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performance-engineering

/performance-engineering

Model a workload's cost, profile it, and prove claimed speedups. Use when something is too slow or allocation-heavy, when a workload's cost must be modeled, or when a claimed speedup needs proof.

Repository
skills ↗
Licence
MIT
Last repository change
09.11.2026

Assessment and security

85/ 100Class: good
Format25 / 25
Frontmatter with name and description, valid name, description up to 1,024 characters, name matching the folder.
Discoverability25 / 25
Description explains when to use the skill, has a reasonable length and uses third person.
Content20 / 35
At least 150 words and at most 500 lines, headings, step lists, examples or code.
Hygiene15 / 15
No placeholders such as TODO, no Windows paths, with a licence.
Riskno signals detected in the scan
Files in this package
1
Scripts
0
HTML comments
0
URLs in SKILL.md
3
Deprecation

No signals detected. Repository changed on 09.11.2026, without references to retired models or APIs.

Licence

MIT, open: use and modify while retaining author attribution.

What it does and when to use it

Model a workload's cost, profile it, and prove claimed speedups. Use when something is too slow or allocation-heavy, when a workload's cost must be modeled, or when a claimed speedup needs proof.

Original author description

Model a workload's cost, profile it, and prove claimed speedups. Use when something is too slow or allocation-heavy, when a workload's cost must be modeled, or when a claimed speedup needs proof.

How it works

  1. Heap allocations
Original SKILL.md headings · 11 lines, 215 words
SKILL.md preview
---
name: performance-engineering
disable-model-invocation: true
description: "Model a workload's cost, profile it, and prove claimed speedups. Use when something is too slow or allocation-heavy, when a workload's cost must be modeled, or when a claimed speedup needs proof."
---

# Performance Engineering

Pin down the workload, input scale, hardware, budget (latency, throughput, memory), and invariants first. Measure before guessing; separate back-of-the-envelope estimates from measurements, and average cost from tail cost. Optimize mechanisms (memory layout, locality, allocations, thread coordination) and count full-system cost when work shifts across paths; bound any claimed gain by [Amdahl's law](https://doi.org/10.1145/1465482.1465560): optimizing a fraction p of the workload caps end-to-end speedup at 1/(1-p). Preserve ownership, lifetimes, and reentrancy when introducing zero-copy views or pools.

Benchmark baseline and candidate under identical conditions. Call a change a strict win only when it improves a target metric and regresses none; trade-offs on the [Pareto](https://en.wikipedia.org/wiki/Pareto_efficiency) frontier belong to the user, and behavior changes are reported separately. Use automated benchmark loops over large tuning spaces. When the workload data the model needs is missing or conflicting, name the gap and sync with the user instead of guessing.

## Heap allocations

Catalog explicit and hidden allocations across happy, startup, and error paths separately: boxing, closure captures, generator/iterator state machines, variadic arrays, formatting, and library calls (via static allocation analysis, including [escape analysis](https://en.wikipedia.org/wiki/Escape_analysis)). Verify the analysis pass completed cleanly under real compiler flags, chase unanalyzed calls into emitted code or profiler traces on hot paths, and report trigger, lifetime, owner, and runtime frequency separately from static call sites.

Versions

RepositoryScoreLicence
This page · hebtcd/skills85MIT
hmfareed/Frontier-Engineer100MIT
Twistor-Labs/twistor-engineer100MIT
jacob-balslev/skill-graph100Apache-2.0
travisjneuman/.claude95MIT

Database snapshot: 13.11.2026 · package version f9c822317b0d (SHA-256 digest of the entire package)

Downloads in the last 30 days: 0 · ranking

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