Auto Balancer

Use this skill to run controlled parameter tuning loops with deterministic validation gates.

Workflow

1. Define balancing contract.

• Declare target metrics, tolerances, hard constraints, and stop conditions.
• Declare which parameters are allowed to move and their bounds.

1. Establish baseline and iteration plan.

• Record baseline metrics before tuning.
• Apply small, traceable parameter changes per iteration.
• Track config hash/version for each run.

1. Run balance loop.

• Execute simulation/evaluation runs.
• Compare observed metrics to targets and compute deltas.
• Keep only changes that improve objective without violating hard constraints.

1. Validate gate conditions.

• Check each metric against tolerance range.
• Fail immediately on hard-constraint breaches.
• Require minimum run count before final pass.

1. Prepare sign-off handoff.

• Return final parameter set, metric table, and failed/passed gates.
• Include patch plan and exact verification commands.

Commands

python3 scripts/validate_balance_runs.py \
  --input <path/to/balance_runs.json> \
  --spec <path/to/target_spec.json>

Treat non-zero exits as blocker results.

Output Contract

Return:

1. Target Contract: metrics, tolerances, and constraints.
2. Run Summary: baseline, best run, and final run deltas.
3. Gate Results: pass/fail per metric and per hard constraint.
4. Patch Plan: exact files/params to update.
5. Residual Risks: unresolved drift or instability concerns.

References

• references/workflow.md: balancing process and iteration order.
• references/metric-rules.md: tolerance and hard-constraint rules.
• references/signoff-template.md: balancing sign-off template.

Execution Rules

• Keep balancing changes bounded and reversible.
• Keep hard constraints non-negotiable.
• Keep baseline comparison in every report.
• Flag non-convergent loops as blockers.