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Grok+17 agentsStructured Json Extractor
Use to turn free-form LLM output into validated JSON via Pydantic models. Covers typed response models, retry on validation failure, nested schemas, and batch p
Free
Structured Json Extractor
Example session with this skill installed
Generate a Pydantic model for a medical prescription and a retry loop that handles validation errors for the dosage field.
- Read your context and instructions
- Compiled the structured json extractor
class Prescription(BaseModel):
drug: str
dosage_mg: int = Field(gt=0)
# Implementation includes a while-loop catching ValidationError,
# appending e.errors() to messages, and retrying up to 3 times
# before logging to failed.txt.
Connects securely to your tools. The creator never sees your data.
About this skill
The problem
LLMs frequently hallucinate JSON structures, skip required fields, or return invalid data types that break downstream applications. Manual regex parsing and basic string cleaning fail at scale when dealing with complex nested data or high-volume batch processing.
What it does
- Defines strict schema constraints using Pydantic models to enforce data integrity.
- Implements a feedback loop that catches validation errors and re-prompts the LLM with specific error locations.
- Handles complex nested schemas and cross-field validation for sophisticated data structures.
- Provides a concurrent batch processing pattern for high-throughput extraction tasks.
- Ensures reliability with a fallback logging system for inputs that fail all retry attempts.
Frameworks & tools
Python, Pydantic, and any LLM SDK (OpenAI, Anthropic) supporting structured outputs or JSON mode.
Why this beats prompting it yourself
Simply asking an LLM for JSON is not a production strategy. This skill provides the infrastructure for error handling, field-level constraints, and self-correction loops that raw prompting lacks. It transforms unreliable text generation into a hardened data pipeline.
Use cases
- Extracting structured line items and totals from unstructured receipt photos.
- Converting messy customer support transcripts into classified tickets with priority levels.
- Automating document ingestion from diverse PDF layouts into a normalized database.
- Batch processing thousands of product reviews into sentiment and feature-score matrices.
Known limitations
Not suitable for creative writing or open-ended chat where strict schemas limit quality. Extremely long documents require pre-chunking before extraction.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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