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extraction-design

Design precise, low-variance extraction prompts with formal specifications and validation criteria.

Updated Aug 23, 2026
Search Tags:#consistency#prompt engineering#data quality#llm#data pipeline#specification design#ai extraction
One-click install
npx skills add https://github.com/darantrute/_virgin-12112025 --skill extraction-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: extraction-design
Source: https://github.com/darantrute/_virgin-12112025/tree/main/.claude/skills/refactoring-design
Command: npx skills add https://github.com/darantrute/_virgin-12112025 --skill extraction-design

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps users design highly precise and consistent AI extraction prompts, eliminating the common problem of inconsistent or "noisy" results (high variance) from LLMs. It formalizes implicit rules into explicit specifications.

Core Features & Use Cases

  • Socratic Interrogation: Guides users through an 11-step Socratic workflow to uncover and formalize extraction rules, exposing edge cases and ambiguities.
  • Formal Specification Output: Produces a detailed specification document including decision matrices, gold standard examples, anti-patterns, and validation strategies.
  • Use Case: When building a new data extraction pipeline using an LLM, use this skill to systematically define what to extract, how to handle edge cases, and how to validate results, ensuring high accuracy and low variance from the AI.

Quick Start

Use the extraction-design skill to start a systematic workflow for extracting customer feedback from support tickets.

Frequently Asked Questions about extraction-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce inconsistency in LLM extraction results?▼

Extraction inconsistency stems from ambiguous prompts. Design precise extraction prompts using a systematic Socratic workflow that formalizes implicit rules into explicit specifications, decision matrices, and validation criteria, eliminating variance in AI-generated outputs.

What's the process for designing a low-variance extraction prompt?▼

Follow an 11-step Socratic interrogation workflow to uncover extraction rules, define edge cases, and create a formal specification. The process requires concrete examples, clear extraction type definition, and produces decision matrices, gold standard examples, and validation strategies.

When should I formalize extraction specifications before deploying an LLM pipeline?▼

Formalize specifications before building production data pipelines to ensure consistency. Defining extraction rules, edge-case handling, and validation criteria upfront prevents high-variance results and reduces costly retraining when LLM outputs diverge.

How do I identify and resolve ambiguities in extraction prompts?▼

Review prompts for ambiguities using structured interrogation that exposes unclear rules and edge cases. The Socratic workflow surfaces hidden assumptions and formalizes them into explicit decision logic, anti-patterns, and validation guidance.

Can I use this approach for any type of data extraction?▼

The workflow applies to any LLM extraction task provided you supply concrete examples and define the extraction type clearly. The Socratic process scales from simple field extraction to complex multi-step data pipelines requiring detailed specification.

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