Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurf+15 more

    Prompt Habits Kit

    by Arnstein Larsen

    1

    Create a system of six reusable, field-specific prompt patterns to ensure consistent AI outputs.

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    See it in action

    You say

    Build my prompt habits for a backend engineer focused on API design.

    Your agent does

    Habit 2 — Negative / anti-pattern list

    Pattern: Tell the model what NOT to do. Template: Design [TASK]. Avoid: [INTERNAL_LEAK], [NON_RESTFUL_PATH], [OAUTH_OMISSION]. Example: "Design the auth flow. Avoid: returning raw DB IDs, using PUT for partial updates, and omitting scopes."

    What you get

    Convert vague requests into structured, repeatable prompt skeletonsEliminate repetitive model errors using targeted anti-pattern listsStandardize AI outputs across a team or specific professional nicheBuild a composable prompt stack that balances context, goals, and constraints

    About this skill

    The problem

    One-off prompts are hard to scale and produce inconsistent results. You spend too much time rewriting instructions from scratch and correcting the same model errors repeatedly.

    What it does

    • Generates six field-specific prompt patterns including WHY-framing and anti-pattern lists.
    • Provides copy-paste skeletons with bracketed variables for immediate use.
    • Supplies worked examples tailored to your specific niche like marketing, dev, or ops.
    • Establishes a composable framework to stack habits for high-complexity tasks.
    • Identifies which prompting strategies offer the highest leverage for your specific profession.

    Why this beats prompting it yourself

    Instead of guessing which instructions stick, this uses a structured spine of triggers, patterns, and guardrails. It shifts your workflow from aspiration-based prompts to testable, negative-constraint logic that reliably kills hallucination and fluff.

    Use cases

    • Creating a library of repeatable templates for a specific department or client.
    • Onboarding team members to a unified prompting standard.
    • Fixing persistent quality issues in model outputs by implementing anti-pattern lists.

    Known limitations

    This kit focuses on structural prompting patterns rather than deep technical prompt engineering like few-shot examples or chain-of-thought logic trees.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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