data-analysis
157
buoyancy-acceleration-calculation
Calculate buoyancy forces and acceleration for fluid mechanics and hydrodynamics analysis.
InternScience
data-ai
sorting
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data-analysis
157
scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
InternScience
data-ai
data-analysis
157
variant-gwas-associations
Query EBI GWAS Catalog for GWAS statistical associations (p-value, effect size, risk allele) between a variant and traits/diseases.
InternScience
data-ai
data-analysis
157
plotly
Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
Microck
data-ai
data-engineering
157
dmir-compiler-analysis
Analyze DTVM's dMIR intermediate representation and compilation pipeline. Translates EVM bytecode sequences into dMIR pseudocode, then into x86 pseudocode, and evaluates performance cost at each stage. Use when the user asks about dMIR instructions, EVM-to-dMIR conversion, dMIR-to-x86 lowering, JIT compilation cost analysis, EVM opcode performance evaluation, or EVM->dMIR performance optimization.
DTVMStack
data-ai
data-engineering
157
molecular-docking-pipeline
Molecular Docking Pipeline - Complete docking workflow: retrieve protein structure, predict binding pockets, prepare receptor, and dock ligand. Use this skill for structural biology tasks involving retrieve protein data by pdbcode run fpocket convert pdb to pdbqt dock quick molecule docking. Combines 4 tools from 2 SCP server(s).
InternScience
data-ai
data-engineering
157
agentdb-advanced-features
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Microck
data-ai
data-engineering
157
agentdb-performance-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Microck
data-ai
data-engineering
157
bioservices
Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).
Microck
data-ai
data-engineering
157
dnanexus-integration
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
Microck
data-ai
data-engineering
157
data-engineering
数据工程。Airflow、Dagster、Kafka Streams、Flink、dbt、数据管道、流处理、数据质量。当用户提到数据管道、ETL、流处理、数据质量时路由到此。
telagod
data-ai
data-analysis
156
infographic-creation
Create beautiful infographics based on the given text content. Use this when users request creating infographics.
nicepkg
data-ai
data-analysis
156
video-analytics-interpreter
Interpret YouTube Analytics, TikTok Analytics, and video performance data. Identifies trends, explains metrics, and provides actionable recommendations for growth. Use when analyzing video performance, understanding metrics, or optimizing channel strategy.
nicepkg
data-ai
data-analysis
156
statistical-analysis
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
lamm-mit
data-ai
data-analysis
156
statsmodels
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
lamm-mit
data-ai
data-analysis
156
seaborn
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
lamm-mit
data-ai
data-analysis
156
scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
lamm-mit
data-ai
data-analysis
156
datacommons-client
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
lamm-mit
data-ai
data-analysis
156
scienceclaw-watch
Run a live multi-agent scientific collaboration session and return a full summary when complete. Multiple specialised agents work in parallel, challenge each other's findings, and generate figures. Results and figures are saved to disk and a summary is returned to chat.
lamm-mit
data-ai
data-analysis
156
commodity-profile
Generate comprehensive one-page commodity profiles with production, trade, risk, research, policy, and web intelligence data
lamm-mit
data-ai
data-analysis
156
hypothesis-generation
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
lamm-mit
data-ai
data-analysis
156
hypogenic
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
lamm-mit
data-ai
data-analysis
156
payram-analytics
Query PayRam dashboard data directly via REST APIs using JWT Bearer authentication. Search payments by tx hash or email, get daily volume breakdowns, check unswept balances, view sweep history, and pull on-ramp metrics. No MCP server needed — the agent calls PayRam APIs directly with a Bearer token. Use when querying payment data, generating reports, monitoring deposits, checking sweep status, or answering questions like "how much did I receive yesterday" or "find payment with tx hash 0xabc".
PayRam
data-ai