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 beita6969/ScienceClaw --skill computational-pathology-agentgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/computational-pathology-agent)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/computational-pathology-agent"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/computational-pathology-agent/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/beita6969/scienceclaw/computational-pathology-agent"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/computational-pathology-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00009 | $0.00595 |
| Opus 5 | $0.00005 | $0.00298 |
| Sonnet 5 | $0.00002 | $0.00119 |
| Haiku 4.5 | $0.00001 | $0.00060 |
Grade A, and why
computational-pathology-agent 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: computational-pathology-agent description: Analyze Whole Slide Images (WSI) for digital pathology, including tissue segmentation and feature extraction. keywords:
- wsi
- digital-pathology
- deep-learning
- resnet
- openslide measurable_outcome: Preprocess and extract tissue patches from a 1GB+ .svs slide within 15 minutes for downstream ML tasks. license: MIT metadata: author: MD BABU MIA, PhD version: "1.0.0" compatibility:
- system: python 3.9+ allowed-tools:
- run_shell_command
- read_file
- write_file
Computational Pathology Agent
Version: 1.0.0 Author: MD BABU MIA, PhD Date: February 2026
Overview
This agent specializes in the analysis of Whole Slide Images (WSIs) for digital pathology. It leverages Deep Learning models (ResNet, ViT, HoverNet) to perform segmentation, classification, and feature extraction from gigapixel histology images.
Capabilities
- WSI Handling: Efficient reading/tiling of .svs, .ndpi, .tiff files (using OpenSlide/TiffSlide).
- Tissue Segmentation: Separation of tissue from background.
- Patch Extraction: Automated generation of patches for ML training/inference.
- Nuclei Segmentation: Integration with StarDist/HoverNet for cellular analysis.
- Feature Extraction: Generating feature vectors for slide-level clustering.
Usage
from Skills.Pathology_AI.Computational_Pathology_Agent.wsi_analyzer import WSIAnalyzer
# Initialize
path_agent = WSIAnalyzer(slide_path="./data/biopsy_001.svs")
# Extract tissue patches
path_agent.extract_patches(patch_size=256, level=1)
# Analyze Nuclei (requires model weights)
# path_agent.segment_nuclei()
What ships with it
1 file 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.
- 10d ago First seen · 77 lines · 9 tokens per session scan A 51538e945020
computational-pathology-agent is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 9 tokens to every session and 595 once invoked, about $0.0000 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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