synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill colab-finetuninggit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/colab-finetuning)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/colab-finetuning"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/colab-finetuning/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/synthetic-sciences/openscience/colab-finetuning"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/colab-finetuning.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 | $0.00067 | $0.01542 |
| Opus 5.5 | $0.00027 | $0.00617 |
| Sonnet 5.5 | $0.00013 | $0.00308 |
| Haiku 4.5 | $0.00007 | $0.00154 |
Grade A, and why
colab-finetuning 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 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.
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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Colab Fine-Tuning
Fine-tune LLMs using Google Colab GPUs directly from the openscience CLI. Connect to free or paid Colab runtimes and run Unsloth training workflows remotely.
When to Use Colab Fine-Tuning
Use Colab when:
- You don't have a local GPU but need to fine-tune a model
- You want free GPU access (T4 with 15GB VRAM on Colab Free)
- Training models up to ~14B parameters (4-bit QLoRA)
- Quick experiments and prototyping before scaling to cloud
- Colab Pro/Pro+ for A100 (40-80GB) access
Don't use Colab when:
- You need persistent long-running jobs (>12h) — use Tinker or cloud providers
- Training 70B+ models — use Lambda, RunPod, or multi-GPU cloud
- You need guaranteed uptime — Colab may disconnect idle sessions
- Production training pipelines — use managed services
Colab vs Alternatives:
| Need | Use |
|---|---|
| Free GPU, quick experiments | Google Colab |
| Managed cloud training (any size) | Tinker |
| Persistent multi-GPU training | Lambda / RunPod |
| Local GPU available | Unsloth directly |
| Enterprise with SLA | Colab Enterprise (Vertex AI) |
Quick Start
Step 1: Generate Bridge Notebook
Use colab_notebook tool with workflow="bridge"
This creates a openscience-bridge.ipynb file that establishes a WebSocket tunnel between openscience and the Colab GPU.
Step 2: Open in Colab
- Go to colab.research.google.com
- Upload the bridge notebook (File → Upload notebook)
- Select GPU runtime (Runtime → Change runtime type → T4 GPU)
- Run all cells
- Copy the WebSocket URL that appears
Step 3: Connect from openscience
Use colab_connect tool with connection_url="wss://..."
Step 4: Run Training
Use colab_finetune tool with:
workflow: "sft"
model: "unsloth/Qwen3-4B-unsloth-bnb-4bit"
dataset: "mlabonne/FineTome-100k"
Or execute individual cells:
Use colab_execute tool with code="import torch; print(torch.cuda.get_device_name(0))"
What ships with it
3 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.
- 1mo ago First seen · 155 lines · 67 tokens per session scan A 55a009743d47
colab-finetuning is a skill published in the GitHub repository synthetic-sciences/openscience (4,082 stars, last pushed yesterday), licensed Apache-2.0. It adds 67 tokens to every session and 1,542 once invoked, about $0.0003 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 2026-09-03.
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