Performance loop

SkillDev tools

Iteratively optimize Fallow performance with stable benchmarks, before-and-after evidence, and correctness gates.

Available today. Use it from your connected AI after setup.

No other account needed.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Performance loop skill

What this skill tells your AI

The instructions your AI receives, as published by fallow-rs/fallow in .agents/skills/perf-loop/SKILL.md and read by ahel’s review.

Choose a stable benchmark and preserve its identity and workload.
Record a statistically useful baseline.
Profile the hot path before editing.
Implement one bounded optimization.
Re-run the same benchmark and correctness checks.
Keep the change only when the improvement is reproducible and no contract regresses.
Use a new benchmark identifier for a materially different workload.
Run review.

Do not report performance gains from debug builds or incomparable fixtures.

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