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

v1.0.0
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Acerca de este Skill

Perfecto para Agentes de Aprendizaje Automático que necesitan capacidades avanzadas de prueba y validación de imágenes con pytest y objetivos de Makefile. 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

Perfecto para Agentes de Aprendizaje Automático que necesitan capacidades avanzadas de prueba y validación de imágenes con pytest y objetivos de Makefile. Run tests in mflux (fast/slow/full), preserve image outputs, and handle golden image diffs safely.

¿Por qué usar esta habilidad?

Habilita a los agentes a ejecutar pruebas rápidas y lentas con generación de imágenes usando pytest, y a gestionar de manera eficiente los flujos de trabajo de prueba con objetivos de Makefile, aprovechando las implementaciones nativas de MLX y los modelos generativos de imágenes de última generación.

Mejor para

Perfecto para Agentes de Aprendizaje Automático que necesitan capacidades avanzadas de prueba y validación de imágenes con pytest y objetivos de Makefile.

↓ Casos de uso accionables for mflux-testing

Ejecutar pruebas rápidas sin generación de imágenes usando `make test-fast`
Depurar pruebas fallidas y analizar discrepancias entre imágenes y oro
Generar imágenes para pruebas usando `make test-slow` y preservar salidas para inspección

! Seguridad y limitaciones

  • Requiere pytest para pruebas que producen imágenes
  • Necesita objetivos de Makefile para pruebas eficientes
  • Preserva las salidas para inspección y no actualiza las imágenes de referencia a menos que se le solicite explícitamente

Why this page is reference-only

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

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Install Path

Start With Installation And Validation

If this skill is worth continuing with, the next step is to confirm the install command, CLI write path, and environment validation.
Trusted Recheck

Cross-Check Against Trusted Picks

If you are still comparing multiple skills or vendors, go back to the trusted collection before amplifying repository noise.
Workflow

Move To Workflow Collections For Team Rollout

When the goal shifts from a single skill to team handoff, approvals, and repeatable execution, move into workflow collections.
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?

Perfecto para Agentes de Aprendizaje Automático que necesitan capacidades avanzadas de prueba y validación de imágenes con pytest y objetivos de Makefile. 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. 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: Ejecutar pruebas rápidas sin generación de imágenes usando `make test-fast`, Depurar pruebas fallidas y analizar discrepancias entre imágenes y oro, Generar imágenes para pruebas usando `make test-slow` y preservar salidas para inspección.

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?

Requiere pytest para pruebas que producen imágenes. Necesita objetivos de Makefile para pruebas eficientes. Preserva las salidas para inspección y no actualiza las imágenes de referencia a menos que se le solicite explícitamente.

↓ 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. 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.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

mflux-testing

Install mflux-testing, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
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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