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performance-tradeoff

Judge performance or resource claims on real hardware: benchmark design or review, latency, throughput, memory or headroom numbers, and accept-or-reject decisions on optimizations. Not for refactors with no resource claim.

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  • agents/openai.yaml205 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Performance tradeoff

Before trusting or reporting a number, check:

  • Equivalent arms. Only the intended change differs: build, inputs, load, power/thermal state, output quality. Alternate arm order; a cache warmed by one arm contaminates the next.
  • Separate regimes. Cold, warm and sustained are different results. Claim a cold-start win only from fresh-cache runs of both arms.
  • Noise before change. Compare the delta with each arm's run-to-run spread; overlapping ranges or few samples mean inconclusive. Give latency percentiles with sample counts.
  • Completion, not enqueue. Time async/GPU work to completion, with identical sync and transfer boundaries in both arms.
  • Raw-value arithmetic. Relative change = (candidate − baseline) / baseline, direction stated; recheck supplied percentages. No speedup against a timeout or OOM.
  • One memory scope. Don't sum overlapping counters (a unified-memory footprint may already include GPU allocations). Headroom is peak versus what the target actually has free; note the sampling interval.
  • Phase is not end to end. A faster component isn't a faster request until measured; overlapping work doesn't add serially.

Decide in order: correctness and quality; whether the change enables a supported workload the baseline fails or nearly fails; absolute user-visible cost; maintenance cost. A resource reduction that enables nothing measured doesn't justify a latency, quality or complexity cost. Set thresholds and margins before seeing candidate results.

Answer accept, reject or unproven per hardware and workload case, naming the smallest missing measurement. Report only the metrics that decide it.

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