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How to evolve an agent's skills from its own task traces with Wikiskill

Run the full WikiSkill loop so the agent distills failures into a wiki and gates skill changes.

Who it is forA developer whose coding agent repeatedly fails the same kinds of tasks and who wants its skills improved without rewriting them by hand.

The problemAgent lessons usually disappear when a session ends, and skill edits made by hand are hard to verify.

The outcomeA workspace with a persistent wiki of pattern pages and a skill set where each kept change beat the best validation score so far.

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 to install the package from PyPI.
  2. Run wikiskill init demo to create the workspace and the 22-task demo bench (13 train, 9 validation).
  3. Run wikiskill status to see the baseline scores, skills, wiki and history.
  4. Run wikiskill evolve --iters 3 to start the full Algorithm 1 loop with your default model.
  5. Open workspaces/demo/wiki/ and read index.md and skill-impact.md to see what was learned and which proposals were rejected.

Prompt for your agent

Use the ashutoshsinghpr7/wikiskill repo to create a demo workspace with `wikiskill init demo`, then run `wikiskill evolve --iters 3`. Report the baseline score from `wikiskill status` and whether each proposal was kept or rolled back. Verify that the wiki folder still contains its pattern pages after the run.

Caveat. The README reports that the demo runs are on a small bundled bench, so gains on your own agent may differ.

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