[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-arize-arize-dataset":3,"mdc--ofre80-key":46,"related-repo-arize-arize-dataset":5343,"related-org-arize-arize-dataset":5446},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":41,"sourceUrl":44,"mdContent":45},"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},"arize","Arize AI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Farize.jpg","Arize-ai",[13,17,20,23],{"name":14,"slug":15,"type":16},"Datasets","datasets","tag",{"name":18,"slug":19,"type":16},"LLM","llm",{"name":21,"slug":22,"type":16},"Data Engineering","data-engineering",{"name":24,"slug":25,"type":16},"Evals","evals",38,"https:\u002F\u002Fgithub.com\u002FArize-ai\u002Farize-skills","2026-07-22T05:37:20.943718",null,5,[32,33,34,8,35,36,37,15,38,39,40],"agent-skills","ai-agents","ai-observability","claude-code","codex","cursor","experiments","llmops","tracing",{"repoUrl":27,"stars":26,"forks":30,"topics":42,"description":43},[32,33,34,8,35,36,37,15,38,39,40],"Agent skills for Arize — datasets, experiments, and traces via the ax CLI","https:\u002F\u002Fgithub.com\u002FArize-ai\u002Farize-skills\u002Ftree\u002FHEAD\u002Fskills\u002Farize-dataset","---\nname: arize-dataset\ndescription: 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.\nmetadata:\n  author: arize\n  version: \"1.0\"\ncompatibility: Requires the ax CLI and a configured Arize profile.\n---\n\n# Arize Dataset 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\n## Concepts\n\n- **Dataset** = a versioned collection of examples used for evaluation and experimentation\n- **Dataset Version** = a snapshot of a dataset at a point in time; updates can be in-place or create a new version\n- **Example** = a single record in a dataset with arbitrary user-defined fields (e.g., `question`, `answer`, `context`)\n- **Space** = an organizational container; datasets belong to a space\n\nSystem-managed fields on examples (`id`, `created_at`, `updated_at`) are auto-generated by the server -- never include them in create or append payloads.\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- Project unclear → ask the user, or run `ax projects list -o json --limit 100` and present as selectable options\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## List Datasets: `ax datasets list`\n\nBrowse datasets in a space. Output goes to stdout.\n\n```bash\nax datasets list\nax datasets list --space SPACE --limit 20\nax datasets list --cursor CURSOR_TOKEN\nax datasets list -o json\n```\n\n### Flags\n\n| Flag | Type | Default | Description |\n|------|------|---------|-------------|\n| `--space` | string | from profile | Filter by space |\n| `--name, -n` | string | none | Substring filter on dataset name |\n| `--limit, -l` | int | 15 | Max results (1-100) |\n| `--cursor` | string | none | Pagination cursor from previous response |\n| `-o, --output` | string | table | Output format: table, json, csv, parquet, or file path |\n\n## Get Dataset: `ax datasets get`\n\nQuick metadata lookup -- returns dataset name, space, timestamps, and version list.\n\n```bash\nax datasets get NAME_OR_ID\nax datasets get NAME_OR_ID -o json\nax datasets get NAME_OR_ID --space SPACE   # required when using dataset name instead of ID\n```\n\n### Flags\n\n| Flag | Type | Default | Description |\n|------|------|---------|-------------|\n| `NAME_OR_ID` | string | required | Dataset name or ID (positional) |\n| `--space` | string | none | Space name or ID (required if using dataset name instead of ID) |\n| `-o, --output` | string | table | Output format |\n\n### Response fields\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `id` | string | Dataset ID |\n| `name` | string | Dataset name |\n| `space_id` | string | Space this dataset belongs to |\n| `created_at` | datetime | When the dataset was created |\n| `updated_at` | datetime | Last modification time |\n| `versions` | array | List of dataset versions (id, name, dataset_id, created_at, updated_at) |\n\n## Export Dataset: `ax datasets export`\n\nDownload all examples to a file. Use `--all` for datasets larger than 500 examples (unlimited bulk export).\n\n```bash\nax datasets export NAME_OR_ID\n# -> dataset_abc123_20260305_141500\u002Fexamples.json\n\nax datasets export NAME_OR_ID --all\nax datasets export NAME_OR_ID --version-id VERSION_ID\nax datasets export NAME_OR_ID --output-dir .\u002Fdata\nax datasets export NAME_OR_ID --stdout\nax datasets export NAME_OR_ID --stdout | jq '.[0]'\nax datasets export NAME_OR_ID --space SPACE   # required when using dataset name instead of ID\n```\n\n### Flags\n\n| Flag | Type | Default | Description |\n|------|------|---------|-------------|\n| `NAME_OR_ID` | string | required | Dataset name or ID (positional) |\n| `--space` | string | none | Space name or ID (required if using dataset name instead of ID) |\n| `--version-id` | string | latest | Export a specific dataset version |\n| `--all` | bool | false | Unlimited bulk export (use for datasets > 500 examples) |\n| `--output-dir` | string | `.` | Output directory |\n| `--stdout` | bool | false | Print JSON to stdout instead of file |\n\n**Agent auto-escalation rule:** If an export returns exactly 500 examples, the result is likely truncated — re-run with `--all` to get the full dataset.\n\n**Export completeness verification:** After exporting, confirm the row count matches what the server reports:\n```bash\n# Get the server-reported count from dataset metadata\nax datasets get DATASET_NAME --space SPACE -o json | jq '.versions[-1] | {version: .id, examples: .example_count}'\n\n# Compare to what was exported\njq 'length' dataset_*\u002Fexamples.json\n\n# If counts differ, re-export with --all\n```\n\nOutput is a JSON array of example objects. Each example has system fields (`id`, `created_at`, `updated_at`) plus all user-defined fields:\n\n```json\n[\n  {\n    \"id\": \"ex_001\",\n    \"created_at\": \"2026-01-15T10:00:00Z\",\n    \"updated_at\": \"2026-01-15T10:00:00Z\",\n    \"question\": \"What is 2+2?\",\n    \"answer\": \"4\",\n    \"topic\": \"math\"\n  }\n]\n```\n\n## Create Dataset: `ax datasets create`\n\nCreate a new dataset from a data file.\n\n```bash\nax datasets create --name \"My Dataset\" --space SPACE --file data.csv\nax datasets create --name \"My Dataset\" --space SPACE --file data.json\nax datasets create --name \"My Dataset\" --space SPACE --file data.jsonl\nax datasets create --name \"My Dataset\" --space SPACE --file data.parquet\n```\n\n### Flags\n\n| Flag | Type | Required | Description |\n|------|------|----------|-------------|\n| `--name, -n` | string | yes | Dataset name |\n| `--space` | string | yes | Space to create the dataset in |\n| `--file, -f` | path | yes | Data file: CSV, JSON, JSONL, or Parquet |\n| `-o, --output` | string | no | Output format for the returned dataset metadata |\n\n### Passing data via stdin\n\nUse `--file -` to pipe data directly — no temp file needed:\n\n```bash\necho '[{\"question\": \"What is 2+2?\", \"answer\": \"4\"}]' | ax datasets create --name \"my-dataset\" --space SPACE --file -\n\n# Or with a heredoc\nax datasets create --name \"my-dataset\" --space SPACE --file - \u003C\u003C 'EOF'\n[{\"question\": \"What is 2+2?\", \"answer\": \"4\"}]\nEOF\n```\n\nTo add rows to an existing dataset, use `ax datasets append --json '[...]'` instead — no file needed.\n\n### Supported file formats\n\n| Format | Extension | Notes |\n|--------|-----------|-------|\n| CSV | `.csv` | Column headers become field names |\n| JSON | `.json` | Array of objects |\n| JSON Lines | `.jsonl` | One object per line (NOT a JSON array) |\n| Parquet | `.parquet` | Column names become field names; preserves types |\n\n**Format gotchas:**\n- **CSV**: Loses type information — dates become strings, `null` becomes empty string. Use JSON\u002FParquet to preserve types.\n- **JSONL**: Each line is a separate JSON object. A JSON array (`[{...}, {...}]`) in a `.jsonl` file will fail — use `.json` extension instead.\n- **Parquet**: Preserves column types. Requires `pandas`\u002F`pyarrow` to read locally: `pd.read_parquet(\"examples.parquet\")`.\n\n## Append Examples: `ax datasets append`\n\nAdd examples to an existing dataset. Two input modes -- use whichever fits.\n\n### Inline JSON (agent-friendly)\n\nGenerate the payload directly -- no temp files needed:\n\n```bash\nax datasets append DATASET_NAME --space SPACE --json '[{\"question\": \"What is 2+2?\", \"answer\": \"4\"}]'\n\nax datasets append DATASET_NAME --space SPACE --json '[\n  {\"question\": \"What is gravity?\", \"answer\": \"A fundamental force...\"},\n  {\"question\": \"What is light?\", \"answer\": \"Electromagnetic radiation...\"}\n]'\n```\n\n### From a file\n\n```bash\nax datasets append DATASET_NAME --space SPACE --file new_examples.csv\nax datasets append DATASET_NAME --space SPACE --file additions.json\n```\n\n### To a specific version\n\n```bash\nax datasets append DATASET_NAME --space SPACE --json '[{\"q\": \"...\"}]' --version-id VERSION_ID\n```\n\n### Flags\n\n| Flag | Type | Required | Description |\n|------|------|----------|-------------|\n| `NAME_OR_ID` | string | yes | Dataset name or ID (positional); add `--space` when using name |\n| `--space` | string | no | Space name or ID (required if using dataset name instead of ID) |\n| `--json` | string | mutex | JSON array of example objects |\n| `--file, -f` | path | mutex | Data file (CSV, JSON, JSONL, Parquet) |\n| `--version-id` | string | no | Append to a specific version (default: latest) |\n| `-o, --output` | string | no | Output format for the returned dataset metadata |\n\nExactly one of `--json` or `--file` is required.\n\n### Validation\n\n- Each example must be a JSON object with at least one user-defined field\n- Maximum 100,000 examples per request\n\n**Schema validation before append:** If the dataset already has examples, inspect its schema before appending to avoid silent field mismatches:\n\n```bash\n# Check existing field names in the dataset\nax datasets export DATASET_NAME --space SPACE --stdout | jq '.[0] | keys'\n\n# Verify your new data has matching field names\necho '[{\"question\": \"...\"}]' | jq '.[0] | keys'\n\n# Both outputs should show the same user-defined fields\n```\n\nFields are free-form: extra fields in new examples are added, and missing fields become null. However, typos in field names (e.g., `queston` vs `question`) create new columns silently -- verify spelling before appending.\n\n## Delete Dataset: `ax datasets delete`\n\n```bash\nax datasets delete NAME_OR_ID\nax datasets delete NAME_OR_ID --space SPACE   # required when using dataset name instead of ID\nax datasets delete NAME_OR_ID --force   # skip confirmation prompt\n```\n\n### Flags\n\n| Flag | Type | Default | Description |\n|------|------|---------|-------------|\n| `NAME_OR_ID` | string | required | Dataset name or ID (positional) |\n| `--space` | string | none | Space name or ID (required if using dataset name instead of ID) |\n| `--force, -f` | bool | false | Skip confirmation prompt |\n\n## Update Dataset: `ax datasets update`\n\nRename an existing dataset.\n\n```bash\nax datasets update NAME_OR_ID --name \"new-dataset-name\"\nax datasets update NAME_OR_ID --name \"new-dataset-name\" --space SPACE\n```\n\n### Flags\n\n| Flag | Type | Required | Description |\n|------|------|----------|-------------|\n| `NAME_OR_ID` | string | yes | Dataset name or ID (positional) |\n| `--name` | string | yes | New dataset name |\n| `--space` | string | no | Space name or ID (required if using dataset name instead of ID) |\n\n## Annotate Examples: `ax datasets annotate-examples`\n\nWrite annotations onto dataset examples in bulk from a file. Upsert semantics — existing annotations with the same key are updated, new ones are created. Up to 1000 annotations per request.\n\n```bash\nax datasets annotate-examples NAME_OR_ID --file annotations.json\nax datasets annotate-examples NAME_OR_ID --file annotations.csv --space SPACE\n```\n\n### Flags\n\n| Flag | Type | Required | Description |\n|------|------|----------|-------------|\n| `NAME_OR_ID` | string | yes | Dataset name or ID (positional) |\n| `--file, -f` | path | yes | Annotation file: JSON, JSONL, CSV, or Parquet (use `-` for stdin) |\n| `--space` | string | no | Space name or ID (required if using dataset name instead of ID) |\n\n## Workflows\n\n### Find a dataset by name\n\nAll dataset commands accept a name or ID directly. You can pass a dataset name as the positional argument (add `--space SPACE` when not using an ID):\n\n```bash\n# Use name directly\nax datasets get \"eval-set-v1\" --space SPACE\nax datasets export \"eval-set-v1\" --space SPACE\n\n# Or resolve name to ID via list if you need the base64 ID\nax datasets list -o json | jq '.[] | select(.name == \"eval-set-v1\") | .id'\n```\n\n### Create a dataset from file for evaluation\n\n1. Prepare a CSV\u002FJSON\u002FParquet file with your evaluation columns (e.g., `input`, `expected_output`)\n   - If generating data inline, pipe it via stdin using `--file -` (see the Create Dataset section)\n2. `ax datasets create --name \"eval-set-v1\" --space SPACE --file eval_data.csv`\n3. Verify: `ax datasets get DATASET_NAME --space SPACE`\n4. Use the dataset name to run experiments\n\n### Add examples to an existing dataset\n\n```bash\n# Find the dataset\nax datasets list --space SPACE\n\n# Append inline or from a file using the dataset name (see Append Examples section for full syntax)\nax datasets append DATASET_NAME --space SPACE --json '[{\"question\": \"...\", \"answer\": \"...\"}]'\nax datasets append DATASET_NAME --space SPACE --file additional_examples.csv\n```\n\n### Download dataset for offline analysis\n\n1. `ax datasets list --space SPACE` -- find the dataset name\n2. `ax datasets export DATASET_NAME --space SPACE` -- download to file\n3. Parse the JSON: `jq '.[] | .question' dataset_*\u002Fexamples.json`\n\n### Export a specific version\n\n```bash\n# List versions\nax datasets get DATASET_NAME --space SPACE -o json | jq '.versions'\n\n# Export that version\nax datasets export DATASET_NAME --space SPACE --version-id VERSION_ID\n```\n\n### Iterate on a dataset\n\n1. Export current version: `ax datasets export DATASET_NAME --space SPACE`\n2. Modify the examples locally\n3. Append new rows: `ax datasets append DATASET_NAME --space SPACE --file new_rows.csv`\n4. Or create a fresh version: `ax datasets create --name \"eval-set-v2\" --space SPACE --file updated_data.json`\n\n### Pipe export to other tools\n\n```bash\n# Count examples\nax datasets export DATASET_NAME --space SPACE --stdout | jq 'length'\n\n# Extract a single field\nax datasets export DATASET_NAME --space SPACE --stdout | jq '.[].question'\n\n# Convert to CSV with jq\nax datasets export DATASET_NAME --space SPACE --stdout | jq -r '.[] | [.question, .answer] | @csv'\n```\n\n## Dataset Example Schema\n\nExamples are free-form JSON objects. There is no fixed schema -- columns are whatever fields you provide. System-managed fields are added by the server:\n\n| Field | Type | Managed by | Notes |\n|-------|------|-----------|-------|\n| `id` | string | server | Auto-generated UUID. Required on update, forbidden on create\u002Fappend |\n| `created_at` | datetime | server | Immutable creation timestamp |\n| `updated_at` | datetime | server | Auto-updated on modification |\n| *(any user field)* | any JSON type | user | String, number, boolean, null, nested object, array |\n\n\n## Related Skills\n\n- **arize-trace**: Export production spans to understand what data to put in datasets → use `arize-trace`\n- **arize-experiment**: Run evaluations against this dataset → next step is `arize-experiment`\n- **arize-prompt-optimization**: Use dataset + experiment results to improve prompts → use `arize-prompt-optimization`\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 is wrong, expired, or doesn't have access to this space. Fix the profile using [references\u002Fax-profiles.md](references\u002Fax-profiles.md). |\n| `No profile found` | No profile is configured. See [references\u002Fax-profiles.md](references\u002Fax-profiles.md) to create one. |\n| `Dataset not found` | Verify dataset ID with `ax datasets list` |\n| `File format error` | Supported: CSV, JSON, JSONL, Parquet. 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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},[5351,5354,5357],{"name":5352,"slug":5353,"type":16},"CLI","cli",{"name":5355,"slug":5356,"type":16},"Operations","operations",{"name":5358,"slug":5359,"type":16},"Permissions","permissions","2026-07-22T05:37:21.991338",{"slug":5362,"name":5362,"fn":5363,"description":5364,"org":5365,"tags":5366,"stars":26,"repoUrl":27,"updatedAt":5380},"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},[5367,5370,5373,5376,5377],{"name":5368,"slug":5369,"type":16},"Anthropic","anthropic",{"name":5371,"slug":5372,"type":16},"Azure","azure",{"name":5374,"slug":5375,"type":16},"Integrations","integrations",{"name":18,"slug":19,"type":16},{"name":5378,"slug":5379,"type":16},"OpenAI","openai","2026-07-22T05:37:23.90468",{"slug":5382,"name":5382,"fn":5383,"description":5384,"org":5385,"tags":5386,"stars":26,"repoUrl":27,"updatedAt":5394},"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},[5387,5390,5391],{"name":5388,"slug":5389,"type":16},"Data Analysis","data-analysis",{"name":18,"slug":19,"type":16},{"name":5392,"slug":5393,"type":16},"Observability","observability","2026-07-22T05:37:19.010776",{"slug":5396,"name":5396,"fn":5397,"description":5398,"org":5399,"tags":5400,"stars":26,"repoUrl":27,"updatedAt":5416},"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},[5401,5404,5407,5410,5413],{"name":5402,"slug":5403,"type":16},"Audit","audit",{"name":5405,"slug":5406,"type":16},"Compliance","compliance",{"name":5408,"slug":5409,"type":16},"GDPR","gdpr",{"name":5411,"slug":5412,"type":16},"Legal","legal",{"name":5414,"slug":5415,"type":16},"Security","security","2026-07-19T05:39:42.632738",{"slug":4,"name":4,"fn":5,"description":6,"org":5418,"tags":5419,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[5420,5421,5422,5423],{"name":21,"slug":22,"type":16},{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"slug":5425,"name":5425,"fn":5426,"description":5427,"org":5428,"tags":5429,"stars":26,"repoUrl":27,"updatedAt":5433},"arize-evaluator","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},[5430,5431,5432],{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":5392,"slug":5393,"type":16},"2026-07-25T05:32:37.552903",{"slug":5018,"name":5018,"fn":5435,"description":5436,"org":5437,"tags":5438,"stars":26,"repoUrl":27,"updatedAt":5444},"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},[5439,5442,5443],{"name":5440,"slug":5441,"type":16},"Analytics","analytics",{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},"2026-07-31T05:53:44.725539",13,{"items":5447,"total":5566},[5448,5462,5471,5483,5495,5505,5517,5527,5538,5544,5552,5558],{"slug":5449,"name":5449,"fn":5450,"description":5451,"org":5452,"tags":5453,"stars":5459,"repoUrl":5460,"updatedAt":5461},"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},[5454,5455,5456,5457],{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":5392,"slug":5393,"type":16},{"name":5458,"slug":40,"type":16},"Tracing",10513,"https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix","2026-07-12T08:08:14.140984",{"slug":15,"name":15,"fn":5463,"description":5464,"org":5465,"tags":5466,"stars":5459,"repoUrl":5460,"updatedAt":5470},"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},[5467,5468,5469],{"name":5388,"slug":5389,"type":16},{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},"2026-07-12T08:08:21.695457",{"slug":5472,"name":5472,"fn":5473,"description":5474,"org":5475,"tags":5476,"stars":5459,"repoUrl":5460,"updatedAt":5482},"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},[5477,5480,5481],{"name":5478,"slug":5479,"type":16},"Debugging","debugging",{"name":5392,"slug":5393,"type":16},{"name":5458,"slug":40,"type":16},"2026-07-12T08:08:10.44243",{"slug":5484,"name":5484,"fn":5485,"description":5486,"org":5487,"tags":5488,"stars":5459,"repoUrl":5460,"updatedAt":5494},"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},[5489,5490,5491],{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":5492,"slug":5493,"type":16},"Testing","testing","2026-07-31T05:58:09.13624",{"slug":38,"name":38,"fn":5496,"description":5497,"org":5498,"tags":5499,"stars":5459,"repoUrl":5460,"updatedAt":5504},"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},[5500,5501,5502,5503],{"name":14,"slug":15,"type":16},{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":5492,"slug":5493,"type":16},"2026-07-12T08:08:11.691477",{"slug":5506,"name":5506,"fn":5507,"description":5508,"org":5509,"tags":5510,"stars":5459,"repoUrl":5460,"updatedAt":5516},"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},[5511,5512,5513],{"name":5440,"slug":5441,"type":16},{"name":5388,"slug":5389,"type":16},{"name":5514,"slug":5515,"type":16},"GraphQL","graphql","2026-07-12T08:08:17.163493",{"slug":5518,"name":5518,"fn":5519,"description":5520,"org":5521,"tags":5522,"stars":5459,"repoUrl":5460,"updatedAt":5526},"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},[5523,5524,5525],{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":5492,"slug":5493,"type":16},"2026-07-12T08:08:12.920792",{"slug":5528,"name":5528,"fn":5529,"description":5530,"org":5531,"tags":5532,"stars":5459,"repoUrl":5460,"updatedAt":5537},"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},[5533,5534,5535,5536],{"name":5478,"slug":5479,"type":16},{"name":18,"slug":19,"type":16},{"name":5392,"slug":5393,"type":16},{"name":5458,"slug":40,"type":16},"2026-07-12T08:08:19.597239",{"slug":5346,"name":5346,"fn":5347,"description":5348,"org":5539,"tags":5540,"stars":26,"repoUrl":27,"updatedAt":5360},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[5541,5542,5543],{"name":5352,"slug":5353,"type":16},{"name":5355,"slug":5356,"type":16},{"name":5358,"slug":5359,"type":16},{"slug":5362,"name":5362,"fn":5363,"description":5364,"org":5545,"tags":5546,"stars":26,"repoUrl":27,"updatedAt":5380},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[5547,5548,5549,5550,5551],{"name":5368,"slug":5369,"type":16},{"name":5371,"slug":5372,"type":16},{"name":5374,"slug":5375,"type":16},{"name":18,"slug":19,"type":16},{"name":5378,"slug":5379,"type":16},{"slug":5382,"name":5382,"fn":5383,"description":5384,"org":5553,"tags":5554,"stars":26,"repoUrl":27,"updatedAt":5394},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[5555,5556,5557],{"name":5388,"slug":5389,"type":16},{"name":18,"slug":19,"type":16},{"name":5392,"slug":5393,"type":16},{"slug":5396,"name":5396,"fn":5397,"description":5398,"org":5559,"tags":5560,"stars":26,"repoUrl":27,"updatedAt":5416},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[5561,5562,5563,5564,5565],{"name":5402,"slug":5403,"type":16},{"name":5405,"slug":5406,"type":16},{"name":5408,"slug":5409,"type":16},{"name":5411,"slug":5412,"type":16},{"name":5414,"slug":5415,"type":16},23]