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How to set a weighted objective that balances pass rate and cost with Gear

Combine pass rate, process scores, and cost or tokens into one objective when tuning a harness.

Short answer: Read the objective guide to see how declared pass rate, process scores, cost, and tokens can be weighted; then Define your weights for pass rate, process scores, and cost or tokens for your task set; run one Refine Skill optimization round on your task set with that weighted objective. All steps.

Who it is forA team lead who must keep agent quality high while limiting API spend and wants to pick the harness that gives the best trade-off.

The problemOptimizing only for pass rate can produce a harness that is correct but expensive to run on every task.

The outcomeA harness selected by a weighted objective, with the original pass rate, process scores, and cost measurements still visible for each version.

What you need first

  • npm installed, to run the global install of rsi-gear, agent-hitch, and DSH
  • An agent environment such as Codex, Claude Code, or DSH that can use the bundled Refine Skill
  • A benchmark of representative tasks with clear success criteria, either an existing one or your own in Harbor format
  • Access to the models you choose for the Meta agent and the task agent

Steps

  1. Read the objective guide to see how declared pass rate, process scores, cost, and tokens can be weighted.
  2. Define your weights for pass rate, process scores, and cost or tokens for your task set.
  3. Run one Refine Skill optimization round on your task set with that weighted objective.
  4. Compare the retained harness with the starting harness on pass rate and on cost or tokens, using the recorded results.

Prompt for your agent

Use the Gear repo at rsi-gear/gear to define a weighted objective that combines pass rate and cost for <target task set> and run one optimization round with it. Report the weights used and the pass rate and cost of the retained harness against the starting harness. Verify that the original measurements are still present in the results.

Caveat. The objective guide defines how weights combine measurements, so choose weights that match your own priorities rather than copying the Marketing experiment's settings.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for rsi-gear/gear and "Set a weighted objective that balances pass rate and cost"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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