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explainx.ai

home/skills/tag/python
skill tag

python▌

103 indexed skills · max 10 per page

skills (103)

fluidsim

K-Dense Inc./fluidsim · code

0

Framework for computational fluid dynamics simulations using Python, supporting various solvers and high-performance computing.

flowio

K-Dense Inc./flowio · data

0

Parse FCS files for flow cytometry data preprocessing, extracting events as NumPy arrays and converting to CSV/DataFrame.

datamol

K-Dense Inc./datamol · data

0

Pythonic wrapper around RDKit for simplified molecular cheminformatics operations.

cobrapy

K-Dense Inc./cobrapy · research

0

COBRApy is a Python library for constraint-based metabolic modeling, essential for systems biology research and metabolic engineering analysis.

bioservices

cokelaer/bioservices · data

0

Unified Python interface to 40+ bioinformatics services for cross-database analysis and ID mapping.

biopython

biopython/biopython · research

0

Comprehensive molecular biology toolkit for sequence manipulation, file parsing, phylogenetics, and NCBI/PubMed access.

astropy

astropy/astropy · data

0

Comprehensive Python library for astronomy and astrophysics, facilitating astronomical data analysis and manipulation.

anndata

scverse/anndata · data

0

Data structure for annotated matrices in single-cell analysis, facilitating efficient storage, manipulation, and analysis of large datasets.

cloudflare-python-workers

jezweb/claude-skills · Cloud

0

Build Python serverless APIs on Cloudflare Workers with async-only execution, external package support, and multi-step workflow automation. \n \n Deploy Python APIs using the WorkerEntrypoint class pattern with pywrangler CLI; supports all Cloudflare bindings (D1, KV, R2, Workers AI, Durable Objects, Queues) \n Requires async-only code: use httpx or aiohttp for HTTP calls, avoid sync libraries like requests and native C extensions \n Python Workflows enable durable multi-step DAG automation with

python-observability

wshobson/agents · Backend

0

Structured logging, metrics, and distributed tracing patterns for Python production systems. \n \n Covers four core observability areas: structured JSON logging with structlog, Prometheus metrics for the four golden signals (latency, traffic, errors, saturation), correlation ID propagation across service boundaries, and OpenTelemetry distributed tracing \n Includes semantic log level guidance, bounded cardinality rules for metrics to prevent storage explosion, and context manager patterns for co

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