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How to adapt the harness bank to a new task domain with Harness Zero

Use the bundled evolution skills to build student and reference harnesses for another domain.

  • Research
  • Advanced
  • 3 hours
  • From the README
  • Any agent

Who it is forA researcher who has a new benchmark task set and wants to apply the same harness distillation method.

The problemEach domain needs its own student harness and reference harness, and the method is hard to start without a worked pattern.

The outcomeA new folder in harness_bank with an evolved student-side harness and an adapted reference harness, modeled on the existing domains.

What you need first

  • Python 3.12 to 3.13 and uv installed
  • Docker running, since rollouts run in local Docker sandboxes via Harbor
  • API key for your model route (OpenAI, Azure AI Foundry, or OpenRouter) in a .env file copied from .env.example
  • A Tinker key (TM_API_KEY) if you plan to run training

Steps

  1. Run uv sync and read the repository layout section of the README.
  2. Open the existing folders under harness_bank/ and compare the *_student harness with the reference harness.
  3. Read the skills in .agents/skills/ that were used to evolve and adapt the harnesses.
  4. Create a new folder under harness_bank/ for your domain and place the task set under data/ in the same Harbor format used by the three benchmarks. (inferred, check the README)

Prompt for your agent

Using the repo metaevo-ai/harness-zero, study harness_bank/uspto and the skills in .agents/skills/, then draft a new harness_bank folder for the domain <domain-name>. Expected output: a folder layout and a short plan listing the student harness and reference harness files. Verify that the plan matches the existing per-domain structure before writing any files.

Caveat. The README describes the evolution skills only at a high level, so the new domain setup will require reading the skill files closely.

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