Datalab API

Documents

datalab_api

Datalab API is an Agent Skill for documents published in the datalab-to/lift repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Extract structured data from documents via the hosted Datalab API - for harder documents, when no GPU/server is available, or when results need verification with confidence scores.

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How to use this skill

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Prompt to paste

Inspect the Agent Skill at https://github.com/datalab-to/lift/blob/HEAD/.claude/skills/datalab_api/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/datalab-api, but wait for my approval before writing files or running scripts.

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Datalab API extraction

Instructions

The Datalab API (https://www.datalab.to) runs document extraction as a managed service with more powerful models than local lift. Use it instead of the lift_extraction skill when:

No lift server or GPU is available locally
The document is hard (degraded scans, complex layouts, handwriting) or local extraction gave wrong/hallucinated values
The user needs verified results — balanced mode runs per-field verification and returns a confidence score

Requires a DATALAB_API_KEY environment variable. If it's not set, ask the user — keys come from https://www.datalab.to (free tier available).

Extracting

Use the helper script in this skill:

python .claude/skills/datalab_api/scripts/datalab_extract.py document.pdf \
    --schema schema.json > extraction.json

--schema accepts a .json file path or an inline JSON string (standard JSON Schema with a properties key — same authoring guidelines as the lift_extraction skill).
--mode picks the speed/accuracy tradeoff:

mode	what it does	when
`turbo`	fastest, cheapest	low-latency, less accuracy sensitive
`fast`	full parse + extraction, low latency	general use
`balanced`	multi-pass with per-field verification (default)	hard docs, when correctness matters

--page-range 0,2-4,10 limits pages; --timeout defaults to 600s (long docs in balanced mode take minutes).
The extraction JSON goes to stdout; the request id and the confidence score (extraction_score_average, 1-5) go to stderr. Treat a score under ~4 as "review the output against the document".

Alternatives

Python SDK: pip install datalab-python-sdk (from datalab_sdk import ...), CLI datalab.
Interactive: the Datalab playground — try schemas against documents in the browser, no code.
API reference: https://www.datalab.to (docs linked from the site; OpenAPI at /openapi.json).

Troubleshooting

401: bad/missing API key. 429: rate limited — wait and retry.
Timeout on long docs: raise --timeout, or reduce scope with --page-range.
Schema rejected (400): ensure it's a JSON object with properties; avoid $ref and exotic constructs.