meridian-result-visualization

name	meridian-result-visualization
description	Loads a fitted Meridian model and generates standard visualization reports: Model Results Summary. Use when the user wants to load a saved model and view results (model performance, fit, health). Don't use for running budget optimization (use meridian-budget-optimization) or building/fitting the model (use meridian-model-building).

Meridian Result Visualization

A skill for loading a fitted Meridian model and generating standard visualization reports.

Core Workflow

Interactivity Checkpoint Rule

Throughout this workflow, you will encounter CRITICAL INTERACTIVE CHECKPOINTs. At each checkpoint, you MUST:

1. Present the current proposed configurations, report paths, script path, or status to the user for approval.
2. Ask the user if they are ready to proceed using the available user-interaction tool (e.g., ask_question), structured as a multiple-choice question. Do NOT use raw chat text.
3. Wait for their response before proceeding.
   • MANDATORY: You MUST pause at every checkpoint regardless of the initial prompt instructions (even if the user request contains phrases like "run autonomously", "execute directly", "fix autonomously", etc.). The initial request does NOT bypass these interactive checkpoints.
   • Note: If the user replies to a checkpoint with a generic approval (e.g., "proceed", "do what you think is best"), proceed with the proposed defaults.

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1. Initial Setup

• Prompt the user for the path to the serialized model file (meridian_model.binpb by default).
• Prompt the user for output paths for:
  • Model Results Summary report (results_summary.html by default).
• Prompt the user for the desired path for the generated Python script.
• Prompt the user for any optional configuration for the reports (e.g., date ranges for results summary).
• CRITICAL INTERACTIVE CHECKPOINT: Present the gathered paths and configurations to the user and obtain confirmation before loading the model.

2. Add Model Loading Code

• Use meridian_serde.load_meridian() to load the model.
• See load_model.md for code template.
• CRITICAL INTERACTIVE CHECKPOINT: Present the model load path configuration and proposed Python code snippet to the user, and obtain approval before continuing to specify health checks.

3. Add Post-Modeling Health Checks Code (Optional)

• Use reviewer.ModelReviewer to run health checks and save to HTML.
• Small / Test Models: If a model has fewer than 2 MCMC draws (e.g. test or mock models where R-hat computation requires >= 2 samples), catch ValueError or skip R-hat calculation gracefully so health check reports generate cleanly.
• See health_check.md for code template.
• CRITICAL INTERACTIVE CHECKPOINT: Present the proposed health check report path and code snippet to the user, and obtain approval before continuing to results summary configuration.

4. Add Model Results Summary Code

• Use summarizer.Summarizer to generate the HTML results summary, applying any user-specified configuration (e.g., date ranges).
  • Date Range Auto-Clipping: If applying a user-specified date range falls outside the model's time coordinates, clip or adjust the date range to match the model's actual coordinates.
  • Tip for errors: If you encounter errors regarding sample_prior, bypass or fix the requirement (for example, by setting sample_prior=False or passing the required parameters) to ensure successful generation.
• See results_summary.md for code template.
• CRITICAL INTERACTIVE CHECKPOINT: Present the proposed date ranges, output path configuration, and code snippet for the results summary to the user, and obtain approval before proceeding to script generation and execution.

5. Pre-execution Checkpoint

• CRITICAL INTERACTIVE CHECKPOINT: Present the final report path and script path to the user, and ask for final confirmation to execute the results generation script now.

6. Execution & Script Setup

• Write the accumulated Python script to the user-specified path. When writing the file using write_to_file, explicitly set ArtifactMetadata.RequestFeedback=false to avoid pausing execution.
• Execute the script using Python: prefer the active virtual environment if available (e.g. .venv/bin/python3 or /tmp/meridian_eval_cache/bin/python3, otherwise python3).
• CRITICAL: Do NOT delete the generated reports, the script, or the output directory at the end of the task. These are the deliverables requested by the user and must be preserved.