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    Normalize Prompt Set

    by Corey Jacobs

    Convert loose prompt sets into structured, target-ready records with variables, contracts, and eval cases.

    Updated Jun 2026
    0 installs

    Free

    Included in download

    • Downloadable skill package
    • Instant install

    Sample input

    I have two prompts for my customer support bot: one for summarizing tickets and one for drafting replies. Convert them into a structured set for an API target.

    Sample output

    { "set_name": "support-bot-workflow", "use_target": "api", "records": [ { "record_id": "summarize-ticket", "prompt_intent": "Extract key issues and sentiment", "user_message_template": "Summarize this ticket: {{ticket_text}}", "response_schema": { "summary": "string", "urgency": "int" } } ] }

    About This Skill

    Streamline Your Prompt Engineering

    Inconsistent prompts lead to unreliable AI behavior. Normalize Prompt Set is a professional-grade developer tool designed to transform fragmented, loose prompt instructions into structured, production-ready "Prompt Records." Whether you are moving from a messy chat history to a stable API implementation or building a multi-step agentic workflow, this skill provides the rigorous schema needed to ensure operational fidelity.

    What it does

    This skill takes two or more related prompts and subjects them to a "Full Normalization Run." It extracts variables, defines strict input/output contracts, establishes evaluation cases, and generates success criteria. The result is a standardized record precisely formatted for your specific target environment.

    • Variable Standardization: Automatically identifies editable placeholders and defines their types and validation rules.
    • Contract Definition: Specifies exactly what data must be provided and how the response must be shaped.
    • Target Optimization: Tailors the output for ChatGPT, Custom GPTs, Stateful Agents, or API-bound development.
    • Reliability Testing: Generates an 'Eval Case' for every record to ensure predictable performance.

    Why use this skill?

    Prompting an AI manually often results in "vibes-based" engineering. This skill replaces guesswork with a repeatable data structure. It is ideal for developers who need to hand off prompts to non-technical users, integrate LLMs into software via API, or manage complex instruction sets for Custom GPTs without hitting token limits or losing behavioral consistency.

    Use Cases

    • Stabilize messy chat prompts into production-ready API system messages.
    • Define strict JSON output schemas for consistent LLM data extraction.
    • Build modular instruction blocks for Custom GPTs and AI Agents.
    • Standardize variable placeholders and validation rules across prompt sets.

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    Security Scanned

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    Permissions

    No special permissions declared or detected

    Tags

    prompt-engineering
    llm-ops
    api-development
    automation
    structured-data

    Compatible with SKILL.md-compatible agents.

    Creator

    Corey Jacobs

    AI Autodidact living at the edge.

    Frequently Asked Questions

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