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/asd-ste100/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/asd-ste100)<a href="https://agentmods.dev/skills/mflux-community/mflux/asd-ste100"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/asd-ste100/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/asd-ste100"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/asd-ste100.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00099 | $0.03670 |
| Opus 5.5 | $0.00040 | $0.01468 |
| Sonnet 5.5 | $0.00020 | $0.00734 |
| Haiku 4.5 | $0.00010 | $0.00367 |
Grade A, and why
asd-ste100 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 20d 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.
This is a copy
98% identical to asd-ste100 — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simplified Technical English (ASD-STE100)
ASD-STE100 is a controlled-language standard built by the aerospace and defense industry (ASD, the AeroSpace and Defense Industries Association of Europe) to stop maintenance technicians from misreading English instructions. The standard removes the two biggest sources of misreading: words with more than one meaning, and sentences with more than one possible structure.
This skill borrows that same discipline for a different reader: an AI agent or a downstream system that has to parse an English string — an error message, a tool description, an inter-agent instruction, a status report — without a human in the loop to resolve ambiguity. If a maintenance technician can misread "close the valve" as an adjective ("the valve that is near") instead of a command, so can a language model.
When to Use This Skill
- An agent's output (explanation, instruction, log message, tool description) reads as dense, jargon-heavy, or ambiguous.
- Text will be consumed by another agent, a translation pipeline, or a non-native English reader, and misparsing has a real cost.
- You are writing a prompt, system message, or tool description and want to remove ambiguity before a model ever sees it.
- You want a before/after comparison showing exactly which rule was violated and how the rewrite fixes it. Ask for it — the default output is the rewritten text alone (see Output Format).
This skill is not for creative or marketing copy — STE is deliberately flat and literal. Do not apply it to text where voice, nuance, or persuasion is the point.
Two Modes
Pick a mode before rewriting. If the user does not say which, infer from the text type. Keep the inferred mode internal. Do not announce it in the default output.
Strict — procedures, error messages, tool and function descriptions, inter-agent instructions, safety text. Anywhere a wrong reading has a cost. Apply every rule below, including the hard length caps and one-word-one-meaning discipline.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 20d ago First seen · 140 lines · 99 tokens per session scan A f5d95e49adb5
asd-ste100 is a skill published in the GitHub repository mflux-community/mflux (2,547 stars, last pushed today), licensed MIT. It adds 99 tokens to every session and 3,670 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to asd-ste100, differing in 6 lines, and is treated as a copy.
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