Results for “pyyaml”

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.

Build, fit, validate, and compare Bayesian models using PyMC, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Analyze proteomics and metabolomics mass spectrometry data with PyOpenMS: read/write MS file formats, process spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines using ready-to-run scripts.

Build complete mass-spectrometry workflows with pyOpenMS — feature detection, peptide identification, protein quantification, and full LC-MS/MS pipelines across many MS file formats (mzML, mzXML) and algorithms. Use for comprehensive proteomics and MS data processing — for simple spectral comparison and metabolite identification use matchms. Part of the AlterLab Academic Skills suite.

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

Therapeutics Data Commons. Conjuntos de dados prontos para IA em descoberta de drogas (ADME, toxicidade, DTI), benchmarks, divisões de scaffold, oráculos moleculares, para ML terapêutico e predição farmacológica.

Modelagem Bayesiana com PyMC. Construa modelos hierárquicos, MCMC (NUTS), inferência variacional, comparação LOO/WAIC, verificações posteriores, para programação probabilística e inferência.

Build, fit, validate, and compare Bayesian models using PyMC's modern API, including hierarchical models, MCMC sampling, variational inference, posterior predictive checks, and model comparison.

Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Explains MariaDB Connector/Python's DB API 2.0 behavior, including qmark placeholders, autocommit, prepared statements, buffered cursors, connection pooling, and error handling, for writing and reviewing Python code that uses the mariadb module.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Use Geniml for audited local genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.

Initialize team config for a project. Creates .agenteam/config.yaml (or legacy agenteam.yaml) and generates .codex/agents/*.toml.

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.

Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use pyopenms. Part of the AlterLab Academic Skills suite.

SQLAlchemy Python SQL toolkit and ORM with powerful query builder, relationship mapping, and database migrations via Alembic

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

Optional low-priority poller that inventories Worklink tool pins from <home>/worklink.yaml and files/reuses Chainlink bump issues when upstream versions drift. Opt-in: copy this directory into <home>/skills/worklink-tool-pins/ and configure tool_pins in worklink.yaml.

Manage Linear issues, projects, and teams via the GraphQL API. Create, update, search, and organize issues. Uses API key auth (no OAuth needed). All operations via curl — no dependencies.

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.

Creates, reviews, debugs, and modernizes Python project structure with deterministic tooling including pyproject.toml, uv, Ruff, Pyright, and pytest.

Kit de ferramentas de patologia computacional para análise de imagens de lâminas inteiras (WSI) e dados de imagem multiparamétrica. Use esta habilidade ao trabalhar com lâminas de histopatologia, imagens coradas com H&E, imunofluorescência multiplex (CODEX, Vectra), proteômica espacial, detecção/segmentação de núcleos, construção de gráficos de tecido ou treinamento de modelos ML em dados de patologia. Suporta 160+ formatos de lâmina incluindo Aperio SVS, NDPI, DICOM, OME-TIFF para fluxos de trabalho de patologia digital.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

Evaluates open-ended generation quality of multilingual LLMs across brainstorming, planning, and long-form tasks, using AYA and DOLLY datasets with qualitative fluency and quality scoring.

Apply image blur effects and privacy masks using Python PIL processing. Use when you need to blur, redact faces, or mask sensitive regions in images.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

Execute código Python na nuvem com contêineres serverless, GPUs e autoscaling. Use ao fazer deploy de modelos de ML, executar jobs de processamento em lote, agendar tarefas compute-intensivas ou servir APIs que exigem aceleração GPU ou scaling dinâmico.

Configure and maintain Python projects, dependencies, packaging, environments, linting, formatting, type checking, testing, builds, and publishing. Use for pyproject.toml, an existing repository toolchain, or repository-level Python tooling work, not for a standalone script.