discover-datasets

name	discover-datasets
description	This skill should be used when the user asks to discover datasets, list datasets, find what datasets exist, browse dataset items, inspect dataset contents, check dataset schemas, or explore dataset metadata in their Langfuse project. It enumerates all datasets with their items, runs, and schema configurations.
metadata	{"ai-assistant-harness-adaptation.claude-code":"references/ai-assistant-harnesses/claude-code.md","ai-assistant-harness-adaptation.codex":"references/ai-assistant-harnesses/codex.md"}

Enumerate all datasets in the Langfuse project and present findings in organized tables.

Harness Adaptation

Depending on who you are as an AI agent, load exactly one metadata-linked reference and skip every non-matching file.

Step 1: List All Datasets

Query the REST API to get all datasets:

curl -s -u "$PUBLIC_KEY:$SECRET_KEY" "$HOST/api/public/datasets"

Response includes: name, id, description, metadata, inputSchema, expectedOutputSchema, items[] (IDs), runs[] (names).

If API is unavailable, fall back to DB:

SELECT id, name, description, metadata,
       input_schema, expected_output_schema,
       remote_experiment_url, remote_experiment_payload,
       created_at, updated_at
FROM datasets
WHERE project_id = '{PROJECT_ID}'
ORDER BY created_at DESC;

Note: remote_experiment_url and remote_experiment_payload are only visible via DB, not the REST API.

Step 2: Present Dataset Summary

Display a table:

Name	Items	Runs	Description	Schema	Remote Experiment

• Items: Count from the items[] array.
• Runs: Count from the runs[] array.
• Schema: "Yes (input)" / "Yes (output)" / "Yes (both)" / "No" based on inputSchema/expectedOutputSchema.
• Remote Experiment: "Configured" if remote_experiment_url is set (DB-only check), otherwise "Not configured" or "Unknown (API-only)".

Step 3: Inspect a Specific Dataset

When the user selects a dataset, fetch its full details:

curl -s -u "$PUBLIC_KEY:$SECRET_KEY" "$HOST/api/public/datasets/{name}"

This returns the dataset with full item objects (including input, expectedOutput, metadata) and run names.

Present items in a table:

#	Item ID	Status	Source Trace	Input Summary	Expected Output

Truncate long input/expectedOutput values to first 100 chars with ....

Step 4: Inspect Dataset Items

To get paginated items (useful for large datasets):

curl -s -u "$PUBLIC_KEY:$SECRET_KEY" "$HOST/api/public/dataset-items?datasetName={name}&limit=50&page=1"

Step 5: Check Item Version History (DB Only)

To see all versions of a specific item:

SELECT id, input, expected_output, status, is_deleted,
       valid_from, valid_to, created_at, updated_at
FROM dataset_items
WHERE project_id = '{PROJECT_ID}'
  AND id = '{ITEM_ID}'
ORDER BY valid_from DESC;

Current version has valid_to IS NULL.

Step 6: Suggest Next Actions

Based on discovered data, suggest:

• If dataset has no items → "Use or suggest the langfuse:langfuse-dataset-expert agent to populate it."
• If dataset has items but no runs → "Use or suggest the langfuse:langfuse-experiment-manager agent to run experiments."
• If dataset has no schema → "Consider adding input/output schemas for validation."
• If remote experiment URL is not configured → "Use or suggest the langfuse:langfuse-experiment-manager agent to configure remote triggering."

Refer to references/datasets-api-reference.md for complete API details.