Browse agent skills for data processing, visualization, and analytics
Categories, GitHub stars, and source recency help organize and order the catalog. These signals are not an endorsement, quality certification, safety review, or guarantee of fit. Open a skill detail and inspect its source before use.
23,041 results
Produce cited research reports from multiple web sources using firecrawl and exa MCP tools — plan sub-questions, search and deep-read sources, then synthesize findings with inline citations and confidence levels. Use when the user asks to research a topic in depth, run a deep dive or investigation, or do competitive analysis, technology evaluation, market sizing, or due diligence on a company.
Explain an idea as an interactive isometric machine figure.
Build and modify Studio Explorer surfaces, including notebooks, chats, SQL snippets, query cells, and their shared toolbar patterns.
Creator-facing social media analytics dashboard in a single HTML file. A platform switcher (X / LinkedIn / YouTube / Instagram), a row of KPI cards (followers, engagement rate, likes, reposts), a follower-growth chart, a "top post this week" preview, and a trending topics / top comments side panel. Use when the brief mentions a "social media dashboard", "creator analytics", "social analytics", or names specific platforms (X, Twitter, LinkedIn, YouTube, Instagram, TikTok) together with metrics like followers, engagement, likes, reposts.
社媒矩阵数据追踪面板模板（Social Media Matrix Tracker）。 Use when users ask for a cinematic, data-dense social media analytics dashboard with multi-platform metrics, interactive charts, hover insights, range compare, and dark/light theme switching in a single HTML artifact.
Creator-facing social media analytics dashboard in a single HTML file. A platform switcher (X / LinkedIn / YouTube / Instagram), a row of KPI cards (followers, engagement rate, likes, reposts), a follower-growth chart, a "top post this week" preview, and a trending topics / top comments side panel. Use when the brief mentions a "social media dashboard", "creator analytics", "social analytics", or names specific platforms (X, Twitter, LinkedIn, YouTube, Instagram, TikTok) together with metrics like followers, engagement, likes, reposts.
社媒矩阵数据追踪面板模板（Social Media Matrix Tracker）。 Use when users ask for a cinematic, data-dense social media analytics dashboard with multi-platform metrics, interactive charts, hover insights, range compare, and dark/light theme switching in a single HTML artifact.
Use this plugin when the user wants a "NYT Graph" HyperFrames motion video — Animated data chart in print editorial style
Use this plugin when the user wants a "Pentagram Stat Frame" HyperFrames motion video — Swiss-grid statistic anchor — giant number, red accent, growing bars, black data bar. Rational and editorial.
Retrieve probabilistic forecasts and their scorecard context. Use when the user asks what World Monitor is forecasting, how probabilities shifted, or how calibrated the forecasts are.
Compare CPI, interest rates and sovereign yield histories from seeded official reports. Use for inflation or rate trends; preserve country provenance, index base, frequency, yield measure and missing readings.
Retrieve probabilistic forecasts and their scorecard context. Use when the user asks what World Monitor is forecasting, how probabilities shifted, or how calibrated the forecasts are.
Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
基于东方财富权威数据库的金融数据查询工具，支持行情、财务及关联关系数据。
Skill content language: Chinese
Design, change or review Netdata topology producers and netdata.topology.v1 payloads, including actors, links, evidence, correlation, presentation, modals, overlays and validators. Operator topology queries use query skills; SNMP profile topology rows use collectors-snmp-profiles.
Run the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table
Wrap getTokenOptimizer().getCompactContext() to retrieve compacted ReasoningBank context for cost-analysis queries; report bridge-reported tokensSaved
Extract entities and relations from source files to build a knowledge graph
Aggregate and display system metrics with anomaly detection for a time period
基于已确认数据执行可审计的数学计算和描述统计。
Skill content language: Chinese
将共享历史中的已验证事实和计算结果整理成符合受众、格式与长度约束的成稿。
Skill content language: Chinese
Extracts and preprocesses whole-slide histology image tiles with Histolab. Use for WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation. For multiplexed imaging or deep learning inference pipelines, use pathml.
Creates and reviews infographics with Nano Banana 2 via OpenRouter. Use for statistical summaries, timelines, comparisons, processes, and visual explanations with supplied data or optional Sonar research. Supports ten layouts, eight style presets, reference images, and accessible palette starting points.
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
Creates and customizes scientific plots with Matplotlib. Used for fine-grained control over plot elements, novel plot types, and scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Supports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.
Creates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.
Creates Seaborn statistical visualizations with pandas integration for distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. Supports function and objects interfaces with explicit aggregation, uncertainty, and missing-data handling. Best suited to static exploratory plots; plotly covers interactive figures and scientific-visualization covers publication styling.
Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
Calculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.
Create, revise, audit, and export manuscript scientific figures in Python or R. Use for 论文配图、科研绘图、多面板图 and submission-ready plots, or explicitly requested AI-generated graphical abstracts and mechanism schematics. Not for interactive dashboards, data cleaning, or statistics-only analysis.
Choose and produce an appropriate chart/dashboard/visual explanation from data, with legible encodings and source/assumption notes.
Creates product analytics or SQL-backed box plot insights in PostHog. Use when a user asks to create, build, or save a box plot, visualize a numeric distribution, compare quartiles or medians across dates or groups, or turn SQL results into a box plot. Chooses between a standard Trends box plot and a SQL insight, validates the distribution data, saves the insight, and verifies it.
Correlation-regime detection and crisis attribution — edge-density regime states with hysteresis, causal (no look-ahead) smoothing, regime-aware exposure context, first-mover crisis attribution with honest NAME / MACRO / AMBIGUOUS / ABSTAIN verdicts, and a correlation-rewiring leaderboard that catches slow bleed-outs
Quantitative statistical methods: ADF unit-root / cointegration tests, GARCH volatility modeling, regression diagnostics (heteroskedasticity / autocorrelation), Bootstrap, and hypothesis testing.
Core technical indicator collection (trend EMA/ADX + mean-reversion BB/RSI + volume-price OBV/volume ratio), generates a composite signal via three-dimensional voting. Pure pandas implementation for any OHLCV data.
Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.
Query a MISP (Malware Information Sharing Platform) instance via PyMISP to compute event statistics, IOC type breakdowns, threat actor galaxy clusters, and tag trends, and generate threat landscape reports with temporal trends. Use when asked to analyze threat intelligence data, summarize top threat actors or malware families, or produce a CTI landscape report from MISP events.
Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.
Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.
Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.
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.
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).
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
