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Use harness-engineering when a repository needs clearer maps, validation lanes, and drift control. It audits repo legibility and scaffolds durable repo docs so agents can work with stronger guardrails. Reach for meta-harness instead when the goal is benchmarked harness.py optimization.

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Quick start

Run from the repo root: 
uv run python plugins/harness-engineering/scripts/audit_repo_harness.py --repo . --format markdown
uv run python plugins/harness-engineering/scripts/audit_repo_harness.py --repo . --format json
uv run python plugins/harness-engineering/scripts/scaffold_harness_docs.py --repo . --dry-run
uv run python plugins/harness-engineering/scripts/scaffold_harness_docs.py --repo . --write
In Claude Code: 
claude --plugin-dir ./plugins/harness-engineering
/harness-engineering:harness-engineering

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When to reach for meta-harness

  • You are optimizing one benchmarked LLM task.
  • The main artifact is a candidate harness.py.
  • You need Pareto / frontier tracking across prompt or retrieval variants.
  • The bottleneck is evaluation quality, not repo operating clarity.

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Validation

uv run pytest plugins/harness-engineering/tests
uv run pytest tests/test_plugin_catalogue.py
uv run python plugins/harness-engineering/scripts/audit_repo_harness.py --repo . --format markdown
uv run python plugins/harness-engineering/scripts/scaffold_harness_docs.py --repo . --dry-run