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

このスキルについて

pytestとMakefileターゲットを使用した高度な画像テストと検証が必要なMachine Learningエージェントに最適です。 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

pytestとMakefileターゲットを使用した高度な画像テストと検証が必要なMachine Learningエージェントに最適です。 Run tests in mflux (fast/slow/full), preserve image outputs, and handle golden image diffs safely.

このスキルを使用する理由

pytestを使用して画像生成を行い、Makefileターゲットを使用してテストワークフローを効率的に管理し、MLXネイティブ実装と最先端の生成画像モデルを活用して、エージェントが高速テストと低速テストを実行できるようにします。

おすすめ

pytestとMakefileターゲットを使用した高度な画像テストと検証が必要なMachine Learningエージェントに最適です。

↓ 実現可能なユースケース for mflux-testing

画像生成を必要としない高速テストを`make test-fast`で実行する
テストの失敗をデバッグし、画像/ゴールデン間の差異を分析する
画像生成を使用してテストを実行する`make test-slow`、および出力を検査するために保存する

! セキュリティと制限

  • 画像生成テストにはpytestが必要
  • 効率的なテストにはMakefileターゲットが必要
  • 出力を検査するために保存しますが、明示的に要求されない限り、リファレンス画像は更新されません

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

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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?

pytestとMakefileターゲットを使用した高度な画像テストと検証が必要なMachine Learningエージェントに最適です。 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: 画像生成を必要としない高速テストを`make test-fast`で実行する, テストの失敗をデバッグし、画像/ゴールデン間の差異を分析する, 画像生成を使用してテストを実行する`make test-slow`、および出力を検査するために保存する.

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?

画像生成テストにはpytestが必要. 効率的なテストにはMakefileターゲットが必要. 出力を検査するために保存しますが、明示的に要求されない限り、リファレンス画像は更新されません.

↓ 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.

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