run-metrics

name	run-metrics
description	Compute numerical metrics for a simulation run (as opposed to the qualitative analysis done by analyze-run). Use when the user says "run metrics", "计算指标", or "compute metrics".

run-metrics

Compute numerical metrics for a simulation run (as opposed to the qualitative analysis done by analyze-run).

Trigger

Use when the user asks to compute run metrics. See the trigger phrases in the frontmatter description.

Workflow

1. Determine data_dir

• Provided by the user → use it directly.
• Not provided → run ls -t data/ | head -5 to list the most recent directories and let the user choose.

2. Run compute_metrics

python scripts/compute_metrics.py --data-dir school_03121031

The world name is inferred from the run config; no extra argument is needed.

3. Produce a summary

Read analysis/results/{data_dir}_metrics.json and output:

• Innate stats: distribution of income/deposit/relationship (min, max, mean, median).
• Per-year key metrics: average deposit change per year, fulfillment trend, social eval trend.
• Outliers: agents whose metrics are extremely high or low.
• Correlation insights: relationships between innate conditions and growth metrics (e.g. income vs deposit_diff).

Format the output as concise tables/lists so the user can grasp the overall picture quickly.