
meridian-result-visualization
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).
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/google/meridian --skill meridian-result-visualization- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads meridian-result-visualization automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
- Present the current proposed configurations, report paths, script path, or status to the user for approval.
- 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. - 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.binpbby default). - Prompt the user for output paths for:
- Model Results Summary report (
results_summary.htmlby default).
- Model Results Summary report (
- 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.ModelReviewerto 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
ValueErroror 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.Summarizerto 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 settingsample_prior=Falseor 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. If your file-writing tool can ask the user to review the written file, don't request that review: the pre-execution checkpoint already asks for approval, and an extra prompt pauses the workflow.
- Execute the script with the Python interpreter of the environment where
google-meridianis installed: the one the user named; elsepython3if a virtual environment is already active (VIRTUAL_ENVorCONDA_PREFIXis set); else the project directory's virtual environment (.venv/orvenv/, e.g.,.venv/bin/python3) if there is one; elsepython3. Ifimport meridianfails, ask the user which environment to use instead of searching the machine or installing packages unprompted. - 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.
FAQ
What does the meridian-result-visualization skill do?
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).
How do I install meridian-result-visualization?
Run npx skills add https://github.com/google/meridian --skill meridian-result-visualization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/meridian-result-visualization/ (personal) or .claude/skills/meridian-result-visualization/ (project).
Which AI agents can use meridian-result-visualization?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is meridian-result-visualization safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains meridian-result-visualization?
It is published by google in google/meridian. It has 56 recorded installs.
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