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-cli/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-cli)<a href="https://agentmods.dev/skills/mflux-community/mflux/mflux-cli"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/mflux-cli/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-cli"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/mflux-cli.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 | $0.00021 | $0.00795 |
| Opus 5.5 | $0.00008 | $0.00318 |
| Sonnet 5.5 | $0.00004 | $0.00159 |
| Haiku 4.5 | $0.00002 | $0.00080 |
Grade A, and why
mflux-cli 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 9d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mflux CLI navigation
Use this skill to inventory CLI capabilities, summarize what the CLI supports, and guide where to look for commands without relying on brittle file paths. Because README examples can drift, prefer verifying support against the current CLI entrypoints.
When to use
- You need to list supported CLI features or commands.
- You need to find where a capability is implemented in the CLI.
- You are documenting or refactoring CLI features and want a stable map.
- A user asks for CLI help, e.g., “Can you help me generate an image using z-image?”, “Which model is best?”, etc.
How to find commands (structure, not exact paths)
- Common/shared CLI arguments live in the central CLI parser module.
- Model-specific CLI entrypoints live under each model's CLI package.
- Repo-level CLI helpers (completions, defaults) live under the shared CLI package.
- Utilities may add standalone CLIs (e.g., metadata info, LoRA library).
Best practices when constructing CLI calls
- Inference steps: When constructing a CLI call for any model, always check
MODEL_INFERENCE_STEPSand use the model's default/recommended step count unless the user explicitly asks for a different value. This is especially important for distilled/non-base variants (for examplefibo-lite,z-image-turbo, and other*-turbo/*-litemodels), where using full/base-model step counts is usually counterproductive. - Resource/inspection flags: Mention
--low-ramto reduce memory usage and--stepwise-image-output-dirfor stepwise outputs when useful. - Python API requests: If a user asks for the Python API, treat the equivalent CLI script as the best starting reference for the underlying parameters and defaults.
- CLI implementation changes: When adding or changing CLI behavior, prefer extending shared parser methods and shared helpers before adding manual one-off arguments or save paths in a model CLI.
- Docs/examples drift: If CLI defaults, supported flags, or recommended usage changed, update the matching README examples in the same pass.
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.
- 9d ago Changed 23d7f72f6e08
- 12d ago Changed 2a60705dfda1
- 2mo ago First seen · 86 lines · 21 tokens per session scan A 1c7044894e1b
mflux-cli is a skill published in the GitHub repository mflux-community/mflux (2,547 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 795 once invoked, about $0.0001 per session on Opus 5.5. 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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