data-analysis
157
excel-analysis
Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.
Microck
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
data-analysis
157
user-file-ops
Simple operations on user-provided text files including summarization.
Microck
data-ai
data-analysis
157
seawater-freezing-temperature
Calculate the freezing point temperature of seawater from absolute salinity and pressure using GSW thermodynamic equations.
InternScience
data-ai
data-analysis
157
seawater-sound-speed-calculation
Calculate sound speed in seawater from practical salinity, temperature, and pressure using the Gibbs Seawater Oceanographic Toolbox.
InternScience
data-ai
data-analysis
157
neural-memory
Associative memory with spreading activation for persistent, intelligent recall. Use PROACTIVELY when: (1) You need to remember facts, decisions, errors, or context across sessions (2) User asks "do you remember..." or references past conversations (3) Starting a new task — inject relevant context from memory (4) After making decisions or encountering errors — store for future reference (5) User asks "why did X happen?" — trace causal chains through memory Zero LLM dependency. Neural graph with Hebbian learning, memory decay, contradiction detection, and temporal reasoning.
nhadaututtheky
data-ai
data-analysis
157
product-health-analysis
Interpret product metrics against goals and surface actionable signals
mohitagw15856
data-ai
data-analysis
157
data-viz-plots
Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
Microck
data-ai
data-analysis
157
buoyancy-acceleration-calculation
Calculate buoyancy forces and acceleration for fluid mechanics and hydrodynamics analysis.
InternScience
data-ai
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-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