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meta-harness scaffolds a workspace, validates candidate harness.py files, runs an outer-loop search over prompt / retrieval / parsing / memory strategies, and exposes script-based query and report tools for inspecting search state.

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Quick start

Run from the repo root: 
# 0. (Optional) Clarify a fuzzy goal into structured config
uv run python plugins/meta-harness/scripts/clarify_task.py --workspace ./workspace

# 1. Initialize a workspace with baselines and starter config
uv run python plugins/meta-harness/scripts/init_workspace.py \
  --workspace ./workspace \
  --task_type qa \
  --num_baselines 3

# 2. Run the outer-loop search
uv run python plugins/meta-harness/scripts/meta_harness_scaffold.py \
  --workspace ./workspace \
  --iterations 30 \
  --candidates 2

# 3. Inspect the resulting search state
uv run python plugins/meta-harness/scripts/query_cli.py --workspace ./workspace summary
uv run python plugins/meta-harness/scripts/query_cli.py --workspace ./workspace top --metric accuracy -k 5
In Claude Code: 
claude --plugin-dir ./plugins/meta-harness
/meta-harness:meta-harness

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What gets created

init_workspace.py creates the stable workspace scaffold:
  • .gitignore
  • search_config.json
  • eval_results.json
  • proposer_skill.txt
  • workspace_manifest.json
  • history/candidates/candidate_*/harness.py baseline seeds
  • candidates/, logs/, traces/, and reports/ directories
meta_harness_scaffold.py creates the live search artifacts:
  • candidates/candidate_<iteration>_<variant>/harness.py
  • search_state.json
  • updated eval_results.json
  • per-iteration history, Pareto frontier, and best-by-metric state

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Validation

uv run pytest plugins/meta-harness/tests