Data Analysis Claude Skills
2 409 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 952 tokens or less — that is what one costs your context window when the agent loads it. 549 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 386 copies of these same skills sitting in other people's repositories — counted once here, not 386 times.
2 409 unique 443 authors 5 updated this month 241 from vendors
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
> Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data. Use when the user wants to set up, fix, or evaluate analytics tracking (GA4, GTM, product analytics, events, conversions, UTMs). This skill focuses on measurement strategy, signal quality, and validation— not just firing events.
Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations. Use PROACTIVELY for business intelligence or strategic analysis.
Automated news aggregation and reporting agent.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
This FigMirror skill should be used when the user asks to "mirror this figure's style", "copy this figure's style", "make a chart that looks like this paper", "reproduce this figure with my data", "match this paper's aesthetic", "I want a NeurIPS-quality version of this", or any variant where they hand over a cropped or uncropped reference figure AND their own data and want their data rendered in the same visual register. ALSO triggers when the user attaches a paper-figure screenshot plus tabular data and asks for matplotlib output. Does NOT trigger on generic matplotlib chart requests with no reference image — that's a basic matplotlib task, not style transfer.
Audit one research Workspace for declared Unit outputs and Pipeline target Artifacts, writing `output/CONTRACT_REPORT.md`; use for mid-Run coverage snapshots or final delivery completeness, not deep provenance integrity.
| Bind papers to chapter-level sections first, writing `outline/section_bindings.jsonl` and `outline/section_binding_report.md`.
| Instantiate or update a workspace `UNITS.csv` from a selected pipeline and units template (deps/checkpoints/acceptance).
| Use when a CDS change in **cds-web**, **cds-common**, **cds-mobile**, **web-visualization**, or **mobile-visualization** needs a **jscodeshift** migration in `packages/migrator` to update callers or mitigate breaking API or import moves (add or change a transform, tests, or preset entry).
Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.
Amazon A+ Content strategy and creation. Module layouts, persuasive copy, comparison charts, image briefs, and conversion optimization. Use when the user asks about A+ Content, Enhanced Brand Content, product storytelling, or Amazon listing enhancement.
Premium A+ and Brand Story — module design, lifestyle imagery, comparison charts, mobile optimization
Seller storefront analysis and competitive intelligence for Amazon. Analyzes seller revenue estimation, product portfolio strategy, growth trajectory, and market positioning. Reverse-engineer successful seller strategies and identify expansion opportunities. Use when the user asks about analyzing sellers, competitor seller analysis, seller revenue estimation, storefront analysis, seller strategy, or learning from successful Amazon sellers.
Self-heal + report bugs to make the ecosystem better. When you hit ANY defect in comfyui-mcp / the sidebar panel, OR in a third-party custom node / ComfyUI core, use this: diagnose it, and for OUR repos (comfyui-mcp / comfyui-mcp-panel) **fix it locally FIRST** — patch the code where it actually runs so the user is unblocked immediately — then file a report that includes the diff, so reports arrive as near-PRs, not just tickets. (Attempt the fix exactly ONCE; if it's genuinely upstream-only, mark it and include the precise change needed.) Report it the right way: our intake Worker for our repos; the node's own GitHub for third-party. For OUR repos (artokun/comfyui-mcp, comfyui-mcp-panel, comfyui-mcp-issue-worker) act **autonomously** — fix-then-file and just inform the user, no per-step permission. For THIRD-PARTY / ComfyUI-core it is **offer-and-ask**: propose the workaround/report and act only with the user's go-ahead. **BETA — bias HARD toward fix-then-file (our repos):** trigger on any comfyui-mcp/panel defect you notice — an error, a crash, a tool that returns wrong/misleading/empty output, a silent failure, a confusing result, or a limitation that forced an awkward workaround — EVEN IF you recovered or worked around it. You do NOT need to be blocked. Also on "report this" / "fix this bug". If a future user would hit the same friction, fix-and-file it — the intake Worker dedupes, so over-reporting is cheap and under-reporting costs us the bug. THIRD-PARTY / ComfyUI-core bugs stay offer-and-ask.
Pull the people who liked or commented on any LinkedIn post and segment them by ICP fit (peer / aspirational / prospect / other). Produces an engager roster, tier breakdown, and outbound action lists (follow back, comment-drop, DM-able with one-line openers). Powered by Apify, no LinkedIn login. Triggers on "who liked my post", "who engaged", "engagers report", "audience analytics". Not for tracking author replies to your comments (use linkedin-thread-monitor).
Track which of your LinkedIn comments earned author replies. Flags the 6-24h warm-reply window where thread momentum peaks, classifies threads as hot/warm/cool/dormant, and routes warm ones to linkedin-reply-handler for follow-up drafts. Powered by Apify, no LinkedIn login. Triggers on "what threads need follow-up", "author replied", "monitor my comments". Not for analyzing likers on a post (use linkedin-engager-analytics).
| Lean facilitator for creating, updating, and validating a product brief — the Analysis-phase foundation of the BMAD Method. Captures problem statement, target users, core features, goals, constraints, and success metrics. "let's do discovery", "help me define my product", "what problem are we solving", "capture our product vision", "update the product brief", "validate the brief", "check if the brief is complete", "run discovery", "start a product brief", "brainstorm the product", or "I want to plan a product". Output is a planning artifact only — no code is written or run.
| Distills ANY messy input — brain dump, transcript, long PRD, stakeholder notes, feature request, voice memo — into a tight five-field SPEC.md kernel that any Constraints, Non-Goals, Success Metrics. Use when the user says "create a spec", "write a spec for", "distill this into a spec", "I have a brain dump", "turn this into something structured", "clean up these notes", "make a SPEC from", "I want to define the problem", "help me scope this", "summarize what we're building", "I have a PRD but need a kernel", "what are we actually solving?", or drops raw text/transcript and asks for structure. Also use when starting any new initiative and a clean shared definition is missing.
维护者专用的短剧 know-how 学习、验证与生命周期治理。仅在维护者明确要求从其当前会话提供的授权只读文本源学习完整短剧项目链，并把私有观察逐步转成去标识、去复刻、经盲测与独立审查的公共 reference、rubric 或 synthetic fixture 候选时使用；不用于普通创作、公开运行时取数、媒体生成或粗略数据分析。
Use this skill to diagnose Jetpack Compose stability problems by enabling and reading the Compose Compiler Reports (classes.txt, composables.txt, composables.csv, module.json). Covers the Gradle DSL, the release-only build requirement, and how to interpret per-class and per-composable stability annotations including stable, unstable, runtime, restartable, skippable, readonly, @static, and @dynamic markers. Use when the developer asks "why does this recompose", reports jank, dropped frames, slow scroll, high recomposition count, suspects an unstable parameter, mentions Compose Compiler Reports, classes.txt, composables.txt, module.json, or wants to know which composables are non-skippable. The fix lives in a sibling skill — this one only diagnoses.
Write ggsql queries — a grammar of graphics for SQL. Use when the user wants to create, modify, or understand a ggsql visualization query.
> Generate and improve accessible alt text for data visualizations and images in R packages and Quarto documents. Use when the user wants to add, improve, or audit alt text for figures in a pkgdown site or .qmd files. Activate for requests that mention fig-alt, fig.alt, figure descriptions, or alt text in the context of an R package or Quarto document.
Build modern Shiny dashboards and applications using bslib (Bootstrap 5). Use when creating new Shiny apps, modernizing legacy apps (fluidPage, fluidRow/column, tabsetPanel, wellPanel, shinythemes), or working with bslib page layouts, grid systems, cards, value boxes, navigation, sidebars, filling layouts, theming, accordions, tooltips, popovers, toasts, or bslib inputs. Assumes familiarity with basic Shiny.
> and what triggers approval. Use when authoring a policy charter, defining discount/payment/liability rules, auditing deals, or generating a regional policy.
> Post-signup user onboarding optimization covering activation metrics, time-to-value reduction, onboarding flow design, empty state optimization, multi-channel coordination, and stalled user recovery.
> Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization
> VP of Engineering advisor on org design, productivity, quality, delivery, and capacity planning. Use when scoring engineering org health, designing the eng org, planning capacity, or building the productivity dashboard.
> Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.
> Business intelligence across dashboard design, visualization, and reporting automation. Use when designing dashboards, building KPI frameworks, automating reports, creating data stories, or optimizing BI tool performance.
> Data analysis across SQL, visualization, statistics, and reporting. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven recommendations.
> Applied statistics for business and product questions — test selection, assumption checks, power planning, effect sizes with intervals, multiplicity correction. Use when interpreting an experiment, sizing a study, or vetting a claim.
> instrumenting a production service, tuning alert rules, designing Grafana dashboards, defining SLOs and error budgets, or reducing alert fatigue.
> Scan codebases for technical debt with AST parsing, prioritize by impact, and generate trend dashboards. Use when tracking tech debt, prioritizing refactoring, calculating cost-of- delay, planning sprint debt, or reporting debt to execs.
> This skill should be used when the user asks to "calculate MRR", "analyze churn", "compute SaaS metrics", "do cohort retention analysis", "calculate LTV or CAC", "evaluate unit economics", or "track subscription revenue growth".
> People analytics across workforce metrics, predictive modeling, and employee insights. Use when building turnover models, analyzing engagement surveys, running pay equity regressions, or scoring flight risk.
> Talent acquisition across recruiting strategy, sourcing, interviews, and hiring analytics. Use when writing job descriptions, designing interview scorecards, analyzing hiring funnels, or improving offer acceptance rates.
> event taxonomy, conversion tracking, and UTM strategy. Use when building a tracking plan, debugging missing events, setting up GTM, or auditing existing analytics.
> Product analytics for instrumenting products, defining metrics, and building retention funnels. Use when designing a metric tree, instrumenting a feature, auditing instrumentation, defining a North Star, or building an analytics roadmap.
Same-epoch comparison and analysis of training runs across wandb, neptune, tensorboard, mlflow, or local result files. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Also writes run summaries and experiment reports, analyzes ablations, and monitors training. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, evaluate run-vs-run performance, analyze results, write a training report or experiment summary, summarize training results, monitor training, or asks which run is best.
Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing, hanging, slow, producing weird metrics, has crashed, or asks to debug, diagnose, troubleshoot, or investigate a training issue.
This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement recommendations. Use when the user requests a business performance report, asks to analyze sales data, wants to identify areas of weakness, or needs recommendations on business improvement strategies.
This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
Comprehensive personal finance management system for analyzing transaction data, generating insights, creating visualizations, and providing actionable financial recommendations. Use when users need to analyze spending patterns, track budgets, visualize financial data, extract transactions from PDFs, calculate savings rates, identify spending trends, generate financial reports, or receive personalized budget recommendations. Triggers include requests like "analyze my finances", "track my spending", "create a financial report", "extract transactions from PDF", "visualize my budget", "where is my money going", "financial insights", "spending breakdown", or any finance-related analysis tasks.
Use when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. This should trigger for requests such as Verify performance fix; Verify the performance; Verify the memory; Verify the threading; Compare before and after profiling results; Check for performance regressions after refactoring. Part of Plinth Toolkit