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 agentlas-ai/agentlas-desktop --skill histolabgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/histolab)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/histolab"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/histolab"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.00065 | $0.02234 |
| Opus 5.5 | $0.00026 | $0.00894 |
| Sonnet 5.5 | $0.00013 | $0.00447 |
| Haiku 4.5 | $0.00006 | $0.00223 |
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
92% identical to histolab — 307 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 — 261 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
Install OpenSlide system libraries first (OpenSlide download), then install histolab:
uv pip install histolab
For built-in TCGA sample slides via histolab.data, also install pooch:
uv pip install pooch
Histolab 0.7.0 (latest stable) supports Python 3.8–3.11 on Linux and macOS. Windows is not supported as of 0.7.0.
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
Six capability areas, each with worked code, are documented in references/core_capabilities.md:
- Slide management — opening slides, properties, levels, thumbnails, and scaled images.
- Tissue detection and masks —
TissueMaskandBiggestTissueBoxMask, and custom masks. - Tile extraction — random, grid, and score-based tilers with size, level, and tissue-fraction control.
- Filters and preprocessing — image and morphological filters, and composing them.
- Stain normalization — Reinhard and Macenko normalization against a target image.
- Visualization — locating tiles on the slide and inspecting masks and extractions.
Five end-to-end workflows are in references/typical_workflows.md. Per-topic detail lives in references/slide_management.md, references/tissue_masks.md, references/tile_extraction.md, references/filters_preprocessing.md, and references/visualization.md.
What ships with it
7 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 · 261 lines · 65 tokens per session scan A af4b9a0ab9de
histolab is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 65 tokens to every session and 2,234 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to histolab, differing in 307 lines, and is treated as a copy.
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