Total 50,476 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Analyze mass spectrometry proteomics data including protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein interactions. Processes MaxQuant, Spectronaut, DIA-NN, and other MS platform outputs. Performs normalization, statistical analysis, pathway enrichment, and integration with transcriptomics. Use when analyzing proteomics data, comparing protein abundance between conditions, identifying PTM changes, studying protein complexes, integrating protein and RNA data, discovering protein biomarkers, or conducting quantitative proteomics experiments.
Scrape social media profiles, posts, comments, followers, and search across 6 platforms via x402. USE FOR: - Getting TikTok, Instagram, X/Twitter, Facebook, Reddit, or LinkedIn profiles - Fetching a user's posts, stories, highlights, or videos - Getting comments, replies, and reactions on posts - Listing followers and following for any account - Searching posts, hashtags, profiles, jobs, and ads across platforms - Cross-platform social media research and monitoring TRIGGERS: - "tiktok", "instagram", "facebook", "linkedin profile", "linkedin posts" - "get followers", "who follows", "following list" - "scrape profile", "get posts from", "social media data" - "instagram stories", "tiktok videos", "facebook page" - "linkedin company", "linkedin jobs", "linkedin ads" - "cross-platform", "social media research" IMPORTANT: StableSocial uses an async two-step flow. Step 1: POST triggers data collection (paid, $0.06). Step 2: Poll GET /api/jobs?token=... until finished (free). All endpoints are $0.06 per call. Use `npx agentcash fetch` for paid POST triggers. Use `npx agentcash fetch` for free GET polling. IMPORTANT: Use exact endpoint paths from the Quick Reference tables below. All paths include a platform prefix (e.g. `https://stablesocial.dev/api/tiktok/...`).
Test JSON SQL primitives with semantic-scholar output
Specialized in database migrations and data seeding. Trigger this when creating tables, modifying schemas, or preparing initial data.
This skill should be used when the user asks to "import", "export", "data migration", "XML", "Excel", "CSV", "bulk load", "data transfer", or any ServiceNow Import/Export development.
Time Travel CRDT Skill
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Dimensional modeling and schema design for data products. Star schema patterns, slowly changing dimensions, denormalization decisions, and architecture decision records. Use when designing data models, reviewing schema designs, choosing between normalization strategies, or when someone asks "how should I model this data?" or "should I denormalize?" For OMOP CDM patterns specifically, see healthcare-data-domain.
Analyze and enforce numerical stability for time-dependent PDE simulations. Use when selecting time steps, choosing explicit/implicit schemes, diagnosing numerical blow-up, checking CFL/Fourier criteria, von Neumann analysis, matrix conditioning, or detecting stiffness in advection/diffusion/reaction problems.
Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.