aggregate_benchmark

Returns dict keyed by config name (e.g. "with_skill"/"without_skill", or "new_skill"/"old_skill"), each containing a list of run results. """ # Support both layouts: eval dirs directly under benchmark_dir, or under runs/ runs_dir =…

Skills
Productivity
6

What it does and how to use it

A skill that teaches Claude Code to do productivity tasks well. It is packaged knowledge the model uses whenever it is relevant.

Install the skill (command above).
In Claude Code, describe what you need - the skill turns on by itself when the context fits.
No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/aggregate-benchmark && curl -fsSL https://doutorai.pt/data/lib/skills__aggregate-benchmark.md -o .claude/skills/aggregate-benchmark/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

#!/usr/bin/env python3
"""
Aggregate individual run results into benchmark summary statistics.

Reads grading.json files from run directories and produces:
- run_summary with mean, stddev, min, max for each metric
- delta between with_skill and without_skill configurations

Usage:
    python aggregate_benchmark.py <benchmark_dir>

Example:
    python aggregate_benchmark.py benchmarks/2026-01-15T10-30-00/

The script supports two directory layouts:

    Workspace layout (from skill-creator iterations):
    <benchmark_dir>/
    └── eval-N/
        ├── with_skill/
        │   ├── run-1/grading.js

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