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tooluniverse-image-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
tooluniverse-image-analysis：Production-ready microscopy image analysis 、 quantitative imaging data skill ，用于 colony morphometry，cell counting，fluorescence quantification，、 统计分析 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 回归，…。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-image-analysis
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Production-ready skill ，用于 analyzing microscopy-derived measurement data ，使用 pandas，numpy，scipy，statsmodels，、 scikit-image. Designed ，用于 BixBench imaging questions covering colony morphometry，cell counting，fluorescence quantification，回归 modeling，、 statistical comparisons。
- IMPORTANT：This skill handles complex multi-workflow analysis. Most implementation details have been moved to references/ ，用于 progressive disclosure. This document focuses on high-level decision-making 、 工作流编排。
- import pandas as pd import numpy as np ，面向 scipy import stats ，面向 scipy.interpolate import BSpline，make_interp_spline import statsmodels.api as sm ，面向 statsmodels.formula.api import ols ，面向 statsmodels.stats.power import TTestIndPower ，面向 patsy import dmatrix，bs，cr。
原始文档
SKILL.md 摘录
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
Optional (for raw image processing)
import skimage import cv2 import tifffile bash pip install pandas numpy scipy statsmodels patsy scikit-image opencv-python-headless tifffile
---
适用场景
- 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)。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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