mflux-testing — apple-silicon mflux-testing, community, apple-silicon, ide skills, diffusers, huggingface, qwen-image, seedvr2, transformers, z-image, Claude Code

v1.0.0
GitHub

이 스킬 정보

pytest와 Makefile 대상으로 고급 이미지 테스트 및 검증이 필요한 기계 학습 에이전트에 적합합니다. Run tests in mflux (fast/slow/full), preserve image outputs, and handle golden image diffs safely.

# Core Topics

apple-silicon diffusers huggingface qwen-image seedvr2 transformers
filipstrand filipstrand
[1.9k]
[124]
Updated: 3/10/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 9/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
9/11
Quality Score
54
Canonical Locale
en
Detected Body Locale
en

pytest와 Makefile 대상으로 고급 이미지 테스트 및 검증이 필요한 기계 학습 에이전트에 적합합니다. Run tests in mflux (fast/slow/full), preserve image outputs, and handle golden image diffs safely.

이 스킬을 사용하는 이유

pytest를 사용하여 이미지 생성을 수행하고 Makefile 대상으로 테스트 워크플로우를 효율적으로 관리하며 MLX 네이티브 구현 및 최첨단 생성 이미지 모델을 활용하여 에이전트가 빠른 테스트 및 느린 테스트를 실행할 수 있도록 합니다.

최적의 용도

pytest와 Makefile 대상으로 고급 이미지 테스트 및 검증이 필요한 기계 학습 에이전트에 적합합니다.

↓ 실행 가능한 사용 사례 for mflux-testing

이미지 생성이 필요 없는 빠른 테스트를 `make test-fast`로 실행합니다.
테스트 실패를 디버깅하고 이미지/골든 차이를 분석합니다.
이미지 생성을 사용하여 테스트를 실행하는 `make test-slow` 및 출력을 검사하기 위해 보존합니다.

! 보안 및 제한 사항

  • 이미지 생성 테스트에는 pytest가 필요합니다.
  • 효율적인 테스트에는 Makefile 대상이 필요합니다.
  • 출력을 검사하기 위해 보존하지만 명시적으로 요청하지 않는 한 참조 이미지는 업데이트되지 않습니다.

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is mflux-testing?

pytest와 Makefile 대상으로 고급 이미지 테스트 및 검증이 필요한 기계 학습 에이전트에 적합합니다. Run tests in mflux (fast/slow/full), preserve image outputs, and handle golden image diffs safely.

How do I install mflux-testing?

Run the command: npx killer-skills add filipstrand/mflux/mflux-testing. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for mflux-testing?

Key use cases include: 이미지 생성이 필요 없는 빠른 테스트를 `make test-fast`로 실행합니다., 테스트 실패를 디버깅하고 이미지/골든 차이를 분석합니다., 이미지 생성을 사용하여 테스트를 실행하는 `make test-slow` 및 출력을 검사하기 위해 보존합니다..

Which IDEs are compatible with mflux-testing?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for mflux-testing?

이미지 생성 테스트에는 pytest가 필요합니다.. 효율적인 테스트에는 Makefile 대상이 필요합니다.. 출력을 검사하기 위해 보존하지만 명시적으로 요청하지 않는 한 참조 이미지는 업데이트되지 않습니다..

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add filipstrand/mflux/mflux-testing. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use mflux-testing immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

mflux-testing

Install mflux-testing, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

mflux testing

This repo uses pytest with image-producing tests. Always preserve outputs for inspection and never update reference images unless explicitly asked.

When to Use

  • You need to run tests (fast/slow/full) or debug failing tests.
  • There are image/golden mismatches and you need to report paths/output for review.

Instructions

  • Prefer the Makefile test targets:
    • make test-fast (fast tests, no image generation)
    • make test-slow (slow tests, image generation)
    • make test (full suite)
  • Always keep MFLUX_PRESERVE_TEST_OUTPUT=1 on test runs (already built into the Makefile test targets).
  • If a change affects defaults, config resolution, metadata fields, or CLI behavior, add or update tests that cover the changed behavior directly instead of relying only on manual verification.
  • If tests fail:
    • Summarize the failing test names and the key assertion output.
    • Point to any generated images/artifacts on disk for manual review.
  • Do not regenerate/replace reference (“golden”) images unless the user explicitly requests it.

Manual validation (config resolution + local model paths)

Use when a change touches model config resolution, mflux-save, or the model’s generate CLI, or when a PR fixes local model-path handling for the model under investigation. Refer to the mflux-cli skill to find the correct generate command for the model you are testing.

  • Run a local-path quantize/save:
    • Use the mflux-cli skill to look up the correct command and flags.
    • Verify CLI usage with the command’s --help before running it.
    • Save to a known location (e.g., Desktop) to make follow-up steps explicit.
  • Run generation from the saved model using the correct model-specific generate CLI:
    • Use the mflux-cli skill to find the generate command and required flags.
    • Verify CLI usage with the command’s --help before running it.
  • If the model has multiple size variants, repeat the above for each variant to confirm the correct overrides are applied.
  • Do not commit output artifacts; delete or leave them untracked.

관련 스킬

Looking for an alternative to mflux-testing or another community skill for your workflow? Explore these related open-source skills.

모두 보기

openclaw-release-maintainer

Logo of openclaw
openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

★ 333.8k
⑂ 0
인공지능

widget-generator

Logo of f
f

prompts.chat 피드 시스템을 위한 사용자 지정 가능한 위젯 플러그인을 생성합니다

★ 149.6k
⑂ 0
인공지능

flags

Logo of vercel
vercel

리액트 프레임워크

★ 138.4k
⑂ 0
브라우저

pr-review

Logo of pytorch
pytorch

파이썬에서 텐서와 동적 신경망 구현 및 강력한 GPU 가속 지원

★ 98.6k
⑂ 0
개발자