How do I install the Datalab API skill?

Run npx skillmds@latest add datalab-to/datalab-api in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Datalab API safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Datalab API?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Datalab API free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Datalab API?

datalab-to (@datalab-to) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Datalab API by datalab-to — Agent Skill

Name: Datalab API
Author: datalab-to

File contents

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.

---name: datalab-apidescription: 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.---# Datalab API extraction## InstructionsThe 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 scoreRequires a `DATALAB_API_KEY` environment variable. If it's not set, ask the user — keys come from https://www.datalab.to (free tier available).## ExtractingUse the helper script in this skill:```shellpython .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](https://www.datalab.to/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.

Frequently asked questions

Datalab API