Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill pathmlgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-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/foryourhealth111-pixel/vibe-skills/pathml)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/pathml"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/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/foryourhealth111-pixel/vibe-skills/pathml"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/pathml.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 | $0.00102 | $0.01607 |
| Opus 5 | $0.00051 | $0.00804 |
| Sonnet 5 | $0.00020 | $0.00321 |
| Haiku 4.5 | $0.00010 | $0.00161 |
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 9d 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
86% identical to pathml — 11 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 — 165 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.
Routing Boundary
Use this skill for full computational pathology workflows, PathML pipelines, WSI analysis, nucleus segmentation, tissue or cell graphs, spatial pathology, multiplex pathology, and multiparametric imaging. Basic WSI tile extraction should stay with histolab, DICOM tag/anonymization work with pydicom, IDC/TCIA/DICOMWeb retrieval with imaging-data-commons, and OMERO server or ROI management with omero-integration.
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.
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.
- 9d ago First seen · 165 lines · 102 tokens per session scan A ed445db5592f
pathml is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 102 tokens to every session and 1,607 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to pathml, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
jupyter-live-kernel
Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a…
sparse-autoencoder-training
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
pytorch-fsdp
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2.
torch-geometric
PyTorch Geometric (PyG) for graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torchgeometric, not for general NetworkX analytics or non-graph PyTorch models.
bids
Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars…
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…