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/.agents/skills/remediating-dependabot/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/remediating-dependabot)<a href="https://agentmods.dev/skills/mflux-community/mflux/remediating-dependabot"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/remediating-dependabot/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/remediating-dependabot"><img src="https://agentmods.dev/badge/skills/mflux-community/mflux/remediating-dependabot.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 | $0.00060 | $0.01188 |
| Opus 5 | $0.00030 | $0.00594 |
| Sonnet 5 | $0.00012 | $0.00238 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade B, and why
remediating-dependabot scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletemediumDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
trap 'rm -rf "$tmp_dir"' EXIT Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remediating Dependabot
Resolve actionable Dependabot alerts with the smallest compatible dependency update and prove the local lock is outside every reported vulnerable range.
Scope
- Use
gh apias the source of truth for open alerts inmflux-community/mflux. - Keep dependency changes in
pyproject.tomlanduv.lockunless compatibility requires source changes. - Preserve supported Python and platform markers.
- Avoid unrelated package upgrades when regenerating the lock.
- Treat dependency optionality or loading refactors as separate work unless explicitly requested.
Workflow
-
Confirm the current branch and working tree before editing.
-
Fetch all open Dependabot alerts, following pagination:
gh api --method GET --paginate \ -H "Accept: application/vnd.github+json" \ repos/mflux-community/mflux/dependabot/alerts \ -f state=open -
Group alerts by manifest, package, severity, vulnerable range, and first patched version. Distinguish direct requirements from transitive lock entries.
-
Inspect
pyproject.toml,uv.lock, supported Python versions, and platform markers before choosing a fix. -
Prefer, in order:
- removing dependencies that are no longer needed;
- raising a direct lower bound to the first secure compatible release;
- upgrading only affected transitive packages in the lock;
- adding or changing environment markers only when compatibility actually differs by Python or platform.
-
Use one requirement when a release supports the full project matrix. Do not introduce overlapping marker-specific requirements without evidence that they are necessary.
-
Regenerate the lock with targeted upgrades:
uv lock --upgrade-package <package> [--upgrade-package <package> ...] -
Review the lock diff. Explain large platform-specific resolver changes, especially PyTorch CUDA package transitions, rather than assuming they are accidental.
Security verification
Do not infer alert closure solely from package names or Dependabot's hosted UI.
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 · 125 lines · 60 tokens per session scan B e8d12cbe77ba
remediating-dependabot is a skill published in the GitHub repository mflux-community/mflux (2,322 stars, last pushed 7d ago), licensed MIT. It adds 60 tokens to every session and 1,188 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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