package-distribution
24
vnstock-solution-architect
Comprehensive expert guide for building Python applications (scripts, notebooks, apps) using the full Vnstock ecosystem (free + sponsored).
vnstock-hq
development
requête active
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package-distribution
24
python-patterns
Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.
codelably
development
divination-mysticism
24
sympy
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
oimiragieo
lifestyle
astronomy-physics
24
huggingface
Hugging Face Hub operations, model inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment via MCP tools and Python APIs
oimiragieo
research
scientific-computing
24
omero-integration
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
oimiragieo
research
astronomy-physics
24
astropy
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
oimiragieo
research
scientific-computing
24
bio-metabolomics-msdial-preprocessing
MS-DIAL-based metabolomics preprocessing as alternative to XCMS. Covers peak detection, alignment, annotation, and export for downstream analysis. Use when processing MS-DIAL output files for R/Python analysis or when preferring GUI-based preprocessing.
mdbabumiamssm
research
scientific-computing
24
bio-entrez-search
Search NCBI databases using Biopython Bio.Entrez. Use when finding records by keyword, building complex search queries, discovering database structure, or getting global query counts across databases.
mdbabumiamssm
research
scientific-computing
24
bio-sequence-properties
Calculate sequence properties like GC content, molecular weight, isoelectric point, and GC skew using Biopython. Use when analyzing sequence composition, computing physical properties, or comparing sequences.
mdbabumiamssm
research
scientific-computing
24
dnanexus-integration
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
oimiragieo
research
scientific-computing
24
simpy
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
oimiragieo
research
scientific-computing
24
uniprot-database
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
oimiragieo
research
scientific-computing
24
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
oimiragieo
research
scientific-computing
24
bioservices
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
oimiragieo
research
scientific-computing
24
rowan
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.
oimiragieo
research
scientific-computing
24
kegg-database
Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.
oimiragieo
research
media
23
ffmpeg-modal-containers
Complete Modal.com FFmpeg deployment system for serverless video processing. PROACTIVELY activate for: (1) Modal.com FFmpeg container setup, (2) GPU-accelerated video encoding on Modal (NVIDIA, NVENC), (3) Parallel video processing with Modal map/starmap, (4) Volume mounting for large video files, (5) CPU vs GPU container cost optimization, (6) apt_install/pip_install for FFmpeg, (7) Python subprocess FFmpeg patterns, (8) Batch video transcoding at scale, (9) Modal pricing for video workloads, (10) Audio/video processing with Whisper. Provides: Image configuration examples, GPU container patterns, parallel processing code, volume usage, cost comparisons, production-ready FFmpeg deployments. Ensures: Efficient, scalable video processing on Modal serverless infrastructure.
JosiahSiegel
content-media
media
23
python-opencv
Complete OpenCV computer vision system for Python. PROACTIVELY activate for: (1) Image loading with cv2.imread (BGR format gotcha), (2) Video capture with cv2.VideoCapture, (3) Color space conversion (BGR to RGB, HSV, grayscale), (4) Image filtering (GaussianBlur, medianBlur, bilateralFilter), (5) Edge detection (Canny), (6) Contour detection with cv2.findContours, (7) Image resizing with interpolation methods, (8) Template matching, (9) Feature detection (SIFT, ORB, AKAZE), (10) Drawing functions (rectangle, circle, text), (11) Video writing with cv2.VideoWriter, (12) Morphological operations, (13) Deep learning with cv2.dnn module, (14) GPU acceleration with cv2.cuda, (15) Coordinate system (x,y vs row,col) gotchas. Provides: Image processing patterns, video capture/writing, memory management, performance optimization, Jupyter notebook workarounds. Ensures correct BGR handling and memory-safe OpenCV usage.
JosiahSiegel
content-media
media
23
ffmpeg-python-integration-reference
Authoritative Python-FFmpeg parameter integration reference ensuring type safety, accurate parameter mappings, and proper unit conversions. PROACTIVELY activate for: (1) ffmpeg-python library usage, (2) Python subprocess FFmpeg calls, (3) Caption/subtitle parameter mapping (drawtext, ASS), (4) Color format conversions (BGR, RGB, ABGR, ASS &HAABBGGRR), (5) Time unit conversions (seconds, centiseconds, milliseconds), (6) Type safety validation (int, float, string), (7) Coordinate systems, (8) Parameter range enforcement, (9) Frame pipe handling, (10) Error detection for type mismatches. Provides: Complete parameter type reference, color format conversion tables, time unit conversion formulas, validation patterns, working Python examples with proper typing.
JosiahSiegel
content-media