MLX-VLM is a Python package for running inference and fine-tuning vision-language models, which process images and text together, on Mac computers using MLX. It is used by developers working with multimodal models and includes command-line, web, server, scripting, and training workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Blaizzy/mlx-vlm --skill reproducible-github-issuesgit clone --depth 1 https://github.com/Blaizzy/mlx-vlmWrote 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/blaizzy/mlx-vlm/reproducible-github-issues)<a href="https://agentmods.dev/skills/blaizzy/mlx-vlm/reproducible-github-issues"><img src="https://agentmods.dev/badge/skills/blaizzy/mlx-vlm/reproducible-github-issues/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/blaizzy/mlx-vlm/reproducible-github-issues"><img src="https://agentmods.dev/badge/skills/blaizzy/mlx-vlm/reproducible-github-issues.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 115 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00067 | $0.00780 |
| Opus 5.5 | $0.00027 | $0.00312 |
| Sonnet 5.5 | $0.00013 | $0.00156 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
reproducible-github-issues scanned grade A 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
3. Prefer `curl` over client SDKs for server repros. How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reproducible GitHub Issues
Use this workflow to turn a failure into a concise, actionable MLX-VLM issue. Do not open a GitHub issue unless the user explicitly asks; otherwise produce issue-ready Markdown.
Required Information
Collect or infer:
- MLX-VLM version or git commit.
- Install method: PyPI, editable checkout, branch, or wheel.
- Python version, OS version, machine/chip, and whether MLX Metal or MLX CUDA is in use.
- Exact model ID or local path.
- Whether the model is from Hugging Face cache, a local conversion, or a custom checkpoint.
- Exact
uv runCLI command or server startup command. - Exact request body for server issues.
- Input media facts: image dimensions, audio duration/sample rate, video duration/frame count, and whether the input can be shared.
- Expected behavior, actual behavior, and full error/traceback.
Collect the Environment Automatically
Instead of hand-filling versions, run and paste the output into the Environment section:
uv run python - <<'PY'
import platform, mlx.core as mx
try:
import mlx_vlm
v = getattr(mlx_vlm, "__version__", "unknown")
except Exception as e:
v = f"import failed: {e}"
print("mlx-vlm:", v)
print("mlx:", mx.__version__, "| default device:", mx.default_device())
print("python:", platform.python_version(), "| platform:", platform.platform())
PY
Repro Minimization
- Reduce to the smallest command or request that still fails.
- Remove private paths, tokens, and unrelated environment variables.
- Prefer
curlover client SDKs for server repros. - Prefer one image/audio/video file before multi-input repros.
- Use small public media or synthetic inputs when possible.
- State whether the bug reproduces with a public model or only a private/local checkpoint.
Issue Template
### Summary
<One sentence describing the failure.>
### Environment
- MLX-VLM:
- Python:
- OS:
- Hardware:
- Install method:
### Model
- Model:
- Source: <HF cache | local path | converted checkpoint>
- Trust remote code: <yes/no>
### Reproduction
```bash
uv run mlx_vlm.generate <args>
```
For server issues:
```bash
uv run mlx_vlm.server <args>
```
```bash
<curl request>
```
### Expected Behavior
<What should have happened.>
### Actual Behavior
<What happened instead.>
### Logs / Traceback
```text
<trimmed traceback or relevant logs>
```
### Inputs
<Describe attached or shareable inputs. Include dimensions/duration when relevant.>
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.
- 1mo ago First seen · 116 lines · 67 tokens per session scan A 14fe74ac754a
reproducible-github-issues is a skill published in the GitHub repository Blaizzy/mlx-vlm (5,642 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 780 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
localvoxtral-doctor
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mlx-model-porting
Guides and validates architecture-aware ports of PyTorch/Hugging Face models to Apple MLX, inspects existing local MLX projects, and plans evidence-gated optimizations for Apple Silicon. Use when the user asks to run, port, convert, inspect, quantize, benchmark, or fix a model (LLM, VLM, audio/TTS/ASR, diffusion, SSM…
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
python-debugpy
Debug Python: pdb REPL + debugpy remote (DAP).
sglang-diffusion-benchmark-profile
Use when benchmarking denoise latency or profiling a diffusion bottleneck in SGLang.
resolve-handoff
Emit the complete Resolve handoff with exact modes, outcomes, test evidence, and publication state.