1-min-eval_skill

This skill quickly evaluates a Python codebase using Gemini CLI, producing metrics, rankings, and progress reports.
  • Python

0

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

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npx veilstrat add skill topvibecoder/eval --skill 1-min-eval

  • .gitignore.example135 B
  • SKILL.md5.4 KB

Overview

This skill performs a fast, parallel codebase evaluation using the Gemini CLI and a set of structured metrics. It scans a repository, runs multiple metric evaluations concurrently, aggregates results into a human-readable report, and submits scores to a ranking API to track progress over time.

How this skill works

The tool first scans the codebase and extracts a repo tree and source files with line numbers while skipping common noise folders. It then runs parallel Gemini evaluations for each selected metric, aggregates JSON outputs into a final markdown report, and visualizes scores with a terminal bar chart. After aggregation it can submit results to the TopVibeCoder ranking API and append ranking history for longitudinal tracking.

When to use it

  • Quickly benchmark a project before demos or PR reviews
  • Track improvements across iterations by saving ranking history
  • Compare similar projects by submitting to a public ranking API
  • Automate periodic quality checks in CI for early feedback
  • Generate a concise report for stakeholders or onboarding

Best practices

  • Exclude heavy or irrelevant directories (node_modules, .git, .gemini) to speed scans
  • Limit included characters with EVAL_MAX_CHARS for very large repos
  • Run evaluations in parallel but cap EVAL_PARALLEL to avoid resource contention
  • Use metadata.json to provide accurate name/description for better ranking context
  • Add .evals/ to .gitignore to avoid committing evaluation artifacts

Example use cases

  • Run a one-minute evaluation before a demo to surface major UX or technical issues
  • Add as a pre-merge check to catch regressions in prompt design or integration
  • Measure impact and creativity of different prototype branches
  • Generate a report to include in a project README or developer handoff
  • Track product improvements month-over-month using ranking_history.jsonl

FAQ

Yes — use the --no-ranking flag to run local evaluations without sending data.

What metrics are evaluated by default?

Default metric set includes impact, technical, creativity, presentation, and prompt_design; additional metrics can be enabled.

How do I include more code context for large repos?

Increase EVAL_MAX_CHARS or pass --max-chars when scanning to include more source content.

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