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 svngoku/coding-agents-skills --skill unsloth-hf-jobsgit clone --depth 1 https://github.com/svngoku/coding-agents-skillsWrote 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/svngoku/coding-agents-skills/unsloth-hf-jobs)<a href="https://agentmods.dev/skills/svngoku/coding-agents-skills/unsloth-hf-jobs"><img src="https://agentmods.dev/badge/skills/svngoku/coding-agents-skills/unsloth-hf-jobs/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/svngoku/coding-agents-skills/unsloth-hf-jobs"><img src="https://agentmods.dev/badge/skills/svngoku/coding-agents-skills/unsloth-hf-jobs.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 | $0.00111 | $0.01487 |
| Opus 5 | $0.00056 | $0.00744 |
| Sonnet 5 | $0.00022 | $0.00297 |
| Haiku 4.5 | $0.00011 | $0.00149 |
Grade C, and why
unsloth-hf-jobs scanned grade C with 2 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 12d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
- HF CLI: `curl -LsSf https://hf.co/cli/install.sh | bash` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- HF CLI: `curl -LsSf https://hf.co/cli/install.sh | bash` How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unsloth Training on HF Jobs
Fine-tune LLMs and VLMs using Unsloth on HF Jobs with UV scripts that handle dependencies automatically.
Prerequisites
- Hugging Face account with token
- HF CLI:
curl -LsSf https://hf.co/cli/install.sh | bash - Dataset on the Hub (see data formats below)
Available Scripts
| Script | Base Model | Task |
|---|---|---|
scripts/sft-qwen3-vl.py |
Qwen3-VL-8B | VLM fine-tuning |
scripts/sft-gemma3-vlm.py |
Gemma 3 4B | VLM fine-tuning (smaller) |
scripts/continued-pretraining.py |
Qwen3-0.6B | Domain adaptation |
Remote URL (for HF Jobs): https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/
Data Formats
VLM Fine-tuning
Requires images and messages columns:
{
"images": [<PIL.Image>],
"messages": [
{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What's in this image?"}]},
{"role": "assistant", "content": [{"type": "text", "text": "A golden retriever playing fetch."}]}
]
}
Example dataset: davanstrien/iconclass-vlm-sft
Continued Pretraining
Any dataset with a text column (use --text-column if named differently):
{"text": "Your domain-specific text here..."}
Usage Patterns
VLM Fine-tuning
hf jobs uv run \
https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/sft-qwen3-vl.py \
--flavor a100-large --secrets HF_TOKEN --timeout 4h \
-- --dataset <username>/<dataset> \
--num-epochs 1 \
--eval-split 0.2 \
--output-repo <username>/<model-name>
Continued Pretraining
hf jobs uv run \
https://huggingface.co/datasets/uv-scripts/unsloth-jobs/raw/main/continued-pretraining.py \
--flavor a100-large --secrets HF_TOKEN \
-- --dataset <username>/<dataset> \
--text-column content \
--max-steps 1000 \
--output-repo <username>/<model-name>
What ships with it
8 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.
- eval.yaml 986 B
- graders/check.py 3.9 KB runs code
- instructions/qwen3-vl-finetune-hf-jobs.md 1.5 KB
- rubrics/quality.md 1.5 KB
- scripts/continued-pretraining.py 12 KB runs code
- scripts/sft-gemma3-vlm.py 11 KB runs code
- scripts/sft-qwen3-vl.py 20 KB runs code
- solutions/reference-qwen3-vl-finetune-hf-jobs 1.6 KB
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.
- 12d ago First seen · 134 lines · 111 tokens per session scan C 0775132b8dd7
unsloth-hf-jobs is a skill published in the GitHub repository svngoku/coding-agents-skills (11 stars, last pushed 29d ago), licensed MIT. It adds 111 tokens to every session and 1,487 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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