fastmcp-development
Use when creating or modifying Model Context Protocol (MCP) servers with FastMCP framework - guides through tools, resources, prompts, authentication, Claude Desktop integration, and production deployment with Python and TypeScript examples
pyimagej-fiji-bridge
Python bridge to ImageJ2/Fiji enabling macro execution, plugin calls (Bio-Formats, TrackMate, Analyze Particles), bidirectional NumPy↔ImagePlus/ImgLib2 data exchange, and ImageJ Ops from Python. Use for automating Fiji-specific workflows headlessly from Python scripts. Use scikit-image instead for pure Python pipelines that do not require Fiji plugins; use napari for interactive visualization.
pydicom-medical-imaging
Pure Python DICOM library for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM files, extract pixel data as NumPy arrays, access/modify metadata tags, apply windowing (VOI LUT), anonymize PHI, build DICOM from scratch, process series into 3D volumes. For whole-slide pathology images use histolab; for NIfTI neuroimaging use nibabel.
opencv-bioimage-analysis
Computer vision library for bio-image preprocessing, feature detection, and real-time microscopy analysis. Performs color space conversion, morphological operations, contour/blob detection, template matching, and optical flow on fluorescence and brightfield images. 10-100× faster than pure Python implementations using optimized C++ kernels. Use scikit-image for scientific morphometry and regionprops; use OpenCV for real-time processing, video, and classical feature extraction pipelines.
matplotlib-scientific-plotting
Low-level Python plotting library for full customization of scientific figures. Use for publication-quality plots (line, scatter, bar, heatmap, contour, 3D), multi-panel subplot layouts, and fine-grained control over every visual element. Export to PNG/PDF/SVG. For quick statistical plots use seaborn; for interactive plots use plotly.
matlab-scientific-computing
MATLAB/GNU Octave numerical computing for matrix operations, linear algebra, differential equations, signal processing, optimization, statistics, and scientific visualization. Code examples in MATLAB syntax (runs on both MATLAB and Octave). For Python-based scientific computing use numpy/scipy; for statistical modeling use statsmodels.
sympy-symbolic-math
Symbolic mathematics in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, differential equations, code generation (lambdify, C/Fortran). Use when exact symbolic results are needed, not numerical approximations. For numerical computing use numpy/scipy; for statistical modeling use statsmodels.
opentrons-protocol-api
Python API v2 for programming Opentrons OT-2 and Flex liquid handling robots. Write protocols as Python files with metadata and a run() function; control pipettes, labware, and hardware modules (thermocycler, heater-shaker, magnetic, temperature). Simulate locally with opentrons_simulate, then upload to the robot app. Use PyLabRobot instead for hardware-agnostic scripts that run on Hamilton, Tecan, or other vendors.
opentrons-integration
Opentrons Protocol API v2 for OT-2 and Flex liquid handling robots. Write Python protocols for automated pipetting, serial dilutions, PCR setup, plate replication. Control hardware modules (thermocycler, heater-shaker, magnetic, temperature). For multi-vendor lab automation use pylabrobot.
pymoo
pymoo is a Python framework for single- and multi-objective optimization using evolutionary algorithms. Define problems as vectorized objective functions and constraints, then solve with NSGA-II, NSGA-III, MOEA/D, genetic algorithms, or differential evolution. Analyze Pareto fronts, visualize trade-off surfaces, and customize operators and callbacks. Ideal for engineering design, hyperparameter search, process optimization, and any problem with multiple conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient-based), platypus (fewer algorithms), jMetalPy (Java-based, more algorithms).
pylabrobot
PyLabRobot is a hardware-agnostic Python library for liquid handling robots. Use it to write portable automation scripts that run on Hamilton STAR, Tecan Freedom EVO, Opentrons OT-2, or a simulation backend — without vendor lock-in. Ideal for protocol automation, method development, plate reformatting, serial dilutions, and integrating liquid handlers into larger Python-based lab workflows.
snakemake-workflow-engine
Python-based workflow management system for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake automatically determines the execution order and parallelism. Supports local, SLURM, LSF, AWS, and Google Cloud execution via profiles; per-rule conda/Singularity environments. Use for bioinformatics NGS pipelines, ML training workflows, and any multi-step file-processing analysis. Use Nextflow instead for Groovy-based dataflow pipelines or when nf-core ecosystem integration is required.
scikit-image-processing
Python image processing library for scientific microscopy and bioimage analysis. Read/write multi-format images, apply filters (Gaussian, median, LoG), segment objects (thresholding, watershed, active contours), measure region properties (area, intensity, shape), and detect features. Part of the SciPy ecosystem; integrates with NumPy arrays. Use OpenCV instead for real-time video processing; use CellPose for deep-learning cell segmentation; use napari for interactive visualization.
geopandas-geospatial
Python library for geospatial vector data analysis extending pandas with spatial operations. Covers reading/writing spatial formats (Shapefile, GeoJSON, GeoPackage, Parquet, PostGIS), coordinate reference systems, geometric operations (buffer, simplify, centroid, affine transforms), spatial analysis (joins, overlays, dissolve, clipping, distance), and visualization (choropleth, interactive maps, basemaps). Use when working with geographic data for spatial joins, overlay operations, coordinate transformations, area/distance calculations, or map creation.
astropy-astronomy
Core Python library for astronomy and astrophysics. Units & quantities with dimensional analysis, celestial coordinate transformations (ICRS/Galactic/AltAz/FK5), FITS file handling, table operations (FITS/HDF5/VOTable/CSV), cosmological calculations (Planck18, distance/age/volume), precise time handling (UTC/TAI/TT/TDB, Julian dates, barycentric corrections), WCS pixel-world mapping, model fitting, image visualization. For general data tables use pandas/polars; for radio astronomy interferometry use CASA.
rowan
Rowan is a cloud-based computational chemistry platform providing quantum chemistry calculations via a Python SDK. Use it to run geometry optimization, conformer generation, torsional scans, and energy minimization with DFT or semiempirical methods, and retrieve molecular properties (dipole moment, partial charges, frontier orbital energies) — without managing local quantum chemistry software or HPC clusters.