upstage-schema-generation

name	upstage-schema-generation
description	Generate a JSON schema for structured document information extraction. Use this skill whenever the user asks to generate or create a schema for a document — including phrases like "스키마 생성해줘", "이 문서에서 뭘 추출할지 스키마 짜줘", "generate a schema for this document", or any request to define extraction fields for a document. Also trigger when the user provides file paths or a folder path containing documents and wants a schema generated.
model	claude-opus-4-6

Upstage Schema Generation

Analyze sample documents and automatically generate a JSON schema for use with Information Extraction.

Prerequisites

• API Key: UPSTAGE_API_KEY environment variable is required. Get your key at console.upstage.ai.

Two Modes

Mode	When to use	Latency
API mode	Default. Fast schema generation via Upstage endpoint.	Low
VLM mode (claude-opus-4-6)	When the user wants careful, hand-tuned schemas with precise extraction rules and table-aware design.	High

API Mode (Default)

Endpoint: POST https://api.upstage.ai/v1/information-extraction/schema-generation

import os
import json
import requests
import base64

api_key = os.environ["UPSTAGE_API_KEY"]

with open("document.pdf", "rb") as f:
    b64 = base64.b64encode(f.read()).decode()

response = requests.post(
    "https://api.upstage.ai/v1/information-extraction/schema-generation",
    headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
    json={
        "model": "information-extract",
        "messages": [
            {"role": "system", "content": "Generate schema for this invoice document."},
            {"role": "user", "content": [
                {"type": "image_url", "image_url": {"url": f"data:application/pdf;base64,{b64}"}}
            ]}
        ]
    }
)

schema = json.loads(response.json()["choices"][0]["message"]["content"])
print(json.dumps(schema, indent=2))

Note: The API model is information-extract (not schema-generate). The system message can guide the schema focus (e.g., "Generate schema about bank_name."). Up to 3 sample images can be provided in the user message.

VLM Mode

For carefully designed schemas with precise extraction rules, follow the 4-step VLM workflow.

• Workflow: Read references/vlm-workflow.md (parameter gathering, document reading, property list, JSON schema conversion)
• Design rules: Read references/schema-design.md (key naming, descriptions, table handling, blank/duplicate handling)

Output Files

• Default: write generated schema to <system-temp>/<input-stem>.schema.json (e.g., /tmp/invoice.schema.json). For inline input with no source file, use <system-temp>/<timestamp>-schema.json.
• Override: if the user specifies an output path, use it.
• Always print the resolved absolute path in your response so the user can locate the file.

Related Skills

• Generated schema → use directly as response_format in upstage-information-extraction
• If documents need pre-sorting → classify with upstage-document-classification first, then generate per-category schemas
• For multi-API pipelines → see upstage-builder