Results for “pypykatz”

Read, write, and modify DICOM medical imaging files, including pixel data extraction, anonymization, format conversion, and compression handling.

Creates desktop applications with PyQt6, Qt Widgets, signals/slots, and Qt Designer.

Test Python services with pytest, async coverage, monkeypatch, and boundary-focused fakes. Use when writing Python tests, fixtures, async tests, regression tests, or dependency-isolated verification.

Biblioteca Python para trabalhar com arquivos DICOM (Digital Imaging and Communications in Medicine). Use essa skill ao ler, escrever ou modificar dados de imagens médicas em formato DICOM, extrair dados de pixel de imagens médicas (TC, RM, Raio-X, ultrassom), anonimizar arquivos DICOM, trabalhar com metadados e tags DICOM, converter imagens DICOM para outros formatos, processar dados DICOM comprimidos ou processar conjuntos de dados de imagens médicas. Aplica-se a tarefas envolvendo análise de imagens médicas, sistemas PACS, fluxos de trabalho de radiologia e aplicações de imagem médica.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants for clean API contracts in Python applications using Pydantic v2.

Fuzz Python code and C extensions with coverage guidance and AddressSanitizer support using a libFuzzer-based fuzzer.

Configures Python projects with modern tooling (uv, ruff, ty) for new projects, scripts, or migrations from legacy tools.

Read, write, and manipulate DICOM medical imaging files, including pixel data extraction, metadata editing, anonymization, format conversion, and compression handling.

Debug Python: pdb REPL + debugpy remote (DAP).

---

Write, review, debug, test, or optimize Python code using the rustworkx graph library, with explicit handling of graph kind, index lifecycle, payload semantics, and algorithm result mapping.

Read, write, and manipulate genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

pytest Python testing framework. Covers fixtures, parametrize, and mocking. Use for Python testing. USE WHEN: user mentions "pytest", "python test", "fixture", asks about "@pytest.fixture", "@pytest.mark.parametrize", "conftest.py", "pytest coverage", "python unit test" DO NOT USE FOR: JavaScript/TypeScript - use `vitest` or `jest`; Java - use `junit`; E2E web tests - use `playwright`; Load testing - use locust

Atheris is a coverage-guided Python fuzzer based on libFuzzer. Use for fuzzing pure Python code and Python C extensions.

Use when generating changesets in the pythinker-code repository, including package bump selection, internal package and CLI bundle handling, bump levels, major confirmation, and English changelog wording.

---

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Read, write, and analyze genomic datasets including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences using a Pythonic interface to htslib.

PyAutoGUI와 OpenCV를 결합하여 화면 고속 캡처, 고정밀 템플릿 매칭, 멀티스레딩 병렬 제어 및 다국어 텍스트 입력 우회를 지원하는 강력한 GUI 자동화 스킬입니다.

Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.

Debug Python code interactively with pdb and debugpy, covering local breakpoints, post-mortem analysis, and remote attachment to running processes.

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Debug Python with pdb, breakpoint(), post-mortem inspection, and debugpy remote attach.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.