Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AndyZhuang/Opentestnpx agentmods add skills/andyzhuang/opentest/tooluniverse-image-analysisWrote 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/andyzhuang/opentest/tooluniverse-image-analysis)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-image-analysis"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-image-analysis/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/andyzhuang/opentest/tooluniverse-image-analysis"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-image-analysis.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 | $0.00156 | $0.03942 |
| Opus 5 | $0.00078 | $0.01971 |
| Sonnet 5 | $0.00031 | $0.00788 |
| Haiku 4.5 | $0.00016 | $0.00394 |
Grade A, and why
tooluniverse-image-analysis 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 8d 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 — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microscopy Image Analysis and Quantitative Imaging Data
Production-ready skill for analyzing microscopy-derived measurement data using pandas, numpy, scipy, statsmodels, and scikit-image. Designed for BixBench imaging questions covering colony morphometry, cell counting, fluorescence quantification, regression modeling, and statistical comparisons.
IMPORTANT: This skill handles complex multi-workflow analysis. Most implementation details have been moved to references/ for progressive disclosure. This document focuses on high-level decision-making and workflow orchestration.
When to Use This Skill
Apply when users:
- Have microscopy measurement data (area, circularity, intensity, cell counts) in CSV/TSV
- Ask about colony morphometry (bacterial swarming, biofilm, growth assays)
- Need statistical comparisons of imaging measurements (t-test, ANOVA, Dunnett's, Mann-Whitney)
- Ask about cell counting statistics (NeuN, DAPI, marker counts)
- Need effect size calculations (Cohen's d) and power analysis
- Want regression models (polynomial, spline) fitted to dose-response or ratio data
- Ask about model comparison (R-squared, F-statistic, AIC/BIC)
- Need Shapiro-Wilk normality testing on imaging data
- Want confidence intervals for peak predictions from fitted models
- Questions mention imaging software output (ImageJ, CellProfiler, QuPath)
- Need fluorescence intensity quantification or colocalization analysis
- Ask about image segmentation results (counts, areas, shapes)
BixBench Coverage: 21 questions across 4 projects (bix-18, bix-19, bix-41, bix-54)
NOT for (use other skills instead):
- Phylogenetic analysis → Use
tooluniverse-phylogenetics - RNA-seq differential expression → Use
tooluniverse-rnaseq-deseq2 - Single-cell scRNA-seq → Use
tooluniverse-single-cell - Statistical regression only (no imaging context) → Use
tooluniverse-statistical-modeling
Core Principles
- Data-first approach - Load and inspect all CSV/TSV measurement data before analysis
- Question-driven - Parse the exact statistic, comparison, or model requested
- Statistical rigor - Proper effect sizes, multiple comparison corrections, model selection
- Imaging-aware - Understand ImageJ/CellProfiler measurement columns (Area, Circularity, Round, Intensity)
- Workflow flexibility - Support both pre-quantified data (CSV) and raw image processing
- Precision - Match expected answer format (integer, range, decimal places)
- Reproducible - Use standard Python/scipy equivalents to R functions
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.
- 8d ago First seen · 440 lines · 156 tokens per session scan A 083c612ea342
tooluniverse-image-analysis is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 156 tokens to every session and 3,942 once invoked, about $0.0008 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 2026-09-03.
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