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Use when users want to list datasets, get dataset details, create new datasets, delete datasets, export dataset data, or work with dataset examples. Triggers on \"list datasets\", \"create dataset\", \"ax datasets\", \"export dataset\", \"delete dataset\", or any request about managing Arize datasets via CLI.\n---\n\n# Arize AX Datasets\n\nManage datasets in the Arize AI platform using the `ax` CLI.\n\n## Prerequisites\n\nThe user must have:\n1. Arize AX CLI installed (`pip install arize-ax-cli`)\n2. CLI configured with valid credentials (`ax config init`)\n\n## Core Dataset Commands\n\n### List All Datasets\n\n```bash\nax datasets list\n```\n\n**Options:**\n- `--output \u003Cformat>` - Output format: `table` (default), `json`, `csv`, `parquet`\n- `--profile \u003Cname>` - Use specific configuration profile\n- `--limit \u003Cn>` - Limit number of results\n- `--offset \u003Cn>` - Skip first n results (pagination)\n\n**Examples:**\n\n```bash\n# List as table (default)\nax datasets list\n\n# List as JSON\nax datasets list --output json\n\n# List with pagination\nax datasets list --limit 10 --offset 0\n\n# Use production profile\nax datasets list --profile production\n```\n\n**Extracting Dataset IDs:**\n\nTo find a specific dataset ID for use in other operations:\n\n```bash\n# Get all dataset IDs and names as JSON\nax datasets list --output json | jq '.[] | {id: .id, name: .name}'\n\n# Find a dataset ID by name\nax datasets list --output json | jq -r '.[] | select(.name == \"Training Data\") | .id'\n\n# Save dataset ID to a variable\nDATASET_ID=$(ax datasets list --output json | jq -r '.[] | select(.name == \"Training Data\") | .id')\necho \"Found dataset: $DATASET_ID\"\n\n# Use the ID in subsequent commands\nax datasets get \"$DATASET_ID\"\nax datasets delete \"$DATASET_ID\"\n```\n\n**Without jq (using grep):**\n\n```bash\n# List with grep to find dataset\nax datasets list --output json | grep -A 2 \"Training Data\" | grep \"id\"\n\n# More reliable pattern\nax datasets list --output json | grep -B 1 '\"name\": \"Training Data\"' | grep \"id\" | cut -d'\"' -f4\n```\n\n### Get Dataset Details\n\nRetrieve information about a specific dataset:\n\n```bash\nax datasets get \u003Cdataset-id>\n```\n\n**Options:**\n- `--output \u003Cformat>` - Output format\n- `--profile \u003Cname>` - Configuration profile to use\n\n**Examples:**\n\n```bash\n# Get dataset details\nax datasets get ds_abc123xyz\n\n# Get as JSON\nax datasets get ds_abc123xyz --output json\n\n# Get from production environment\nax datasets get ds_abc123xyz --profile production\n```\n\n### Create a New Dataset\n\nCreate a dataset from a file:\n\n```bash\nax datasets create --file \u003Cpath> [options]\n```\n\n**Supported File Formats:**\n- CSV (`.csv`)\n- JSON (`.json`, `.jsonl`)\n- Parquet (`.parquet`)\n\n**Options:**\n- `--name \u003Cname>` - Dataset name (required or inferred from filename)\n- `--description \u003Ctext>` - Dataset description\n- `--profile \u003Cname>` - Configuration profile to use\n\n**Examples:**\n\n```bash\n# Create from CSV\nax datasets create --file data.csv --name \"Training Data\" --description \"Production training set\"\n\n# Create from JSON\nax datasets create --file examples.json --name \"Test Examples\"\n\n# Create from Parquet\nax datasets create --file dataset.parquet --name \"Large Dataset\"\n\n# Use staging profile\nax datasets create --file data.csv --name \"Test Data\" --profile staging\n```\n\n### Delete a Dataset\n\nRemove a dataset from Arize:\n\n```bash\nax datasets delete \u003Cdataset-id>\n```\n\n**Options:**\n- `--profile \u003Cname>` - Configuration profile to use\n- `--yes` or `-y` - Skip confirmation prompt\n\n**Examples:**\n\n```bash\n# Delete with confirmation\nax datasets delete ds_abc123xyz\n\n# Delete without confirmation\nax datasets delete ds_abc123xyz --yes\n\n# Delete from production\nax datasets delete ds_abc123xyz --profile production\n```\n\n**⚠️ Warning**: Deletion is permanent. Always verify the dataset ID before deleting.\n\n## Export Dataset Data\n\nExport dataset examples to various formats:\n\n```bash\nax datasets get \u003Cdataset-id> --output \u003Cformat>\n```\n\n**Export Formats:**\n- `json` - JSON format\n- `csv` - Comma-separated values\n- `parquet` - Apache Parquet format\n\n**Examples:**\n\n```bash\n# Export to JSON\nax datasets get ds_abc123xyz --output json > dataset.json\n\n# Export to CSV\nax datasets get ds_abc123xyz --output csv > dataset.csv\n\n# Export to Parquet\nax datasets get ds_abc123xyz --output parquet > dataset.parquet\n```\n\n## Working with Multiple Profiles\n\nWhen working across different environments (dev, staging, production):\n\n```bash\n# List datasets in production\nax datasets list --profile production\n\n# Create dataset in staging\nax datasets create --file test_data.csv --profile staging\n\n# Get dataset from dev environment\nax datasets get ds_dev_123 --profile dev\n```\n\n## Pagination for Large Results\n\nFor accounts with many datasets, use pagination:\n\n```bash\n# First page (10 items)\nax datasets list --limit 10 --offset 0\n\n# Second page\nax datasets list --limit 10 --offset 10\n\n# Third page\nax datasets list --limit 10 --offset 20\n```\n\n## Common Workflows\n\n### Workflow 1: Find Dataset by Name and Get Details\n\n```bash\n# 1. List all datasets and find the one you want\nax datasets list --output json | jq '.[] | {id: .id, name: .name}'\n\n# 2. Extract the specific dataset ID by name\nDATASET_ID=$(ax datasets list --output json | jq -r '.[] | select(.name == \"Production Data\") | .id')\n\n# 3. Get detailed information about that dataset\nax datasets get \"$DATASET_ID\"\n\n# 4. Export the dataset if needed\nax datasets get \"$DATASET_ID\" --output csv > dataset_export.csv\n```\n\n### Workflow 2: Create and Verify Dataset\n\n```bash\n# 1. Create dataset\nax datasets create --file data.csv --name \"My Dataset\"\n\n# 2. Find the new dataset ID\nDATASET_ID=$(ax datasets list --output json | jq -r '.[] | select(.name == \"My Dataset\") | .id')\necho \"Created dataset: $DATASET_ID\"\n\n# 3. Verify details\nax datasets get \"$DATASET_ID\"\n```\n\n### Workflow 2: Export, Modify, and Re-upload\n\n```bash\n# 1. Export existing dataset\nax datasets get ds_abc123 --output csv > dataset.csv\n\n# 2. Modify the CSV file (manual editing)\n\n# 3. Create new version\nax datasets create --file dataset.csv --name \"Updated Dataset v2\"\n```\n\n### Workflow 3: Migrate Dataset Between Environments\n\n```bash\n# 1. Export from production\nax datasets get ds_prod_123 --profile production --output json > prod_data.json\n\n# 2. Import to staging\nax datasets create --file prod_data.json --name \"Production Copy\" --profile staging\n```\n\n### Workflow 4: Cleanup Old Datasets\n\n```bash\n# 1. List all datasets\nax datasets list --output json > all_datasets.json\n\n# 2. Review and identify datasets to delete (manual review)\n\n# 3. Delete old datasets\nax datasets delete ds_old_001 --yes\nax datasets delete ds_old_002 --yes\n```\n\n## Output Format Examples\n\n### Table Format (Default)\nHuman-readable table with columns for ID, Name, Created, and Status.\n\n### JSON Format\nStructured JSON with full dataset metadata:\n```json\n{\n  \"id\": \"ds_abc123xyz\",\n  \"name\": \"Training Data\",\n  \"description\": \"Production training set\",\n  \"created_at\": \"2024-01-15T10:30:00Z\",\n  \"num_examples\": 1000,\n  \"size_bytes\": 52428800\n}\n```\n\n### CSV Format\nComma-separated values, useful for importing into spreadsheets or pandas.\n\n### Parquet Format\nEfficient columnar format, ideal for large datasets and data processing.\n\n## Troubleshooting\n\n### \"Dataset not found\"\n\n1. Verify dataset ID: `ax datasets list`\n2. Check you're using the correct profile: `ax config show`\n3. Ensure the dataset exists in the current space\u002Fproject\n\n### \"Permission denied\" or \"Unauthorized\"\n\n1. Check API key is valid: `ax config show --expand`\n2. Verify the key has dataset permissions in Arize\n3. Try re-authenticating: `ax config init`\n\n### \"File format not supported\"\n\nSupported formats are CSV, JSON (including JSONL), and Parquet. Check:\n1. File extension is correct\n2. File is not corrupted\n3. File content matches the extension\n\n### Large dataset creation fails\n\nFor very large datasets:\n1. Check file size and network stability\n2. Try breaking into smaller chunks\n3. Use Parquet format for better compression\n4. Consider using the Arize Python SDK for programmatic uploads\n\n### Output is too large\n\nFor datasets with many examples:\n1. Use `--limit` to restrict output size\n2. Export to file instead of viewing in terminal:\n   ```bash\n   ax datasets get ds_abc123 --output json > dataset.json\n   ```\n3. Use pagination with `--limit` and `--offset`\n\n## Tips\n\n1. **Extract dataset IDs by name**:\n   ```bash\n   DATASET_ID=$(ax datasets list --output json | jq -r '.[] | select(.name == \"My Dataset\") | .id')\n   ```\n2. **Use JSON output for scripting**: `ax datasets list --output json | jq '.[] | .id'`\n3. **List IDs and names together**: `ax datasets list --output json | jq '.[] | {id, name}'`\n4. **Pipe to files for export**: Always redirect large outputs to files\n5. **Verify before delete**: Use `ax datasets get \"$DATASET_ID\"` to confirm before deleting\n6. **Profile naming**: Use descriptive names like `prod`, `staging`, `dev`\n7. **Save IDs to variables**: Store dataset IDs in shell variables for reuse in scripts\n8. **Check limits**: Some operations may have rate limits or quotas\n\n## Next Steps\n\n- View dataset details in Arize UI: https:\u002F\u002Fapp.arize.com\n- Use datasets in experiments and evaluations\n- Integrate with Arize Python SDK for programmatic access\n- Set up CI\u002FCD pipelines using the CLI\n\n## When to Use This Skill\n\nUse this skill when users want to:\n- ✅ List all datasets in their Arize account\n- ✅ Get details about a specific dataset\n- ✅ Create a new dataset from a local file\n- ✅ Delete datasets they no longer need\n- ✅ Export dataset data to different formats\n- ✅ Work with datasets across multiple environments\n- ✅ Troubleshoot dataset-related CLI issues\n\n**Don't use this skill for:**\n- ❌ GraphQL queries (use `\u002Farize-graphql-analytics` instead)\n- ❌ Installing\u002Fconfiguring the CLI (use `\u002Fsetup-arize-cli` instead)\n- ❌ Managing projects, models, or other Arize resources beyond 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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":4047,"total":4218},[4048,4069,4080,4092,4104,4115,4129,4139,4150,4166,4186,4196],{"slug":4049,"name":4049,"fn":4050,"description":4051,"org":4052,"tags":4053,"stars":4066,"repoUrl":4067,"updatedAt":4068},"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},[4054,4057,4060,4063],{"name":4055,"slug":4056,"type":16},"Evals","evals",{"name":4058,"slug":4059,"type":16},"LLM","llm",{"name":4061,"slug":4062,"type":16},"Observability","observability",{"name":4064,"slug":4065,"type":16},"Tracing","tracing",10513,"https:\u002F\u002Fgithub.com\u002FArize-ai\u002Fphoenix","2026-07-12T08:08:14.140984",{"slug":15,"name":15,"fn":4070,"description":4071,"org":4072,"tags":4073,"stars":4066,"repoUrl":4067,"updatedAt":4079},"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},[4074,4077,4078],{"name":4075,"slug":4076,"type":16},"Data Analysis","data-analysis",{"name":14,"slug":15,"type":16},{"name":4055,"slug":4056,"type":16},"2026-07-12T08:08:21.695457",{"slug":4081,"name":4081,"fn":4082,"description":4083,"org":4084,"tags":4085,"stars":4066,"repoUrl":4067,"updatedAt":4091},"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},[4086,4089,4090],{"name":4087,"slug":4088,"type":16},"Debugging","debugging",{"name":4061,"slug":4062,"type":16},{"name":4064,"slug":4065,"type":16},"2026-07-12T08:08:10.44243",{"slug":4093,"name":4093,"fn":4094,"description":4095,"org":4096,"tags":4097,"stars":4066,"repoUrl":4067,"updatedAt":4103},"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},[4098,4099,4100],{"name":4055,"slug":4056,"type":16},{"name":4058,"slug":4059,"type":16},{"name":4101,"slug":4102,"type":16},"Testing","testing","2026-07-31T05:58:09.13624",{"slug":4105,"name":4105,"fn":4106,"description":4107,"org":4108,"tags":4109,"stars":4066,"repoUrl":4067,"updatedAt":4114},"experiments","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},[4110,4111,4112,4113],{"name":14,"slug":15,"type":16},{"name":4055,"slug":4056,"type":16},{"name":4058,"slug":4059,"type":16},{"name":4101,"slug":4102,"type":16},"2026-07-12T08:08:11.691477",{"slug":4116,"name":4116,"fn":4117,"description":4118,"org":4119,"tags":4120,"stars":4066,"repoUrl":4067,"updatedAt":4128},"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},[4121,4124,4125],{"name":4122,"slug":4123,"type":16},"Analytics","analytics",{"name":4075,"slug":4076,"type":16},{"name":4126,"slug":4127,"type":16},"GraphQL","graphql","2026-07-12T08:08:17.163493",{"slug":4130,"name":4130,"fn":4131,"description":4132,"org":4133,"tags":4134,"stars":4066,"repoUrl":4067,"updatedAt":4138},"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},[4135,4136,4137],{"name":4055,"slug":4056,"type":16},{"name":4058,"slug":4059,"type":16},{"name":4101,"slug":4102,"type":16},"2026-07-12T08:08:12.920792",{"slug":4140,"name":4140,"fn":4141,"description":4142,"org":4143,"tags":4144,"stars":4066,"repoUrl":4067,"updatedAt":4149},"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},[4145,4146,4147,4148],{"name":4087,"slug":4088,"type":16},{"name":4058,"slug":4059,"type":16},{"name":4061,"slug":4062,"type":16},{"name":4064,"slug":4065,"type":16},"2026-07-12T08:08:19.597239",{"slug":4151,"name":4151,"fn":4152,"description":4153,"org":4154,"tags":4155,"stars":4163,"repoUrl":4164,"updatedAt":4165},"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},[4156,4157,4160],{"name":21,"slug":22,"type":16},{"name":4158,"slug":4159,"type":16},"Operations","operations",{"name":4161,"slug":4162,"type":16},"Permissions","permissions",38,"https:\u002F\u002Fgithub.com\u002FArize-ai\u002Farize-skills","2026-07-22T05:37:21.991338",{"slug":4167,"name":4167,"fn":4168,"description":4169,"org":4170,"tags":4171,"stars":4163,"repoUrl":4164,"updatedAt":4185},"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},[4172,4175,4178,4181,4182],{"name":4173,"slug":4174,"type":16},"Anthropic","anthropic",{"name":4176,"slug":4177,"type":16},"Azure","azure",{"name":4179,"slug":4180,"type":16},"Integrations","integrations",{"name":4058,"slug":4059,"type":16},{"name":4183,"slug":4184,"type":16},"OpenAI","openai","2026-07-22T05:37:23.90468",{"slug":4187,"name":4187,"fn":4188,"description":4189,"org":4190,"tags":4191,"stars":4163,"repoUrl":4164,"updatedAt":4195},"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},[4192,4193,4194],{"name":4075,"slug":4076,"type":16},{"name":4058,"slug":4059,"type":16},{"name":4061,"slug":4062,"type":16},"2026-07-22T05:37:19.010776",{"slug":4197,"name":4197,"fn":4198,"description":4199,"org":4200,"tags":4201,"stars":4163,"repoUrl":4164,"updatedAt":4217},"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},[4202,4205,4208,4211,4214],{"name":4203,"slug":4204,"type":16},"Audit","audit",{"name":4206,"slug":4207,"type":16},"Compliance","compliance",{"name":4209,"slug":4210,"type":16},"GDPR","gdpr",{"name":4212,"slug":4213,"type":16},"Legal","legal",{"name":4215,"slug":4216,"type":16},"Security","security","2026-07-19T05:39:42.632738",23,{"items":4220,"total":263},[4221,4227],{"slug":4,"name":4,"fn":5,"description":6,"org":4222,"tags":4223,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[4224,4225,4226],{"name":21,"slug":22,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":4228,"name":4228,"fn":4229,"description":4230,"org":4231,"tags":4232,"stars":23,"repoUrl":24,"updatedAt":4235},"arize-projects","manage projects in Arize AI","Manage projects in Arize AI using the ax CLI. Use when users want to list projects, get project details, create new projects, delete projects, or organize work within Arize spaces. Triggers on \"list projects\", \"create project\", \"ax projects\", \"delete project\", or any request about managing Arize projects via CLI.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[4233,4234],{"name":21,"slug":22,"type":16},{"name":4061,"slug":4062,"type":16},"2026-07-12T08:08:07.341667"]