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 agentmods add skills/synthetic-sciences/openscience/histolabnpx skills add synthetic-sciences/openscience --skill histolabgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWhat 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 | $0.00062 | $0.04498 |
| Opus 5 | $0.00031 | $0.02249 |
| Sonnet 5 | $0.00012 | $0.00900 |
| Haiku 4.5 | $0.00006 | $0.00450 |
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 3d 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
How it starts
The opening of the file, as written. The whole thing — 678 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.
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
Example workflow:
from histolab.slide import Slide
from histolab.data import prostate_tissue
# Load sample data
prostate_svs, prostate_path = prostate_tissue()
# Initialize slide
slide = Slide(prostate_path, processed_path="output/")
# Inspect properties
print(f"Dimensions: {slide.dimensions}")
print(f"Levels: {slide.levels}")
print(f"Magnification: {slide.properties.get('openslide.objective-power')}")
# Save thumbnail
slide.save_thumbnail()
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.
- 3d ago First seen · 678 lines · 62 tokens per session scan A 4721a7cf35db
histolab is a skill published in the GitHub repository synthetic-sciences/openscience (3,385 stars, last pushed yesterday), licensed Apache-2.0. It adds 62 tokens to every session and 4,498 once invoked, about $0.0003 per session on Opus 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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