tooluniverse-image-analysis
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and n
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-image-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 normal
- When to Use This Skill
- Core Principles
- Required Python Packages
- High-Level Workflow Decision Tree
- Quantitative Data Analysis Workflow
- Phase 0: Question Parsing and Data Discovery
- Phase 1: Grouped Statistics
- Phase 2: Statistical Testing
- Phase 3: Regression Modeling
- Raw Image Processing Workflow
- When Processing Raw Images
- Segmentation Method Selection
- Library Selection: scikit-image vs OpenCV
- Common BixBench Patterns
pip install pandas numpy scipy statsmodels patsy scikit-image opencv-python-headless tifffile Count cells in image python scripts/segment_cells.py cells.tif --channel 0 --min-area 50 Batch process folder python scripts/batch_process.py input_folder/ output.csv --analysis cell_count
What does the tooluniverse-image-analysis skill do?
Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and n
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-image-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.