chatgpt canvas workflow pro

    1

    A disciplined multi-pass workflow for precise document editing and code refactoring inside ChatGPT Canvas.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    chatgpt canvas workflow pro

    Example session with this skill installed

    I have a Python function in Canvas that is failing silently. I need you to run a debug pass to find the bug and then add logging to every early return.

    • Read your context and instructions
    • Compiled the chatgpt canvas workflow
    • Generated the document

    I will start the debug pass on your Python function. I'll first add explanatory comments to identify the logic flow, then locate the silent failure.

    Debug and Logging Audit

    StepActionDescription
    1Logical MappingInserted # comments above lines 12, 18, and 24 to trace variable state.
    2Bug DetectionIdentified silent failure at line 29: the except block catches Exception but does not log or re-raise.
    3Logging InsertionAdded logger.debug() calls to the three early return paths at lines 15, 21, and 34.
    4Fix ProposalProposed changing pass to logger.exception("Validation failed") in the catch block.
    import logging
logger = logging.getLogger(__name__)

def validate_user_session(session_id, user_data):
    # Check if session_id is provided
    if not session_id:
        logger.debug("Early return: Missing session_id")
        return False
    
    # Verify user_data contains required keys
    if "uid" not in user_data:
        logger.debug("Early return: Missing uid in user_data")
        return False
        
    try:
        # Simulate database lookup that might fail
        record = db.fetch(session_id)
        if not record:
            logger.debug("Early return: No record found for session_id")
            return False
    except Exception:
        # BUG: This was previously a silent 'pass'
        logger.exception("Database connection failure during session validation")
        return False
        
    return True

    Next steps

    1. Paste the updated code into your Canvas document.
    2. Verify that your logging configuration (level/handler) is set to capture DEBUG outputs.
    3. Confirm if you want to replace the generic Exception catch with a specific DatabaseError.

    chatgpt-canvas-workflow-pro.pdf

    PDF · document

    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

    Refactor large code blocks using targeted inline edits to avoid regressions.Restructure long-form documents while preserving all original factual claims.Insert consistent logging across complex logic gates and early return paths.Convert prose or code between formats while maintaining structural integrity.

    About this skill

    The problem

    Large-scale editing and coding in a standard chat window lead to context loss, truncated code blocks, and messy versioning. You need a way to perform precise, multi-step revisions without the agent rewriting the entire file or losing track of your original intent.

    What it does

    • Routes tasks between chat and Canvas based on artifact complexity and revision needs.
    • Executes targeted inline edits using precise coordinate prompts to prevent meaning drift.
    • Performs structured code reviews and debug traces with line-by-line logical analysis.
    • Handles prose restructuring and style polishing while strictly preserving factual claims.
    • Converts code and documents across formats while maintaining original naming and error handling.

    Frameworks & tools

    ChatGPT Canvas (GPT-4o+), HTML/CSS/JS for live previews, and Markdown for document structuring.

    Why this beats prompting it yourself

    Standard prompts often result in the AI rewriting everything, which wastes tokens and introduces bugs. This skill uses a disciplined, multi-pass workflow that separates restructuring from polishing, ensuring no data is dropped during the transition. It enforces a "validate then move" protocol that prevents the hallucination of new claims or code behaviors.

    Use cases

    • Refactoring a complex Python script into TypeScript while keeping all docstrings intact.
    • Restructuring a technical report into new sections without losing specific data points.
    • Adding debug logging to every error path in a large codebase using a specific format.
    • Performing a style polish on an essay to reduce word count by 20% without adding new arguments.

    Known limitations

    Does not support direct UI automation like clicking buttons. Limited to tasks within the ChatGPT environment; no external network requests or file system writes.

    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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    Recently published to Agensi

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    Security scanned

    Verified clean 9 days ago

    • Passed all security checks, Safe to install

    Listed9 days ago

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