AI Prompt Optimizer & Engineer

    1

    Transforms rough task ideas into structured, production-ready prompts for specific AI models.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseOpenClawOpenClaw+21 more

    AI Prompt Optimizer & Engineer

    Example session with this skill installed

    Refine this task: "Write a Python script to clean a CSV file." Target model: Claude 3.5 Sonnet.

    • Read your context and instructions
    • Compiled the ai prompt optimizer
    • Generated the data export

    I have engineered a structured prompt for Claude 3.5 Sonnet to handle CSV cleaning tasks with high reliability.

    Engineered Prompt

    ROLE
    You are a senior Python developer specializing in robust, memory-efficient data engineering and pandas-based ETL pipelines.

    CONTEXT
    You are writing a Python script to clean a raw CSV file. The script needs to be production-ready, handling common data quality issues like missing values, incorrect types, and malformed rows. The user will provide a CSV schema or sample data.

    INSTRUCTIONS

    1. Import necessary libraries, prioritizing pandas for data manipulation and logging for error tracking.
    2. Load the CSV file at [FILE_PATH] using appropriate encoding and error handling for malformed lines.
    3. Identify and handle missing values based on the strategy: [MISSING_VALUE_STRATEGY].
    4. Validate and convert columns to the following data types: [COLUMN_TYPES_MAP].
    5. Remove duplicate rows and strip whitespace from all string-based columns.
    6. Implement a basic logging system to report the number of rows processed and any dropped records.
    7. Export the cleaned dataframe to a new CSV file at [OUTPUT_PATH].

    CONSTRAINTS
    Use Python 3.10+ syntax. Prioritize vectorized pandas operations over loops. Avoid hardcoding file paths; use the provided placeholders. Do not use generic try-except blocks without specific logging. Ensure the code is PEP 8 compliant.

    OUTPUT FORMAT
    A single, self-contained Python script (.py) including:

    • Import block
    • Configuration constants
    • A clean_data() function
    • A main execution block with basic CLI feedback

    EDGE CASES

    • If the CSV is empty, the script should log a warning and exit gracefully instead of crashing.
    • If a column specified for type conversion is missing, the script must raise a specific KeyError with a helpful message.
    • If the file encoding is not UTF-8, use chardet to detect the encoding before reading.

    Next steps

    1. Copy the prompt into a new Claude chat.
    2. Replace the [BRACKETED] placeholders with your specific file paths and cleaning rules.
    3. Run the generated code against a sample of your dataset.

    ai-prompt-optimizer-engineer.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Convert vague ideas into structured six-part production prompts.Standardize system prompts for AI-driven applications and agents.Optimize existing prompts to reduce hallucinations and generic output.Draft model-specific instructions for Midjourney or DALL-E image generation.

    About this skill

    The problem

    Writing reliable prompts is time-consuming and often results in inconsistent AI outputs. Most users struggle to define clear constraints, leading to hallucinations or generic responses that require constant manual correction.

    What it does

    • Standardizes rough ideas into structured six-part production-ready prompts.
    • Builds model-specific instructions for ChatGPT, Claude, Gemini, or Midjourney.
    • Generates unambiguous numbered instructions and concrete constraints to prevent AI filler.
    • Identifies and handles potential edge cases for the target prompt to ensure reliability.
    • Includes bracketed placeholders for variable data to make prompts reusable.

    Why this beats prompting it yourself

    It replaces trial-and-error with a proven engineering framework. Instead of guessing how to structure a system prompt, you get a copy-paste deliverable that enforces strict boundaries and clear output formats, significantly reducing model drift.

    Use cases

    • Creating robust system prompts for LLM-powered applications.
    • Refining vague team requests into technical prompts for specific models.
    • Building reusable prompt templates for high-volume content workflows.
    • Engineering precise image generation prompts with defined styles and framing.

    Known limitations

    Requires a basic description of the task to function effectively. Will ask one clarifying question if the input is too vague to act upon.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Security scanned

    Verified clean 21 days ago

    • Passed all security checks, Safe to install

    Listed21 days ago

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