Results for “pipeline”

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.

Runs a two-wave validation pipeline (deterministic checks then rule-based checks) before every git commit, writing a sentinel file that a pre-commit hook enforces. Includes optional quick mode (first wave only) and a fix loop with up to three iterations.

This skill should be used when the user asks to "design interview processes", "create hiring pipelines", "calibrate interview loops", "generate interview questions", "design competency matrices", "analyze interviewer bias", "create scoring rubrics", "build question banks", or "optimize hiring systems". Use for designing role-specific interview loops, competency assessments, and hiring calibration systems.

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.

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.

Read and write genomic alignment and variant files in Python with pysam (htslib bindings) — SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, plus region extraction and per-base coverage/pileup. Use when scripting NGS data-processing pipelines that parse, filter, index, or compute coverage over BAM/CRAM/VCF files. Part of the AlterLab Academic Skills suite.

Process audio files with SoX (Sound eXchange). Use when a user asks to apply audio effects, mix and combine audio tracks, convert audio formats, batch process audio files, normalize volume, trim silence, add reverb or echo, change tempo or pitch, split audio files, create spectrograms, generate test tones, resample audio, or build audio processing pipelines. Covers all SoX effects, format conversion, mixing, and batch workflows.

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

Sistema multiagente de IA para assistência em pesquisa científica que automatiza fluxos de trabalho de pesquisa desde análise de dados até publicação. Esta skill deve ser usada ao gerar ideias de pesquisa a partir de conjuntos de dados, desenvolver metodologias de pesquisa, executar experimentos computacionais, realizar buscas de literatura ou gerar papers prontos para publicação em formato LaTeX. Suporta pipelines de pesquisa end-to-end com orquestração de agentes personalizável.