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/meridianbudgetoptimization

@a000de2
by googlegoogle/meridian1.6k stars
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Loads a fitted Meridian model and runs budget optimization scenarios. Use when the user wants to load a saved model and find the optimal budget allocation or target ROI spending. Don't use for visualizing model performance (use meridian-result-visualization) or building/fitting the model (use meridian-model-building).

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  • Updated last month
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Use this Skill: https://skilld.dev/gh/google/meridian/meridianbudgetoptimization

This session only. Nothing lands on disk.

SKILL.md

≈87 tokens always: the name and description. ≈1.1k when used: this file. ≈861 more on demand in 2 files.

Meridian Budget Optimization

A skill for loading a fitted Meridian model and running budget optimization 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.

1. Initial Setup

  • Prompt the user for the path to the serialized model file (meridian_model.binpb by default).
  • Prompt the user for the output path for the Budget Optimization report (optimization.html by default).
  • Prompt the user for the desired path for the generated Python script. If unspecified, default to model_build/run_budget_optimization.py (or relative to the model directory).
  • CRITICAL INTERACTIVE CHECKPOINT: Present the gathered paths to the user and obtain confirmation before configuring optimization parameters.

2. Configure Optimization Parameters & Interactive Checkpoint

  • Prompt the user to configure the optimization scenario. See optimization.md for the full list of available parameters. Key configurations include:
    • Fixed vs Flexible Budget: Is the total budget fixed, or are we looking for a budget to hit a target ROI/mROI? (Defaults to Fixed).
    • Total Budget: If fixed, what is the total budget? (Defaults to historical spend).
    • Spend Constraints: What are the upper/lower bounds for spend shift per channel? (Defaults to 0.3 for fixed, 1.0 for flexible).
    • Target ROI / Target mROI: If flexible, what are the target ratios?
    • Reach & Frequency Parameters: Should optimal frequency be used? (Defaults to True for RF channels).
  • CRITICAL INTERACTIVE CHECKPOINT: Present the proposed optimization configurations to the user and obtain approval before continuing to generate the code.

3. 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 budget optimization.

4. Add Budget Optimization Code

  • Use optimizer.BudgetOptimizer to run optimization and generate the summary, applying the confirmed configuration.
  • See optimization.md for code template.

5. Pre-execution Checkpoint

  • CRITICAL INTERACTIVE CHECKPOINT: Ask the user for final confirmation to execute the budget optimization 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.

Source: SKILL.md on GitHub

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Signed by skilld at a000de2. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 5 hours ago.

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