# Perf Loop

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

- Repository: https://github.com/fallow-rs/fallow (5289 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add fallow-rs/fallow --skill perf-loop -g -a claude-code -y`
Codex: `npx skills add fallow-rs/fallow --skill perf-loop -g -a codex -y`
Cursor: `npx skills add fallow-rs/fallow --skill perf-loop -g -a cursor -y`
Gemini CLI: `npx skills add fallow-rs/fallow --skill perf-loop -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Performance loop

1. Choose a stable benchmark and preserve its identity and workload. Before
   you freeze it, prove that it can see the problem: a known slowdown must
   move the result.
2. Record a statistically useful baseline. Set a target and a minimum number
   of rounds. Do not stop before that minimum unless you reach the target.
3. Profile the hot path before editing.
4. Implement one bounded optimization.
5. Re-run the same benchmark and correctness checks.
6. Keep the change only when the improvement is reproducible and no contract
   regresses.
7. Use a new benchmark identifier for a materially different workload.
8. Run `review`.

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