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How to test whether an evolved skill actually improved results with Wikiskill

Use paired per-task comparison with an exact binomial p-value to judge a skill change.

Who it is forA team lead or researcher who has an evolved skill and needs evidence it helps before shipping it to other agents.

The problemA single average score can hide skills that help on some tasks and hurt on others.

The outcomeA per-task win, loss and tie count with a two-sided exact-binomial p-value showing whether the skill's effect is distinguishable from chance.

What you need first

  • Python 3.10 or newer
  • Hermes Agent installed as the reference backend, or another supported agent CLI (Claude Code, Codex or GitHub Copilot CLI)
  • A configured default model for the agent, for example through OpenRouter using --model and --provider
  • Enough API budget for several iterations (the README cites about $0.09 per iteration on one free-tier run)

Steps

  1. Run pip install wikiskill if the package is not installed yet.
  2. Run wikiskill init demo and then wikiskill evolve --iters 3 to produce evolved skills.
  3. Run wikiskill compare to get the paired statistical comparison.
  4. Read the win, loss and tie counts and the p-value in the output.
  5. Check docs/COMPARING.md in the repo for how to interpret the result.

Prompt for your agent

In the ashutoshsinghpr7/wikiskill repo, run `wikiskill compare` on the demo workspace after an evolve run. Report the per-task wins, losses, ties and the two-sided exact-binomial p-value. Verify that the comparison used the same validation tasks for both the baseline and the evolved skills.

Caveat. The compare command needs an evolve run first, so the result only covers the tasks in the bundled or your own bench.

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