/harness:health

Check eval dataset quality and auto-correct issues. Can be run independently or is invoked by

/harness:evolve

before the iteration loop.

Prerequisites

.evolver.json

must exist. If not, tell user to run

/harness:setup

.

Resolve Tool Path and Python

TOOLS="${EVOLVER_TOOLS:-$([ -d ".evolver/tools" ] && echo ".evolver/tools" || echo "$HOME/.evolver/tools")}"
EVOLVER_PY="${EVOLVER_PY:-$([ -f "$HOME/.evolver/venv/bin/python" ] && echo "$HOME/.evolver/venv/bin/python" || echo "python3")}"

1. Run Health Diagnostic

$EVOLVER_PY $TOOLS/dataset_health.py \
    --config .evolver.json \
    --production-seed production_seed.json \
    --output health_report.json 2>/dev/null

Print summary:

python3 -c "
import json, os
if os.path.exists('health_report.json'):
    r = json.load(open('health_report.json'))
    print(f'Dataset Health: {r[\"health_score\"]}/10 ({r[\"example_count\"]} examples)')
    for issue in r.get('issues', []):
        print(f'  [{issue[\"severity\"]}] {issue[\"message\"]}')
    if not r.get('issues'):
        print('  No issues found.')
"

2. Auto-Correct Issues

If

health_report.json

has corrections, apply them automatically:

CORRECTIONS=$(python3 -c "
import json, os
if os.path.exists('health_report.json'):
    r = json.load(open('health_report.json'))
    for c in r.get('corrections', []):
        print(c['action'])
" 2>/dev/null)

For each correction:

If

create_splits

: Assign 70/30 train/held_out splits:

$EVOLVER_PY -c "
from langsmith import Client
import json, random
client = Client()
config = json.load(open('.evolver.json'))
examples = list(client.list_examples(dataset_name=config['dataset']))
random.shuffle(examples)
sp = int(len(examples) * 0.7)
for ex in examples[:sp]:
    client.update_example(ex.id, split='train')
for ex in examples[sp:]:
    client.update_example(ex.id, split='held_out')
print(f'Assigned splits: {sp} train, {len(examples)-sp} held_out')
"

If

generate_hard

: Spawn testgen agent to generate hard examples:

Agent(
  subagent_type: "harness-testgen",
  description: "Generate hard examples to rebalance dataset",
  prompt: "The dataset is skewed toward easy examples. Generate {count} HARD examples that the current agent is likely to fail on. Focus on edge cases, adversarial inputs, and complex multi-step queries. Read .evolver.json and production_seed.json for context."
)

If

fill_coverage

: Spawn testgen agent for missing categories:

Agent(
  subagent_type: "harness-testgen",
  description: "Generate examples for missing categories",
  prompt: "The dataset is missing these production categories: {categories}. Generate 5 examples per missing category. Read .evolver.json and production_seed.json for context."
)

If

retire_dead

: Move dead examples to retired split:

$EVOLVER_PY -c "
from langsmith import Client
import json
client = Client()
report = json.load(open('health_report.json'))
dead_ids = report.get('dead_examples', {}).get('ids', [])
config = json.load(open('.evolver.json'))
examples = {str(e.id): e for e in client.list_examples(dataset_name=config['dataset'])}
retired = 0
for eid in dead_ids:
    if eid in examples:
        client.update_example(examples[eid].id, split='retired')
        retired += 1
print(f'Retired {retired} dead examples')
"

After corrections, log what was done.

3. Report

Print final health status. If critical issues remain that couldn't be auto-corrected, warn the user.