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meta-harness-optimization — Claude Code skill

A skill for automated search over model harnesses: the scaffolding that decides what an agent stores, retrieves and sees while it works.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add reason-machines/trending-skills --skill meta-harness-optimization -a claude-code
Open on GitHub

What meta-harness-optimization does

A harness is everything wrapped around a fixed base model — memory, retrieval, prompt assembly, tool calls. Meta-Harness searches that space instead of you hand-tuning it. A proposer agent suggests a variant, an evaluator scores it on a benchmark, and the result feeds the next round.

Two reference experiments ship with it: reference_examples/text_classification for memory-system search and reference_examples/terminal_bench_2 for scaffold evolution. Dependencies are per-experiment through uv and pyproject.toml. There is no global pip step, but the uv sync and uv run commands assume uv is on your machine.

For a new domain you start from ONBOARDING.md: open it in Claude or Cursor, talk through the task, and you get a domain_spec.md covering what the harness controls, how quality is measured and what the proposer may change. From there you write your own harness.py, evaluator.py and claude_wrapper.py.

What it helps with

  • pick a memory strategy for a text classifier without manual trial and error
  • run scaffold evolution on Terminal-Bench 2
  • describe a new domain for harness search
  • compare harness variants against a numeric score
  • wire up a propose-evaluate-feedback loop for an agent

Example prompts for the agent

  • Set up meta-harness for my text classification project
  • Run one optimization iteration in reference_examples/text_classification
  • Help me fill in domain_spec.md for a new domain

How to install meta-harness-optimization

1Skills CLI (Vercel)

npx skills add reason-machines/trending-skills --skill meta-harness-optimization -a claude-code

2Manually: put the files in place

~/.claude/skills/meta-harness-optimization/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/meta-harness-optimization/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about meta-harness-optimization

How do I install meta-harness-optimization in Claude Code?

Run npx skills add reason-machines/trending-skills --skill meta-harness-optimization -a claude-code. The skill lands in ~/.claude/skills/meta-harness-optimization/SKILL.md.

What does meta-harness-optimization do?

It lays out a loop for automated harness search: a proposer agent emits a variant, an evaluator scores it on a benchmark, and the score goes back into the history for the next step.

What do I need to run the reference experiments?

The uv package manager. Each experiment runs uv sync first, then uv run, with dependencies declared in its own pyproject.toml.

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