an LLM on your behalf",{"type":59,"tag":220,"props":231,"children":232},{},[233,238,240,246,247,252,253,259],{"type":59,"tag":76,"props":234,"children":235},{},[236],{"type":65,"value":237},"Provider",{"type":65,"value":239}," = the LLM service backing the integration (e.g., ",{"type":59,"tag":80,"props":241,"children":243},{"className":242},[],[244],{"type":65,"value":245},"openAI",{"type":65,"value":184},{"type":59,"tag":80,"props":248,"children":250},{"className":249},[],[251],{"type":65,"value":19},{"type":65,"value":184},{"type":59,"tag":80,"props":254,"children":256},{"className":255},[],[257],{"type":65,"value":258},"awsBedrock",{"type":65,"value":260},")",{"type":59,"tag":220,"props":262,"children":263},{},[264,269,271,277],{"type":59,"tag":76,"props":265,"children":266},{},[267],{"type":65,"value":268},"Integration ID",{"type":65,"value":270}," = a base64-encoded global identifier for an integration (e.g., ",{"type":59,"tag":80,"props":272,"children":274},{"className":273},[],[275],{"type":65,"value":276},"TGxtSW50ZWdyYXRpb246MTI6YUJjRA==",{"type":65,"value":278},"); required for evaluator creation and other downstream operations",{"type":59,"tag":220,"props":280,"children":281},{},[282,287],{"type":59,"tag":76,"props":283,"children":284},{},[285],{"type":65,"value":286},"Scoping",{"type":65,"value":288}," = visibility rules controlling which spaces or users can use an integration",{"type":59,"tag":220,"props":290,"children":291},{},[292,297,299,305,307,313,315,321],{"type":59,"tag":76,"props":293,"children":294},{},[295],{"type":65,"value":296},"Auth type",{"type":65,"value":298}," = how Arize authenticates with the provider: ",{"type":59,"tag":80,"props":300,"children":302},{"className":301},[],[303],{"type":65,"value":304},"default",{"type":65,"value":306}," (provider API key), ",{"type":59,"tag":80,"props":308,"children":310},{"className":309},[],[311],{"type":65,"value":312},"proxy_with_headers",{"type":65,"value":314}," (proxy via custom headers), or ",{"type":59,"tag":80,"props":316,"children":318},{"className":317},[],[319],{"type":65,"value":320},"bearer_token",{"type":65,"value":322}," (bearer token auth)",{"type":59,"tag":209,"props":324,"children":326},{"id":325},"prerequisites",[327],{"type":65,"value":328},"Prerequisites",{"type":59,"tag":72,"props":330,"children":331},{},[332,334,340],{"type":65,"value":333},"Proceed directly with the task — run the ",{"type":59,"tag":80,"props":335,"children":337},{"className":336},[],[338],{"type":65,"value":339},"ax",{"type":65,"value":341}," command you need. 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If the profile is missing or the API key is wrong, follow ",{"type":59,"tag":369,"props":394,"children":396},{"href":395},"references\u002Fax-profiles.md",[397],{"type":65,"value":395},{"type":65,"value":399}," to create\u002Fupdate it. 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Do NOT trigger on: (1) manual prompt drafting (use `playground`), (2) running or comparing experiments themselves (use `experiments`), (3) cross-trace failure diagnosis with no evaluator in scope (use `debug-trace`).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3256,3257,3258],{"name":3215,"slug":3216,"type":16},{"name":21,"slug":22,"type":16},{"name":3259,"slug":3260,"type":16},"Testing","testing","2026-07-31T05:58:09.13624",{"slug":42,"name":42,"fn":3263,"description":3264,"org":3265,"tags":3266,"stars":3223,"repoUrl":3224,"updatedAt":3271},"run and compare dataset-backed experiments","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\u002Flatency\u002Fcost, or decide whether a change actually helped. Running a prompt over a dataset is implicitly an experiment — load this skill when dataset-backed work begins, before authoring evaluators for the experiment and before starting the recorded run, not only when reading results. Do NOT trigger on: (1) manual prompt drafting with no dataset-backed evaluation in scope (use `playground`), (2) authoring or refining an evaluator's logic or rubric (use `evaluators`), (3) cross-trace failure diagnosis with no experiment in scope (use `debug-trace`).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3267,3268,3269,3270],{"name":3235,"slug":41,"type":16},{"name":3215,"slug":3216,"type":16},{"name":21,"slug":22,"type":16},{"name":3259,"slug":3260,"type":16},"2026-07-12T08:08:11.691477",{"slug":3273,"name":3273,"fn":3274,"description":3275,"org":3276,"tags":3277,"stars":3223,"repoUrl":3224,"updatedAt":3285},"phoenix-graphql","query Phoenix API with GraphQL","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 eliminate the need for introspection; (2) when the user asks for help writing GraphQL queries for their own scripts, tools, or integrations against Phoenix — it covers the endpoint, authentication, and client examples.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3278,3281,3282],{"name":3279,"slug":3280,"type":16},"Analytics","analytics",{"name":3232,"slug":3233,"type":16},{"name":3283,"slug":3284,"type":16},"GraphQL","graphql","2026-07-12T08:08:17.163493",{"slug":3287,"name":3287,"fn":3288,"description":3289,"org":3290,"tags":3291,"stars":3223,"repoUrl":3224,"updatedAt":3295},"playground","author and iterate on prompts in Phoenix","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3292,3293,3294],{"name":3215,"slug":3216,"type":16},{"name":21,"slug":22,"type":16},{"name":3259,"slug":3260,"type":16},"2026-07-12T08:08:12.920792",{"slug":3297,"name":3297,"fn":3298,"description":3299,"org":3300,"tags":3301,"stars":3223,"repoUrl":3224,"updatedAt":3306},"span-coding","analyze and code Phoenix spans","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3302,3303,3304,3305],{"name":3245,"slug":3246,"type":16},{"name":21,"slug":22,"type":16},{"name":3219,"slug":3220,"type":16},{"name":3222,"slug":44,"type":16},"2026-07-12T08:08:19.597239",{"slug":3308,"name":3308,"fn":3309,"description":3310,"org":3311,"tags":3312,"stars":29,"repoUrl":30,"updatedAt":3322},"arize-admin","manage Arize enterprise user access","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\u002FSSO 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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3313,3316,3319],{"name":3314,"slug":3315,"type":16},"CLI","cli",{"name":3317,"slug":3318,"type":16},"Operations","operations",{"name":3320,"slug":3321,"type":16},"Permissions","permissions","2026-07-22T05:37:21.991338",{"slug":4,"name":4,"fn":5,"description":6,"org":3324,"tags":3325,"stars":29,"repoUrl":30,"updatedAt":31},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3326,3327,3328,3329,3330],{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":21,"slug":22,"type":16},{"name":27,"slug":28,"type":16},{"slug":3332,"name":3332,"fn":3333,"description":3334,"org":3335,"tags":3336,"stars":29,"repoUrl":30,"updatedAt":3340},"arize-annotation","manage Arize annotation workflows","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3337,3338,3339],{"name":3232,"slug":3233,"type":16},{"name":21,"slug":22,"type":16},{"name":3219,"slug":3220,"type":16},"2026-07-22T05:37:19.010776",{"slug":3342,"name":3342,"fn":3343,"description":3344,"org":3345,"tags":3346,"stars":29,"repoUrl":30,"updatedAt":3362},"arize-compliance-audit","audit AI agents for regulatory compliance","INVOKE THIS SKILL when auditing an AI agent or LLM app for regulatory compliance. Covers EU AI Act, GPAI Code of Practice, GDPR, NIST AI RMF, Colorado AI Act, HIPAA, and ISO 42001. Scans the codebase for compliance gaps, cross-references Arize instrumentation for audit trail coverage, and produces an actionable remediation checklist tailored to the selected frameworks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3347,3350,3353,3356,3359],{"name":3348,"slug":3349,"type":16},"Audit","audit",{"name":3351,"slug":3352,"type":16},"Compliance","compliance",{"name":3354,"slug":3355,"type":16},"GDPR","gdpr",{"name":3357,"slug":3358,"type":16},"Legal","legal",{"name":3360,"slug":3361,"type":16},"Security","security","2026-07-19T05:39:42.632738",23,{"items":3365,"total":2723},[3366,3372,3380,3386,3394,3407,3416],{"slug":3308,"name":3308,"fn":3309,"description":3310,"org":3367,"tags":3368,"stars":29,"repoUrl":30,"updatedAt":3322},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3369,3370,3371],{"name":3314,"slug":3315,"type":16},{"name":3317,"slug":3318,"type":16},{"name":3320,"slug":3321,"type":16},{"slug":4,"name":4,"fn":5,"description":6,"org":3373,"tags":3374,"stars":29,"repoUrl":30,"updatedAt":31},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3375,3376,3377,3378,3379],{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":21,"slug":22,"type":16},{"name":27,"slug":28,"type":16},{"slug":3332,"name":3332,"fn":3333,"description":3334,"org":3381,"tags":3382,"stars":29,"repoUrl":30,"updatedAt":3340},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3383,3384,3385],{"name":3232,"slug":3233,"type":16},{"name":21,"slug":22,"type":16},{"name":3219,"slug":3220,"type":16},{"slug":3342,"name":3342,"fn":3343,"description":3344,"org":3387,"tags":3388,"stars":29,"repoUrl":30,"updatedAt":3362},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3389,3390,3391,3392,3393],{"name":3348,"slug":3349,"type":16},{"name":3351,"slug":3352,"type":16},{"name":3354,"slug":3355,"type":16},{"name":3357,"slug":3358,"type":16},{"name":3360,"slug":3361,"type":16},{"slug":3395,"name":3395,"fn":3396,"description":3397,"org":3398,"tags":3399,"stars":29,"repoUrl":30,"updatedAt":3406},"arize-dataset","manage Arize datasets and examples","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3400,3403,3404,3405],{"name":3401,"slug":3402,"type":16},"Data Engineering","data-engineering",{"name":3235,"slug":41,"type":16},{"name":3215,"slug":3216,"type":16},{"name":21,"slug":22,"type":16},"2026-07-22T05:37:20.943718",{"slug":3158,"name":3158,"fn":3408,"description":3409,"org":3410,"tags":3411,"stars":29,"repoUrl":30,"updatedAt":3415},"configure and run Arize evaluations","Handles LLM-as-judge and code evaluator workflows on Arize including creating\u002Fupdating 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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3412,3413,3414],{"name":3215,"slug":3216,"type":16},{"name":21,"slug":22,"type":16},{"name":3219,"slug":3220,"type":16},"2026-07-25T05:32:37.552903",{"slug":3173,"name":3173,"fn":3417,"description":3418,"org":3419,"tags":3420,"stars":29,"repoUrl":30,"updatedAt":3424},"run and analyze Arize experiments","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\u002FB test models, or measure accuracy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3421,3422,3423],{"name":3279,"slug":3280,"type":16},{"name":3215,"slug":3216,"type":16},{"name":21,"slug":22,"type":16},"2026-07-31T05:53:44.725539"]