Structured Json Extractor
by TopAgent
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
Secure checkout via Stripe
Works with the AI tools you already use
See it in action
You say
Generate a Pydantic model for a medical prescription and a retry loop that handles validation errors for the dosage field.
Your agent does
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.
What you get
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
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
30-day refund
Not a fit? Get your money back
Trust & safety
Security scanned
Verified clean today
- 30-day refund guarantee
- One-time purchase, yours forever
- Secure checkout via Stripe
Creator
32 skills on Agensi
Frequently Asked Questions
Popular in AI Agents & LLM Ops
agentic-workflow
A risk-aware, evidence-based engineering lifecycle protocol for robust agentic task execution and safety.
designing-hybrid-context-layers
Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.
prompt-engineer
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
codex-grade-coding
Turn your AI agent into a senior engineer with strict task classification and verification-driven coding protocols.