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All Skills

Total 30,859 skills, Data Processing has 1473 skills

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Showing 12 of 1473 skills

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Data Processingmims-harvard/tooluniverse

tooluniverse-image-analysis

Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical analysis of imaging-derived measurements. Processes ImageJ/CellProfiler output (area, circularity, intensity, cell counts), performs Dunnett's test, Cohen's d effect size, power analysis, Shapiro-Wilk normality tests, two-way ANOVA, polynomial regression, natural spline regression with confidence intervals, and comparative morphometry. Supports CSV/TSV measurement tables, multi-channel fluorescence data, colony swarming assays, and neuron counting datasets. Use when analyzing microscopy measurement data, colony area/circularity, cell count statistics, swarming assays, co-culture ratio optimization, or answering questions about imaging-derived quantitative data.

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2
4 scripts/Checked
Data Processingmims-harvard/tooluniverse

tooluniverse-metabolomics

Comprehensive metabolomics research skill for identifying metabolites, analyzing studies, and searching metabolomics databases. Integrates HMDB (220k+ metabolites), MetaboLights, Metabolomics Workbench, and PubChem. Use when asked to identify or annotate metabolites (HMDB IDs, chemical properties, pathways), retrieve metabolomics study information from MetaboLights (MTBLS*) or Metabolomics Workbench (ST*), search for studies by keywords or disease, or generate comprehensive metabolomics research reports.

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2
6 scripts/Attention
Data Processingmims-harvard/tooluniverse

tooluniverse-gwas-study-explorer

Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS studies for a trait, performing meta-analysis of genetic loci, assessing replication across cohorts, or exploring the genetic architecture of complex diseases.

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2
2 scripts/Checked
Data Processingmims-harvard/tooluniverse

tooluniverse-multiomic-disease-characterization

Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.

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2
1 scripts/Checked
Data Processingmims-harvard/tooluniverse

tooluniverse-multi-omics-integration

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.

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2
Data Processingmims-harvard/tooluniverse

tooluniverse-statistical-modeling

Perform statistical modeling and regression analysis on biomedical datasets. Supports linear regression, logistic regression (binary/ordinal/multinomial), mixed-effects models, Cox proportional hazards survival analysis, Kaplan-Meier estimation, and comprehensive model diagnostics. Extracts odds ratios, hazard ratios, confidence intervals, p-values, and effect sizes. Designed to solve BixBench statistical reasoning questions involving clinical/experimental data. Use when asked to fit regression models, compute odds ratios, perform survival analysis, run statistical tests, or interpret model coefficients from provided data.

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2
3 scripts/Checked
Data Processingmims-harvard/tooluniverse

tooluniverse-variant-analysis

Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.

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2
5 scripts/Checked
Data Processingmims-harvard/tooluniverse

tooluniverse-proteomics-analysis

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.

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2
Data Processingmerit-systems/agentcash-s...

data-enrichment

Enrich contact, company, and influencer data using x402-protected APIs. Superior to generic web search for structured business data. USE FOR: - Enriching person profiles by email, LinkedIn URL, or name - Enriching companies by domain - Finding contact details (email, phone) with confidence scores - Scraping full LinkedIn profiles (experience, education, skills) - Searching for people or companies by criteria - Bulk enrichment operations (up to 10 at a time) - Verifying email deliverability before outreach - Enriching influencer/creator profiles across social platforms TRIGGERS: - "enrich", "lookup", "find info about", "research" - "who is [person]", "company profile for", "tell me about" - "find contact for", "get LinkedIn for", "get email for" - "employee at", "works at", "company details" - "verify email", "check email", "is this email valid" - "influencer", "creator", "influencer contact", "influencer marketing" ALWAYS use `npx agentcash fetch` for stableenrich.dev endpoints - never curl or WebFetch. Returns structured JSON data, not web page HTML. IMPORTANT: Use exact endpoint paths from the Quick Reference table below. All paths include a provider prefix (`https://stableenrich.dev/api/apollo/...`, `https://stableenrich.dev/api/clado/...`, etc.).

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2
Data Processingmerit-systems/agentcash-s...

social-scraping

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/...`).

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2
Data Processingsanjay3290/ai-skills

mssql

Execute read-only SQL queries against multiple Microsoft SQL Server databases. Use when: (1) querying MSSQL/SQL Server databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.

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2
1 scripts/Checked
Data Processingclidey/whodb

query-builder

Convert natural language questions into SQL queries. Activates when users ask data questions in plain English like "show me users who signed up last week" or "find orders over $100".

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2
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