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documentation scientific computing AI Skills

Discover documentation scientific computing skills, compare source repositories and read the instructions for each workflow.

Categories: documentation scientific computing

Showing 169–192 of 926

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GitHub

mesh-analysis

benchflow-ai

Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris.

GitHub

deep-researcher

lofcz

Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources.

GitHub

hypothesis-generation

jimmc414

Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.

GitHub

tooluniverse

jimmc414

Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+...

GitHub

research-lookup

jimmc414

Look up current research information using Perplexity's Sonar Pro or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent...

GitHub

skill-researcher

benbrastmckie

Conduct general research using web search, documentation, and codebase exploration. Invoke for non-Lean research tasks.

GitHub

ark-research

mckinsey

Research technical solutions by searching the web, examining GitHub repos, and gathering evidence. Use when exploring implementation options or evaluating technologies.

GitHub

business-analytics-reporter

ailabs-393

This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement recommendations. Use when the use...

GitHub

bio-flow-cytometry-fcs-handling

GPTomics

Read and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration. Use when loading and inspecting flow or mass cytometry data before preprocessing.

GitHub

bio-pathway-gsea

GPTomics

Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Uses a ranked gene list to find coordinated changes in gene sets without requiring arbitrary significance cutoffs. Detects subtle but coordi...

GitHub

bio-transcription-translation

GPTomics

Transcribe DNA to RNA and translate to protein using Biopython. Use when converting between DNA, RNA, and protein sequences, finding ORFs, or using alternative codon tables.

GitHub

bio-alignment-msa-parsing

GPTomics

Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis.

GitHub

bio-proteomics-data-import

GPTomics

Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Handles common preprocessing steps like contaminant filtering and missing va...

GitHub

bio-pathway-wikipathways

GPTomics

WikiPathways enrichment using clusterProfiler and rWikiPathways. Performs over-representation analysis and GSEA on community-curated biological pathways. WikiPathways is an open, collaborative platform with pat...

GitHub

bio-metabolomics-pathway-mapping

GPTomics

Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when interpreting metabolomics results in the context of biochemical pathway...

GitHub

bio-read-alignment-bwa-alignment

GPTomics

Align DNA short reads to reference genomes using bwa-mem2, the faster successor to BWA-MEM. Use for whole genome sequencing, exome sequencing, and ChIP-seq alignment.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.

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