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 histolabgit 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/histolab)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/histolab"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/histolab/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/histolab"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/histolab.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 | $0.00062 | $0.04564 |
| Opus 5.5 | $0.00025 | $0.01826 |
| Sonnet 5.5 | $0.00012 | $0.00913 |
| Haiku 4.5 | $0.00006 | $0.00456 |
Grade A, and why
histolab 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
84% identical to histolab — 748 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 — 680 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Histolab
Overview
Histolab is a Python library for processing whole slide images (WSI) in digital pathology. It automates tissue detection, extracts informative tiles from gigapixel images, and prepares datasets for deep learning pipelines. The library handles multiple WSI formats, implements sophisticated tissue segmentation, and provides flexible tile extraction strategies.
Routing Boundary
Use this skill for basic WSI tile extraction, tissue detection, H&E tile preprocessing, and quick histolab dataset preparation. Full computational pathology workflows, PathML pipelines, nucleus segmentation, spatial pathology, multiplex pathology, DICOM/IDC retrieval, OMERO server work, and generic image-processing tasks are outside this skill.
Installation
uv pip install histolab
Quick Start
Basic workflow for extracting tiles from a whole slide image:
from histolab.slide import Slide
from histolab.tiler import RandomTiler
# Load slide
slide = Slide("slide.svs", processed_path="output/")
# Configure tiler
tiler = RandomTiler(
tile_size=(512, 512),
n_tiles=100,
level=0,
seed=42
)
# Preview tile locations
tiler.locate_tiles(slide, n_tiles=20)
# Extract tiles
tiler.extract(slide)
Core Capabilities
1. Slide Management
Load, inspect, and work with whole slide images in various formats.
Common operations:
- Loading WSI files (SVS, TIFF, NDPI, etc.)
- Accessing slide metadata (dimensions, magnification, properties)
- Generating thumbnails for visualization
- Working with pyramidal image structures
- Extracting regions at specific coordinates
Key classes: Slide
Reference: references/slide_management.md contains comprehensive documentation on:
- Slide initialization and configuration
- Built-in sample datasets (prostate, ovarian, breast, heart, kidney tissues)
- Accessing slide properties and metadata
- Thumbnail generation and visualization
- Working with pyramid levels
- Multi-slide processing workflows
What ships with it
5 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 · 680 lines · 62 tokens per session scan A a6acdb034be5
histolab is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,772 stars, last pushed yesterday), licensed Apache-2.0. It adds 62 tokens to every session and 4,564 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to histolab, differing in 748 lines, and is treated as a copy.
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