[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-arize-arize-annotation":3,"mdc--gs89gb-key":44,"related-org-arize-arize-annotation":3452,"related-repo-arize-arize-annotation":3613},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":39,"sourceUrl":42,"mdContent":43},"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},"arize","Arize AI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Farize.jpg","Arize-ai",[13,17,20],{"name":14,"slug":15,"type":16},"Observability","observability","tag",{"name":18,"slug":19,"type":16},"LLM","llm",{"name":21,"slug":22,"type":16},"Data Analysis","data-analysis",38,"https:\u002F\u002Fgithub.com\u002FArize-ai\u002Farize-skills","2026-07-22T05:37:19.010776",null,5,[29,30,31,8,32,33,34,35,36,37,38],"agent-skills","ai-agents","ai-observability","claude-code","codex","cursor","datasets","experiments","llmops","tracing",{"repoUrl":24,"stars":23,"forks":27,"topics":40,"description":41},[29,30,31,8,32,33,34,35,36,37,38],"Agent skills for Arize — datasets, experiments, and traces via the ax CLI","https:\u002F\u002Fgithub.com\u002FArize-ai\u002Farize-skills\u002Ftree\u002FHEAD\u002Fskills\u002Farize-annotation","---\nname: arize-annotation\ndescription: 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.\nmetadata:\n  author: arize\n  version: \"1.0\"\ncompatibility: Requires the ax CLI and a configured Arize profile.\n---\n\n# Arize Annotation Skill\n\n> **`SPACE`** — All `--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\nThis skill covers **annotation configs** (the label schema) and **annotation queues** (human review workflows), as well as programmatically annotating project spans via the Python SDK.\n\n**Direction:** Human labeling in Arize attaches values defined by configs to **spans**, **dataset examples**, **experiment-related records**, and **queue items** in the product UI. This skill covers: `ax annotation-configs`, `ax annotation-queues`, and bulk span updates with `ArizeClient.spans.update_annotations`.\n\n---\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- **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## Concepts\n\n### What is an Annotation Config?\n\nAn **annotation config** defines the schema for a single type of human feedback label. Before anyone can annotate a span, dataset record, experiment output, or queue item, a config must exist for that label in the space.\n\n| Field | Description |\n|-------|-------------|\n| **Name** | Descriptive identifier (e.g. `Correctness`, `Helpfulness`). Must be unique within the space. |\n| **Type** | `categorical` (pick from a list), `continuous` (numeric range), or `freeform` (free text). |\n| **Values** | For categorical: array of `{\"label\": str, \"score\": number}` pairs. |\n| **Min\u002FMax Score** | For continuous: numeric bounds. |\n| **Optimization Direction** | Whether higher scores are better (`maximize`) or worse (`minimize`). Used to render trends in the UI. |\n\n### Where labels get applied (surfaces)\n\n| Surface | Typical path |\n|---------|----------------|\n| **Project spans** | Python SDK `spans.update_annotations` (below) and\u002For the Arize UI |\n| **Dataset examples** | Arize UI (human labeling flows); configs must exist in the space |\n| **Experiment outputs** | Often reviewed alongside datasets or traces in the UI — see arize-experiment, arize-dataset |\n| **Annotation queue items** | `ax annotation-queues` CLI (below) and\u002For the Arize UI; configs must exist |\n\nAlways ensure the relevant **annotation config** exists in the space before expecting labels to persist.\n\n---\n\n## Basic CRUD: Annotation Configs\n\n### List\n\n```bash\nax annotation-configs list --space SPACE\nax annotation-configs list --space SPACE -o json\nax annotation-configs list --space SPACE --limit 20\nax annotation-configs list --space SPACE --name \"Correctness\"   # substring filter\n```\n\n### Create — Categorical\n\nCategorical configs present a fixed set of labels for reviewers to choose from.\n\n```bash\nax annotation-configs create \\\n  --name \"Correctness\" \\\n  --space SPACE \\\n  --type categorical \\\n  --value correct \\\n  --value incorrect \\\n  --optimization-direction maximize\n```\n\nCommon binary label pairs:\n- `correct` \u002F `incorrect`\n- `helpful` \u002F `unhelpful`\n- `safe` \u002F `unsafe`\n- `relevant` \u002F `irrelevant`\n- `pass` \u002F `fail`\n\n### Create — Continuous\n\nContinuous configs let reviewers enter a numeric score within a defined range.\n\n```bash\nax annotation-configs create \\\n  --name \"Quality Score\" \\\n  --space SPACE \\\n  --type continuous \\\n  --min-score 0 \\\n  --max-score 10 \\\n  --optimization-direction maximize\n```\n\n### Create — Freeform\n\nFreeform configs collect open-ended text feedback. No additional flags needed beyond name, space, and type.\n\n```bash\nax annotation-configs create \\\n  --name \"Reviewer Notes\" \\\n  --space SPACE \\\n  --type freeform\n```\n\n### Get\n\n```bash\nax annotation-configs get NAME_OR_ID\nax annotation-configs get NAME_OR_ID -o json\nax annotation-configs get NAME_OR_ID --space SPACE   # required when using name instead of ID\n```\n\n### Delete\n\n```bash\nax annotation-configs delete NAME_OR_ID\nax annotation-configs delete NAME_OR_ID --space SPACE   # required when using name instead of ID\nax annotation-configs delete NAME_OR_ID --force   # skip confirmation\n```\n\n**Note:** Deletion is irreversible. Any annotation queue associations to this config are also removed in the product (queues may remain; fix associations in the Arize UI if needed).\n\n---\n\n## Annotation Queues: `ax annotation-queues`\n\nAnnotation queues route records (spans, dataset examples, experiment runs) to human reviewers. Each queue is linked to one or more annotation configs that define what labels reviewers can apply.\n\n### List \u002F Get\n\n```bash\nax annotation-queues list --space SPACE\nax annotation-queues list --space SPACE -o json\nax annotation-queues list --space SPACE --name \"Review\"   # substring filter\n\nax annotation-queues get NAME_OR_ID --space SPACE\nax annotation-queues get NAME_OR_ID --space SPACE -o json\n```\n\n### Create\n\nAt least one `--annotation-config-id` is required.\n\n```bash\nax annotation-queues create \\\n  --name \"Correctness Review\" \\\n  --space SPACE \\\n  --annotation-config-id CONFIG_ID \\\n  --annotator-email reviewer@example.com \\\n  --instructions \"Label each response as correct or incorrect.\" \\\n  --assignment-method all   # or: random\n```\n\nRepeat `--annotation-config-id` and `--annotator-email` to attach multiple configs or reviewers.\n\n### Update\n\nList flags (`--annotation-config-id`, `--annotator-email`) **fully replace** existing values when provided — pass all desired values, not just the new ones.\n\n```bash\nax annotation-queues update NAME_OR_ID --space SPACE --name \"New Name\"\nax annotation-queues update NAME_OR_ID --space SPACE --instructions \"Updated instructions\"\nax annotation-queues update NAME_OR_ID --space SPACE \\\n  --annotation-config-id CONFIG_ID_A \\\n  --annotation-config-id CONFIG_ID_B\n```\n\n### Delete\n\n```bash\nax annotation-queues delete NAME_OR_ID --space SPACE\nax annotation-queues delete NAME_OR_ID --space SPACE --force   # skip confirmation\n```\n\n### List Records\n\n```bash\nax annotation-queues list-records NAME_OR_ID --space SPACE\nax annotation-queues list-records NAME_OR_ID --space SPACE --limit 50 -o json\n```\n\n### Submit an Annotation for a Record\n\nAnnotations are upserted by config name — call once per annotation config. Supply at least one of `--score`, `--label`, or `--text`.\n\n```bash\nax annotation-queues annotate-record NAME_OR_ID RECORD_ID \\\n  --annotation-name \"Correctness\" \\\n  --label \"correct\" \\\n  --space SPACE\n\nax annotation-queues annotate-record NAME_OR_ID RECORD_ID \\\n  --annotation-name \"Quality Score\" \\\n  --score 8.5 \\\n  --text \"Response was accurate but slightly verbose.\" \\\n  --space SPACE\n```\n\n### Assign a Record\n\nAssign users to review a specific record:\n\n```bash\nax annotation-queues assign-record NAME_OR_ID RECORD_ID --space SPACE\n```\n\n### Delete Records\n\n```bash\nax annotation-queues delete-records NAME_OR_ID --space SPACE\n```\n\n---\n\n## Applying Annotations to Spans (Python SDK)\n\nUse the Python SDK to bulk-apply annotations to **project spans** when you already have labels (e.g., from a review export or an external labeling tool).\n\n```python\nimport pandas as pd\nfrom arize import ArizeClient\n\nimport os\n\nclient = ArizeClient(api_key=os.environ[\"ARIZE_API_KEY\"])\n\n# Build a DataFrame with annotation columns\n# Required: context.span_id + at least one annotation.\u003Cname>.label or annotation.\u003Cname>.score\nannotations_df = pd.DataFrame([\n    {\n        \"context.span_id\": \"span_001\",\n        \"annotation.Correctness.label\": \"correct\",\n        \"annotation.Correctness.updated_by\": \"reviewer@example.com\",\n    },\n    {\n        \"context.span_id\": \"span_002\",\n        \"annotation.Correctness.label\": \"incorrect\",\n        \"annotation.Correctness.updated_by\": \"reviewer@example.com\",\n    },\n])\n\nresponse = client.spans.update_annotations(\n    space_id=os.environ[\"ARIZE_SPACE_ID\"],\n    project_name=\"your-project\",\n    dataframe=annotations_df,\n    validate=True,\n)\n```\n\n**DataFrame column schema:**\n\n| Column | Required | Description |\n|--------|----------|-------------|\n| `context.span_id` | yes | The span to annotate |\n| `annotation.\u003Cname>.label` | one of | Categorical or freeform label |\n| `annotation.\u003Cname>.score` | one of | Numeric score |\n| `annotation.\u003Cname>.updated_by` | no | Annotator identifier (email or name) |\n| `annotation.\u003Cname>.updated_at` | no | Timestamp in milliseconds since epoch |\n| `annotation.notes` | no | Freeform notes on the span |\n\n**Limitation:** Annotations apply only to spans within 31 days prior to submission.\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 at https:\u002F\u002Fapp.arize.com\u002Fadmin > API Keys |\n| `Annotation config not found` | `ax annotation-configs list --space SPACE` (or use `ax annotation-configs get NAME_OR_ID --space SPACE`) |\n| `409 Conflict on create` | Name already exists in the space. Use a different name or get the existing config ID. |\n| Queue not found | `ax annotation-queues list --space SPACE`; verify the queue name or ID |\n| Record not appearing in queue | Ensure the annotation config linked to the queue exists; check `ax annotation-configs list --space SPACE` |\n| Span SDK errors or missing spans | Confirm `project_name`, `space_id`, and span IDs; use arize-trace to export spans |\n\n---\n\n## Batch Annotate via CLI\n\nThe `ax` CLI provides batch annotation commands for writing annotations at scale without the Python SDK. All commands accept a file (CSV, JSON, JSONL, or Parquet) with up to **1000 annotations per request** and use **upsert semantics** (existing annotations with the same key are updated; new ones are created).\n\n| Resource | Command | Skill |\n|----------|---------|-------|\n| Spans | `ax spans annotate PROJECT --file annotations.json` | **arize-trace** |\n| Dataset examples | `ax datasets annotate-examples NAME_OR_ID --file annotations.json` | **arize-dataset** |\n| Experiment runs | `ax experiments annotate-runs NAME_OR_ID --file annotations.json --dataset DATASET` | **arize-experiment** |\n\nAll three commands support `--space SPACE`. See the linked skills for full flag tables and file format details.\n\n---\n\n## Related Skills\n\n- **arize-trace**: Export spans to find span IDs and time ranges; batch annotate spans via `ax spans annotate`\n- **arize-dataset**: Find dataset IDs and example IDs; batch annotate examples via `ax datasets annotate-examples`\n- **arize-evaluator**: Automated LLM-as-judge alongside human annotation\n- **arize-experiment**: Experiments tied to datasets and evaluation workflows; batch annotate runs via `ax experiments annotate-runs`\n- **arize-prompts**: Manage prompt templates; annotate prompt outputs for quality tracking\n- **arize-link**: Deep links to annotation configs and queues in the Arize UI\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":45,"body":49},{"name":4,"description":6,"metadata":46,"compatibility":48},{"author":8,"version":47},"1.0","Requires the ax CLI and a configured Arize profile.",{"type":50,"children":51},"root",[52,61,135,154,213,217,224,237,249,357,360,366,373,385,554,560,659,670,673,679,685,848,854,859,993,998,1082,1088,1093,1219,1225,1230,1307,1313,1401,1407,1494,1504,1507,1518,1523,1529,1706,1712,1725,1867,1887,1893,1918,2075,2080,2149,2155,2234,2240,2267,2475,2481,2486,2525,2531,2566,2569,2575,2587,2831,2839,2994,3004,3007,3013,3191,3194,3200,3226,3327,3340,3343,3349,3427,3430,3436,3446],{"type":53,"tag":54,"props":55,"children":57},"element","h1",{"id":56},"arize-annotation-skill",[58],{"type":59,"value":60},"text","Arize Annotation Skill",{"type":53,"tag":62,"props":63,"children":64},"blockquote",{},[65],{"type":53,"tag":66,"props":67,"children":68},"p",{},[69,80,82,88,90,96,98,103,105,111,113,118,119,125,127,133],{"type":53,"tag":70,"props":71,"children":72},"strong",{},[73],{"type":53,"tag":74,"props":75,"children":77},"code",{"className":76},[],[78],{"type":59,"value":79},"SPACE",{"type":59,"value":81}," — All ",{"type":53,"tag":74,"props":83,"children":85},{"className":84},[],[86],{"type":59,"value":87},"--space",{"type":59,"value":89}," flags and the ",{"type":53,"tag":74,"props":91,"children":93},{"className":92},[],[94],{"type":59,"value":95},"ARIZE_SPACE",{"type":59,"value":97}," env var accept a space ",{"type":53,"tag":70,"props":99,"children":100},{},[101],{"type":59,"value":102},"name",{"type":59,"value":104}," (e.g., ",{"type":53,"tag":74,"props":106,"children":108},{"className":107},[],[109],{"type":59,"value":110},"my-workspace",{"type":59,"value":112},") or a base64 space ",{"type":53,"tag":70,"props":114,"children":115},{},[116],{"type":59,"value":117},"ID",{"type":59,"value":104},{"type":53,"tag":74,"props":120,"children":122},{"className":121},[],[123],{"type":59,"value":124},"U3BhY2U6...",{"type":59,"value":126},"). Find yours with ",{"type":53,"tag":74,"props":128,"children":130},{"className":129},[],[131],{"type":59,"value":132},"ax spaces list",{"type":59,"value":134},".",{"type":53,"tag":66,"props":136,"children":137},{},[138,140,145,147,152],{"type":59,"value":139},"This skill covers ",{"type":53,"tag":70,"props":141,"children":142},{},[143],{"type":59,"value":144},"annotation configs",{"type":59,"value":146}," (the label schema) and ",{"type":53,"tag":70,"props":148,"children":149},{},[150],{"type":59,"value":151},"annotation queues",{"type":59,"value":153}," (human review workflows), as well as programmatically annotating project spans via the Python SDK.",{"type":53,"tag":66,"props":155,"children":156},{},[157,162,164,169,171,176,177,182,184,189,191,197,198,204,206,212],{"type":53,"tag":70,"props":158,"children":159},{},[160],{"type":59,"value":161},"Direction:",{"type":59,"value":163}," Human labeling in Arize attaches values defined by configs to ",{"type":53,"tag":70,"props":165,"children":166},{},[167],{"type":59,"value":168},"spans",{"type":59,"value":170},", ",{"type":53,"tag":70,"props":172,"children":173},{},[174],{"type":59,"value":175},"dataset examples",{"type":59,"value":170},{"type":53,"tag":70,"props":178,"children":179},{},[180],{"type":59,"value":181},"experiment-related records",{"type":59,"value":183},", and ",{"type":53,"tag":70,"props":185,"children":186},{},[187],{"type":59,"value":188},"queue items",{"type":59,"value":190}," in the product UI. 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Do NOT check versions, env vars, or profiles upfront.",{"type":53,"tag":66,"props":238,"children":239},{},[240,242,247],{"type":59,"value":241},"If an ",{"type":53,"tag":74,"props":243,"children":245},{"className":244},[],[246],{"type":59,"value":234},{"type":59,"value":248}," command fails, troubleshoot based on the error:",{"type":53,"tag":250,"props":251,"children":252},"ul",{},[253,271,306,318],{"type":53,"tag":254,"props":255,"children":256},"li",{},[257,263,265],{"type":53,"tag":74,"props":258,"children":260},{"className":259},[],[261],{"type":59,"value":262},"command not found",{"type":59,"value":264}," or version error → see ",{"type":53,"tag":266,"props":267,"children":269},"a",{"href":268},"references\u002Fax-setup.md",[270],{"type":59,"value":268},{"type":53,"tag":254,"props":272,"children":273},{},[274,280,282,288,290,295,297,304],{"type":53,"tag":74,"props":275,"children":277},{"className":276},[],[278],{"type":59,"value":279},"401 Unauthorized",{"type":59,"value":281}," \u002F missing API key → run ",{"type":53,"tag":74,"props":283,"children":285},{"className":284},[],[286],{"type":59,"value":287},"ax profiles show",{"type":59,"value":289}," to inspect the current profile. 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See the linked skills for full flag tables and file format details.",{"type":53,"tag":214,"props":3341,"children":3342},{},[],{"type":53,"tag":218,"props":3344,"children":3346},{"id":3345},"related-skills",[3347],{"type":59,"value":3348},"Related Skills",{"type":53,"tag":250,"props":3350,"children":3351},{},[3352,3367,3382,3392,3407,3417],{"type":53,"tag":254,"props":3353,"children":3354},{},[3355,3359,3361],{"type":53,"tag":70,"props":3356,"children":3357},{},[3358],{"type":59,"value":3277},{"type":59,"value":3360},": Export spans to find span IDs and time ranges; batch annotate spans via ",{"type":53,"tag":74,"props":3362,"children":3364},{"className":3363},[],[3365],{"type":59,"value":3366},"ax spans annotate",{"type":53,"tag":254,"props":3368,"children":3369},{},[3370,3374,3376],{"type":53,"tag":70,"props":3371,"children":3372},{},[3373],{"type":59,"value":3301},{"type":59,"value":3375},": Find dataset IDs and example IDs; batch annotate examples via ",{"type":53,"tag":74,"props":3377,"children":3379},{"className":3378},[],[3380],{"type":59,"value":3381},"ax datasets annotate-examples",{"type":53,"tag":254,"props":3383,"children":3384},{},[3385,3390],{"type":53,"tag":70,"props":3386,"children":3387},{},[3388],{"type":59,"value":3389},"arize-evaluator",{"type":59,"value":3391},": Automated LLM-as-judge alongside human annotation",{"type":53,"tag":254,"props":3393,"children":3394},{},[3395,3399,3401],{"type":53,"tag":70,"props":3396,"children":3397},{},[3398],{"type":59,"value":3326},{"type":59,"value":3400},": Experiments tied to datasets and evaluation workflows; batch annotate runs via ",{"type":53,"tag":74,"props":3402,"children":3404},{"className":3403},[],[3405],{"type":59,"value":3406},"ax experiments annotate-runs",{"type":53,"tag":254,"props":3408,"children":3409},{},[3410,3415],{"type":53,"tag":70,"props":3411,"children":3412},{},[3413],{"type":59,"value":3414},"arize-prompts",{"type":59,"value":3416},": Manage prompt templates; annotate prompt outputs for quality tracking",{"type":53,"tag":254,"props":3418,"children":3419},{},[3420,3425],{"type":53,"tag":70,"props":3421,"children":3422},{},[3423],{"type":59,"value":3424},"arize-link",{"type":59,"value":3426},": Deep links to annotation configs and queues in the Arize UI",{"type":53,"tag":214,"props":3428,"children":3429},{},[],{"type":53,"tag":218,"props":3431,"children":3433},{"id":3432},"save-credentials-for-future-use",[3434],{"type":59,"value":3435},"Save Credentials for Future Use",{"type":53,"tag":66,"props":3437,"children":3438},{},[3439,3440,3444],{"type":59,"value":3051},{"type":53,"tag":266,"props":3441,"children":3442},{"href":292},[3443],{"type":59,"value":292},{"type":59,"value":3445}," § Save Credentials for Future Use.",{"type":53,"tag":3447,"props":3448,"children":3449},"style",{},[3450],{"type":59,"value":3451},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":3453,"total":2779},[3454,3470,3480,3492,3504,3514,3528,3538,3549,3565,3585,3591],{"slug":3455,"name":3455,"fn":3456,"description":3457,"org":3458,"tags":3459,"stars":3467,"repoUrl":3468,"updatedAt":3469},"annotate-spans","annotate LLM spans and traces","Write effective, consistent annotations on LLM\u002Fagent 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, score, or review spans\u002Ftraces, build a failure taxonomy, or set up human\u002FLLM review. Do NOT load for: pure analysis with no intent to save feedback (use debug-trace), latency or cost statistics, or prompt authoring (use playground).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3460,3463,3464,3465],{"name":3461,"slug":3462,"type":16},"Evals","evals",{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":3466,"slug":38,"type":16},"Tracing",10513,"https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix","2026-07-12T08:08:14.140984",{"slug":35,"name":35,"fn":3471,"description":3472,"org":3473,"tags":3474,"stars":3467,"repoUrl":3468,"updatedAt":3479},"reason about Phoenix dataset structure","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 datasets relate to experiments and evals. This skill governs the judgment; any tool descriptions govern the mechanics.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3475,3476,3478],{"name":21,"slug":22,"type":16},{"name":3477,"slug":35,"type":16},"Datasets",{"name":3461,"slug":3462,"type":16},"2026-07-12T08:08:21.695457",{"slug":3481,"name":3481,"fn":3482,"description":3483,"org":3484,"tags":3485,"stars":3467,"repoUrl":3468,"updatedAt":3491},"debug-trace","diagnose failures using trace investigation","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3486,3489,3490],{"name":3487,"slug":3488,"type":16},"Debugging","debugging",{"name":14,"slug":15,"type":16},{"name":3466,"slug":38,"type":16},"2026-07-12T08:08:10.44243",{"slug":3493,"name":3493,"fn":3494,"description":3495,"org":3496,"tags":3497,"stars":3467,"repoUrl":3468,"updatedAt":3503},"evaluators","author and refine Phoenix evaluators","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. 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},[3498,3499,3500],{"name":3461,"slug":3462,"type":16},{"name":18,"slug":19,"type":16},{"name":3501,"slug":3502,"type":16},"Testing","testing","2026-07-31T05:58:09.13624",{"slug":36,"name":36,"fn":3505,"description":3506,"org":3507,"tags":3508,"stars":3467,"repoUrl":3468,"updatedAt":3513},"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},[3509,3510,3511,3512],{"name":3477,"slug":35,"type":16},{"name":3461,"slug":3462,"type":16},{"name":18,"slug":19,"type":16},{"name":3501,"slug":3502,"type":16},"2026-07-12T08:08:11.691477",{"slug":3515,"name":3515,"fn":3516,"description":3517,"org":3518,"tags":3519,"stars":3467,"repoUrl":3468,"updatedAt":3527},"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},[3520,3523,3524],{"name":3521,"slug":3522,"type":16},"Analytics","analytics",{"name":21,"slug":22,"type":16},{"name":3525,"slug":3526,"type":16},"GraphQL","graphql","2026-07-12T08:08:17.163493",{"slug":3529,"name":3529,"fn":3530,"description":3531,"org":3532,"tags":3533,"stars":3467,"repoUrl":3468,"updatedAt":3537},"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},[3534,3535,3536],{"name":3461,"slug":3462,"type":16},{"name":18,"slug":19,"type":16},{"name":3501,"slug":3502,"type":16},"2026-07-12T08:08:12.920792",{"slug":3539,"name":3539,"fn":3540,"description":3541,"org":3542,"tags":3543,"stars":3467,"repoUrl":3468,"updatedAt":3548},"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},[3544,3545,3546,3547],{"name":3487,"slug":3488,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":3466,"slug":38,"type":16},"2026-07-12T08:08:19.597239",{"slug":3550,"name":3550,"fn":3551,"description":3552,"org":3553,"tags":3554,"stars":23,"repoUrl":24,"updatedAt":3564},"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},[3555,3558,3561],{"name":3556,"slug":3557,"type":16},"CLI","cli",{"name":3559,"slug":3560,"type":16},"Operations","operations",{"name":3562,"slug":3563,"type":16},"Permissions","permissions","2026-07-22T05:37:21.991338",{"slug":3566,"name":3566,"fn":3567,"description":3568,"org":3569,"tags":3570,"stars":23,"repoUrl":24,"updatedAt":3584},"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},[3571,3574,3577,3580,3581],{"name":3572,"slug":3573,"type":16},"Anthropic","anthropic",{"name":3575,"slug":3576,"type":16},"Azure","azure",{"name":3578,"slug":3579,"type":16},"Integrations","integrations",{"name":18,"slug":19,"type":16},{"name":3582,"slug":3583,"type":16},"OpenAI","openai","2026-07-22T05:37:23.90468",{"slug":4,"name":4,"fn":5,"description":6,"org":3586,"tags":3587,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3588,3589,3590],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":3592,"name":3592,"fn":3593,"description":3594,"org":3595,"tags":3596,"stars":23,"repoUrl":24,"updatedAt":3612},"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},[3597,3600,3603,3606,3609],{"name":3598,"slug":3599,"type":16},"Audit","audit",{"name":3601,"slug":3602,"type":16},"Compliance","compliance",{"name":3604,"slug":3605,"type":16},"GDPR","gdpr",{"name":3607,"slug":3608,"type":16},"Legal","legal",{"name":3610,"slug":3611,"type":16},"Security","security","2026-07-19T05:39:42.632738",{"items":3614,"total":2693},[3615,3621,3629,3635,3643,3655,3664],{"slug":3550,"name":3550,"fn":3551,"description":3552,"org":3616,"tags":3617,"stars":23,"repoUrl":24,"updatedAt":3564},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3618,3619,3620],{"name":3556,"slug":3557,"type":16},{"name":3559,"slug":3560,"type":16},{"name":3562,"slug":3563,"type":16},{"slug":3566,"name":3566,"fn":3567,"description":3568,"org":3622,"tags":3623,"stars":23,"repoUrl":24,"updatedAt":3584},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3624,3625,3626,3627,3628],{"name":3572,"slug":3573,"type":16},{"name":3575,"slug":3576,"type":16},{"name":3578,"slug":3579,"type":16},{"name":18,"slug":19,"type":16},{"name":3582,"slug":3583,"type":16},{"slug":4,"name":4,"fn":5,"description":6,"org":3630,"tags":3631,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3632,3633,3634],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":3592,"name":3592,"fn":3593,"description":3594,"org":3636,"tags":3637,"stars":23,"repoUrl":24,"updatedAt":3612},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[3638,3639,3640,3641,3642],{"name":3598,"slug":3599,"type":16},{"name":3601,"slug":3602,"type":16},{"name":3604,"slug":3605,"type":16},{"name":3607,"slug":3608,"type":16},{"name":3610,"slug":3611,"type":16},{"slug":3301,"name":3301,"fn":3644,"description":3645,"org":3646,"tags":3647,"stars":23,"repoUrl":24,"updatedAt":3654},"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},[3648,3651,3652,3653],{"name":3649,"slug":3650,"type":16},"Data Engineering","data-engineering",{"name":3477,"slug":35,"type":16},{"name":3461,"slug":3462,"type":16},{"name":18,"slug":19,"type":16},"2026-07-22T05:37:20.943718",{"slug":3389,"name":3389,"fn":3656,"description":3657,"org":3658,"tags":3659,"stars":23,"repoUrl":24,"updatedAt":3663},"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},[3660,3661,3662],{"name":3461,"slug":3462,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},"2026-07-25T05:32:37.552903",{"slug":3326,"name":3326,"fn":3665,"description":3666,"org":3667,"tags":3668,"stars":23,"repoUrl":24,"updatedAt":3672},"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},[3669,3670,3671],{"name":3521,"slug":3522,"type":16},{"name":3461,"slug":3462,"type":16},{"name":18,"slug":19,"type":16},"2026-07-31T05:53:44.725539"]