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 deepspot-mgit 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/deepspot-m)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/deepspot-m"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/deepspot-m/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/deepspot-m"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/deepspot-m.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 | $0.00080 | $0.01935 |
| Opus 5.5 | $0.00032 | $0.00774 |
| Sonnet 5.5 | $0.00016 | $0.00387 |
| Haiku 4.5 | $0.00008 | $0.00194 |
Grade A, and why
deepspot-m 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.
This is a copy
97% identical to deepspot-m — 101 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepSpot-M
Overview
DeepSpot-M is a multimodal foundation model that maps a 224x224 H&E histology tile to spatial gene expression in log1p-CPM. The output is virtual spatial transcriptomics: one value per queried gene per tile, laid out on the grid the tiles came from.
A LoRA-adapted pathology foundation backbone (Midnight) tokenises the tile. A
cross-attention gene decoder lets each gene query attend to the patch tokens, and a gene
router hypernetwork builds gene-specific projections from frozen biological embeddings
(Evo 2, Orthrus, ProtT5, scGPT, Apertus). Genes enter the model as queryable embeddings
rather than fixed output slots, so the released model covers a ~19k protein-coding gene
panel including genes unseen in training. The panel ships with the weights as
tokens.csv and is exposed as model.gene_names; genes outside it cannot be queried in
this release.
Applied to TCGA, the model produced a virtual spatial transcriptomics atlas of 28,664 slides across 32 cancer types.
Licensing
The code is PolyForm Noncommercial 1.0.0 and the weights are CC-BY-NC-SA-4.0. Use it for noncommercial research and check both licences before redistributing outputs.
Installation
uv pip install deepspotm==1.0.0
Version 1.0.0 targets Python 3.10 to 3.13 and pulls in PyTorch. Install the PyTorch build that matches your CUDA version first if you want GPU inference.
Model access
The weights are gated:
- Open https://huggingface.co/ratschlab/DeepSpotM and request access.
- Once access is granted, authenticate the machine that will download them:
huggingface-cli login
from_pretrained reads that cached token, so a login is needed once per machine.
Quick start
from deepspotm import DeepSpotM
model, image_processor = DeepSpotM.from_pretrained("ratschlab/DeepSpotM", source="scgpt")
vals = model.predict_genes(image_processor(pil_tile).unsqueeze(0), ["EPCAM", "CD3D"])
pil_tile is a PIL image of exactly 224x224 pixels. image_processor turns it into a
tensor, unsqueeze(0) adds the batch dimension, and predict_genes takes the batch plus a
list of HGNC gene symbols. Values come back in log1p-CPM, aligned with the gene list you
passed, so keep that list beside the output to keep the columns labelled. Symbols must be
in the released ~19k-gene panel (model.gene_names); an unknown symbol raises KeyError
naming the offending genes.
What ships with it
2 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 Changed · +5 lines 8bbd5f20538b
- 1mo ago First seen · 178 lines · 80 tokens per session scan A 4160e032bf06
deepspot-m is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 80 tokens to every session and 1,935 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to deepspot-m, differing in 101 lines, and is treated as a copy.
Other skills, from other repositories
songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
arxiv
Search arXiv papers by keyword, author, category, or ID.
research-paper-writing
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.