package-distribution
39
python-package-starter
Use `chattool pypi init` to scaffold a minimal Python package, then validate it with doctor/build/check. Example package name `mychat`.
cubenlp
development
تلاش فعال ہے
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مقبول تلاشیں
نتائج
4,166
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صفحہ
95
209 میں سے
کلیدی لفظ
python
نام، ٹیگ یا وضاحت سے تلاش کریں
package-distribution
39
loom-python
Python language expertise for writing idiomatic, production-quality Python code. Covers web frameworks (FastAPI, Django, Flask), data processing (pandas, numpy, dask), ML patterns (sklearn, pytorch), async programming, type hints, testing with pytest, packaging (pip, uv, poetry), linting (ruff, mypy, black), and PEP 8 standards. Use for any Python development including data engineering and machine learning workflows. Triggers: python, py, pip, uv, poetry, virtualenv, pytest, pydantic, fastapi, django, flask, pandas, numpy, dataclass, type hints, asyncio, mypy, ruff, black, sklearn, pytorch, tensorflow, jupyter, pipenv, conda.
cosmix
development
divination-mysticism
39
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.
lingxling
lifestyle
astronomy-physics
39
geopandas
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
lingxling
research
astronomy-physics
39
geomaster
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
lingxling
research
astronomy-physics
39
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.
lingxling
research
astronomy-physics
39
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.
lingxling
research
computational-chemistry
39
rowan
Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.
lingxling
research
computational-chemistry
39
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
lingxling
research
computational-chemistry
39
adaptyv
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
lingxling
research
lab-tools
39
huggingface-trackio
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
lingxling
research
scientific-computing
39
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.
lingxling
research
scientific-computing
39
fluidsim
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
lingxling
research
technical-docs
38
code-documentation
Write comprehensive code documentation including JSDoc, Python docstrings, inline comments, function documentation, and API comments. Use when documenting code, writing docstrings, or creating inline documentation.
aj-geddes
documentation
business-apps
38
quotation-generator
Auto-generate professional PDF proforma invoices with company letterhead, multi-language support, and post-quote tracking.
iPythoning
business
framework-internals
38
kernelgen-flagos
Unified GPU kernel operator generation and optimization skill. Automatically detects the target repository type (FlagGems, vLLM, or general Python/Triton) and dispatches to the appropriate specialized sub-skill. Includes operator generation, MCP-based iterative optimization, and feedback submission sub-skills. Use this skill when the user wants to generate or optimize a GPU kernel operator, create a Triton kernel, or says things like "generate an operator", "create a kernel for X", "optimize triton kernel", or "/kernelgen-flagos".
flagos-ai
development
documents
37
docx-advanced-patterns
Advanced python-docx patterns for handling nested tables, complex cell structures, and content extraction beyond basic .text property. Complements the official docx skill with specialized techniques for forms, checklists, and complex layouts.
belumume
content-media
documents
37
docx-advanced-patterns
Advanced python-docx patterns for nested tables, complex cells, and content extraction beyond .text property. Techniques for forms, checklists, and complex layouts.
belumume
content-media
package-distribution
37
setup-to-pyproject
Migrate Python projects from setup.py/setup.cfg to pyproject.toml for use with uv. Use when upgrading legacy Python packaging, converting setup.py to modern pyproject.toml format, setting up dependency groups for development/testing, and ensuring `uv run pytest` works correctly.
simonw
development