인터페이스는 현지화되어 있으며, Skill 소개와 설명 문서는 선택한 언어로 AI가 자동 번역합니다. 코드·명령어·경로·전문 용어는 원문을 유지하며 원작자 문서가 우선합니다.
Skills 둘러보기
생산성 및 협업 · SKILL.md

evaluation-and-performance

evaluation-and-performance

Guides FastVideo media metrics, benchmark configuration, quality regression checks, and honest performance comparisons.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: evaluation-and-performance description: "Guides FastVideo media metrics, benchmark configuration, quality regression checks, and honest performance comparisons." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Evaluation and performance

Use for registered metrics, video/image/audio evaluation, benchmark requests, latency/memory comparisons, and regression interpretation.

Workflow

  1. Fix model revision, prompt/data sample, seed, dimensions, FPS, steps, backend, precision, and output policy.
  2. Decide whether the operation is a metric run, a server benchmark, a quality regression, or a component profile. Read evaluation reference and performance reference.
  3. Install only the metric extra required by the selected dataset/metric. Some judge metrics call remote APIs and need credentials; do not enable them in a safe local smoke.
  4. Warm up compiled or kernel-backed runs, exclude startup/download/compile time, and compare steady-state runs with identical shapes.
  5. Preserve raw outputs, config, metric versions, and failure/skipped reasons.

The CLI exposes fastvideo eval list, fastvideo eval run, and fastvideo bench for benchmark-server workloads. Use evaluation troubleshooting when metrics, codecs, references, or server connectivity fail. Run the bundled metric inspector for a read-only registry listing when the package is installed.

저장한 Skills