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 pathmlgit 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/pathml)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/pathml"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/pathml/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/pathml"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/pathml.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 | $0.00069 | $0.01573 |
| Opus 5.5 | $0.00028 | $0.00629 |
| Sonnet 5.5 | $0.00014 | $0.00315 |
| Haiku 4.5 | $0.00007 | $0.00157 |
Grade A, and why
pathml 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- pathml — 95% identical, 9 lines differ
- pathml — 95% identical, 7 lines differ
- pathml — 95% identical, 9 lines differ
- pathml — 95% identical, 7 lines differ
- pathml — 94% identical, 10 lines differ
- pathml — 94% identical, 10 lines differ
- pathml — 94% identical, 10 lines differ
- pathml — 91% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PathML
Overview
PathML is a comprehensive Python toolkit for computational pathology workflows, designed to facilitate machine learning and image analysis for whole-slide pathology images. The framework provides modular, composable tools for loading diverse slide formats, preprocessing images, constructing spatial graphs, training deep learning models, and analyzing multiparametric imaging data from technologies like CODEX and multiplex immunofluorescence.
When to Use This Skill
Apply this skill for:
- Loading and processing whole-slide images (WSI) in various proprietary formats
- Preprocessing H&E stained tissue images with stain normalization
- Nucleus detection, segmentation, and classification workflows
- Building cell and tissue graphs for spatial analysis
- Training or deploying machine learning models (HoVer-Net, HACTNet) on pathology data
- Analyzing multiparametric imaging (CODEX, Vectra, MERFISH) for spatial proteomics
- Quantifying marker expression from multiplex immunofluorescence
- Managing large-scale pathology datasets with HDF5 storage
- Tile-based analysis and stitching operations
Core Capabilities
PathML provides six major capability areas documented in detail within reference files:
1. Image Loading & Formats
Load whole-slide images from 160+ proprietary formats including Aperio SVS, Hamamatsu NDPI, Leica SCN, Zeiss ZVI, DICOM, and OME-TIFF. PathML automatically handles vendor-specific formats and provides unified interfaces for accessing image pyramids, metadata, and regions of interest.
See: references/image_loading.md for supported formats, loading strategies, and working with different slide types.
2. Preprocessing Pipelines
Build modular preprocessing pipelines by composing transforms for image manipulation, quality control, stain normalization, tissue detection, and mask operations. PathML's Pipeline architecture enables reproducible, scalable preprocessing across large datasets.
Key transforms:
StainNormalizationHE- Macenko/Vahadane stain normalizationTissueDetectionHE,NucleusDetectionHE- Tissue/nucleus segmentationMedianBlur,GaussianBlur- Noise reductionLabelArtifactTileHE- Quality control for artifacts
What ships with it
6 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 · +6 lines b2a3f3c82366
- 1mo ago First seen · 166 lines · 69 tokens per session scan A 5febafb20a15
pathml is a skill published in the GitHub repository synthetic-sciences/openscience (4,121 stars, last pushed 2d ago), licensed Apache-2.0. It adds 69 tokens to every session and 1,573 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 2026-08-30.
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