Individual skill

benchmark-optimization-loop

Convert 'make it faster' requests into a bounded measured optimization loop — baseline first, generate one-hypothesis variants, benchmark each against a correctness gate, and promote the fastest safe variant with reproducible commands. Use when asked to speed something up, try many variants, run recursive optimization, benchmark latency/throughput/cost, or pick the best implementation by repeated measured tests.

pi/core/skills/benchmark-optimization-loop/SKILL.md

Read pinned source

Source commit b38be6a7f6350964d59328b52f0624e1d1d552d1

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 3, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
299,612
Repository last updated
Nov 6, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Benchmark Optimization Loop

Use this skill to convert "make it 20x faster" or "try 50 recursive
optimizations" into a bounded measured loop that can actually improve a system.

## Required Baseline

Do not optimize until these exist:

- the operation being optimized;
- the correctness gate that must stay green;
- the metric: wall time, p95 latency, rows/sec, cost/run, memory, error rate;
- the current baseline;
- the search budget: max variants, max time, max spend, max data impact.