MFLUX is a native MLX implementation of generative image models that runs locally on Mac computers. It is for generating images with supported models through command-line tools or a Python API. The catalogue skills and instruction support workflows built around these image-generation models.
Borrowing it
Nothing to install: this file belongs to mflux-community/mflux. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mflux-community/mflux/main/.cursor/skills/mflux-manual-testing/SKILL.mdgit clone --depth 1 https://github.com/mflux-community/mfluxWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mflux-community/mflux/mflux-manual-testing)<a href="https://agentmods.dev/skills/mflux-community/mflux/mflux-manual-testing"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/mflux-manual-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mflux-community/mflux/mflux-manual-testing"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/mflux-manual-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.00993 |
| Opus 5 | $0.00013 | $0.00496 |
| Sonnet 5 | $0.00005 | $0.00199 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
mflux-manual-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mflux manual testing
Some regressions (especially in CLIs and image IO) are easiest to catch by running the commands and visually inspecting outputs. This skill provides a lightweight, change-driven manual test checklist.
When to Use
- You changed any CLI entrypoint(s) under
src/mflux/models/**/cli/. - You touched callbacks (e.g. stepwise output, memory saver) or metadata/image saving.
- Tests are green but you want confidence in real command usage.
Strategy (change-driven)
- Identify what changed on your branch (new flags, default behavior changes, new callbacks, new models).
- Only run manual checks for the touched areas; don’t try to exercise every CLI.
- Prefer 1–2 seeds and a small step count (e.g. 4) for fast iteration, unless the change affects convergence/quality.
- Before manual CLI testing, reinstall the local tool executables so you’re testing the latest code:
uv tool install --force --editable --reinstall .
Core CLI checks (pick what’s relevant)
- Basic generation: run the CLI once with a representative prompt and confirm the output is not “all noise”.
- Model saving (if relevant): if you touched weight loading/saving or model definitions, run
mflux-savefor the affected model(s) and verify:- the output directory is created
- the command completes without missing-file errors
- Run from disk (if relevant): if you touched save/load paths or model resolution, generate from a locally saved model directory by passing
--model /full/path/to/saved-modeland confirm it runs and produces a sane image. - Stepwise outputs (if relevant): run with
--stepwise-image-output-dirand confirm:- step images are written for each step
- the final step image matches the final output image qualitatively
- the composite image is created
- Low-RAM path (if relevant): run with
--low-ramand confirm:- generation completes
- output quality is sane (no unexpected all-noise output)
- Metadata (if relevant): run with
--metadataand confirm the.metadata.jsonsidecar is emitted and looks consistent.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 81 lines · 25 tokens per session scan A 8e582d96207a
mflux-manual-testing is a skill published in the GitHub repository mflux-community/mflux (2,322 stars, last pushed 8d ago), licensed MIT. It adds 25 tokens to every session and 993 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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