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
> Analyze website traffic, global rank, engagement metrics, and traffic sources for any domain. Use this skill to evaluate affiliate program websites, compare competitor traffic, "analyze traffic for [domain]", "how much traffic does [site] get", "compare traffic between [site A] and [site B]", "is [program] worth promoting based on traffic", "traffic analysis", "website analytics for [domain]", "where does [site] get traffic", "check if [advertiser] is legit", "evaluate [program] website health", "SimilarWeb analysis", "traffic sources for [domain]", "how popular is [site]", "website rank", "domain authority check", "compare affiliate program websites".
Shop analytics — traffic sources, conversion rates, search terms, top listings performance
Shopify/DTC product differentiation strategy tool. Analyze competitor stores, extract pain points from reviews, identify brand positioning opportunities, and generate actionable differentiation strategies. Includes DTC-specific metrics like CAC, LTV, and brand story analysis. No API key required.
TikTok Shop product differentiation strategy tool. Analyze viral product trends, competitor affiliate strategies, content performance, and identify differentiation opportunities. Includes TikTok-specific metrics like engagement rate, viral potential, and creator collaboration insights. No API key required.
Track and analyze e-commerce sales performance across platforms. Set up KPI dashboards, trend analysis, and performance alerts to catch issues and opportunities early.
Tax setup and compliance — sales tax, VAT, duty collection, tax-exempt customers, reporting
TikTok Shop analytics and performance tracking. Video metrics, live selling data, affiliate performance, advertising analytics, and business intelligence. Use when the user asks about TikTok Shop analytics, performance tracking, metrics analysis, or data-driven optimization.
Generates data visualization charts (bar, line, area, pie, doughnut, scatter, radar, treemap) as PNG using Apache ECharts v6. 1080×1080px default, 5 style presets, highlight annotations. Trigger when user says "create a chart", "visualize data", "make a bar chart", "line graph", "pie chart", "data visualization", "chart this data", "plot", "graph", or "visualize these numbers".
全面的电子表格创建、编辑和分析功能，支持公式、格式化、数据分析和可视化。当 Claude 需要处理电子表格（.xlsx、.xlsm、.csv、.tsv 等）时使用，包括：(1) 创建带有公式和格式的新电子表格，(2) 读取或分析数据，(3) 修改现有电子表格同时保留公式，(4) 电子表格中的数据分析和可视化，或 (5) 重新计算公式
>- **WORKFLOW SKILL** — Generate weekly executive reports for the azd team. Pulls PR/issue/release data from GitHub, reads changelogs, combines with team notes, and produces a polished markdown report. summary, generate report, report time. (use changelog-generation), general docs, PRs.
> Execute ES|QL (Elasticsearch Query Language) queries, use when the user wants to query Elasticsearch data, analyze logs, aggregate metrics, explore data, or create charts and dashboards from ES|QL results.
> Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms. Use when loading files or batch importing data — not for reindexing, general ingest pipeline design, or bulk API patterns.
> Create and manage Kibana Dashboards and visualizations. Use when you need to define dashboards and visualizations declaratively, version control them, or automate their deployment.
> Create Vega and Vega-Lite visualizations with ES|QL data sources in Kibana. Use when building custom charts, dashboards, or programmatic panel layouts beyond standard Lens charts.
Analyze Zephyr test case coverage for a Jira user story and produce a QE management report with metrics, risk scoring, and prioritized recommendations.
Enforce structured JSON logging, OpenTelemetry distributed tracing, and RED metrics across backend services. Use when adding request correlation, setting up tracing spans, defining SLO burn-rate alerts, or instrumenting middleware.
Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.
Takes a curriculum framework and a statutory or accreditation requirement list; produces a coverage table, gap summary, and CSV showing which framework content covers each requirement and where gaps exist.
Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.
Synthesise completed KUD charts into a developmental progression matrix and per-competency narrative sections. Use when you need a programme-level view of how knowledge, understanding, and performance develop across bands.
Developer relations - community building, documentation, DevRel metrics.
Develops mathematical understanding through examples, visualization, and analogy
Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.
Product analytics - event taxonomy, funnel analysis, A/B testing, retention metrikleri.
PromQL queries, alerting rules, recording rules, Grafana dashboard JSON, SLO
Turn raw marketing data into actionable insight — analysis by channel, campaign, creative, audience, time. Descriptive → Diagnostic → Predictive → Prescriptive.
Defines a testable hypothesis with clear success metrics and a validation approach. Use when forming assumptions to test or aligning a team on what success looks like, before any experiment is designed. To design the A/B test or experiment that will validate the hypothesis, use measure-experiment-design.
Specifies what questions a dashboard must answer and the metrics, visualizations, filters, and data sources it needs, so data teams build something that informs decisions rather than displaying numbers. Use when requesting a dashboard or formalizing ad-hoc reporting. For the event tracking that feeds the dashboard, use measure-instrumentation-spec instead; instrument first, visualize second.
Designs an A/B test or experiment with variants, success metrics, sample size, and duration for an existing hypothesis. Use when planning an experiment to validate a product change or test an assumption you have already framed. To articulate the hypothesis itself first, use define-hypothesis.
Specifies what analytics events to track, when they fire, and what properties to include, as a contract between product and engineering that prevents undertracked features. Use before engineering builds a feature or when auditing existing tracking for gaps. For the dashboard built on top of these events, use measure-dashboard-requirements instead.
Day 1 afternoon move of a Foundation Sprint. Converts the morning's Basics frame into a defensible strategic position by scoring differentiator candidates against customer-perceived value, choosing two committed differentiators, plotting alternatives on a 2x2 chart, writing decision principles, and producing a one-page Mini Manifesto. Use after Basics is signed; before Approach Options the next morning.
一套模板驱动的单色数据可视化 skill，严格从 Lupi、Basics、Glance 与 Interactive gallery 的真实实现生成 HTML 图表；默认优先 Lupi Editorial 与 Lupi Basics，无合适模板时才使用 Glance。
Compute metrics for Claude Code sessions. Discovers via ccrider, filters trivial, computes friction/opportunity/fingerprint scores. Use for broad session triage.
Analyze trends across session metrics. Computes windowed aggregates, deltas, and compares against MEMORY.md findings. Use periodically for progress tracking.
Analyze skill effectiveness across sessions. Computes per-skill metrics (action rate, friction, outcomes), identifies degrading skills, and generates improvement recommendations. Requires session-scan data in metrics.jsonl.
Design measurement frameworks including event taxonomy, KPI hierarchy, dashboard architecture, attribution models, and analytics implementation strategy. Use this skill whenever the user wants to plan analytics, design dashboards, build event taxonomies, define KPIs, set up tracking, or audit existing measurement. Triggers on analytics strategy, measurement plan, event taxonomy, tracking plan, KPI framework, dashboard design, north star metric, attribution model, conversion tracking, GA4 setup, Mixpanel setup, analytics audit. Also triggers when the user has data but no clear way to use it, or wants to make decisions but doesn't know what to track.
Run a structured after-action review (postmortem, retrospective) on a launch, incident, or completed project to capture timeline, root cause analysis, contributing factors, and actionable lessons. Use this skill whenever the user wants to run a postmortem, retrospective, AAR, or after-action review on any past event. Triggers on after-action report, AAR, postmortem, retrospective, retro, post-incident review, what went well what didn't, lessons learned, blameless postmortem, root cause analysis, RCA, five whys. Also triggers when the user has just shipped something or just resolved an incident and wants to capture learnings.
How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. Honest about scripted-bot (rigid trees, fail edge cases), hallucinating-bot (LLM without structure, makes things up), and structured-guided-conversation (LLM-powered with intent architecture and fallback discipline) patterns. Distinguishes chatbot DESIGN (this skill) from chatbot IMPLEMENTATION (engineering and platform work). Triggers on chatbot, conversational AI, AI agent, chat widget, intent design, conversational flow, bot escalation, LLM grounding. Also triggers when a chatbot is hallucinating, when a scripted bot is failing edge cases, or when a chatbot is being scoped for the first time.
How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and the interpretation failures that produce confidently wrong shipping decisions.
How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and the failure modes that produce data nobody trusts. Triggers on product analytics setup, event taxonomy, tracking plan, instrumentation, schema versioning, North Star metric, retention cohorts, funnel design, naming conventions, instrument new feature, audit existing analytics, dashboard reconciliation, instrumentation debt, Mixpanel setup, Amplitude setup, PostHog setup, warehouse-native analytics. Also triggers when the team has data but cannot trust it, or when designing instrumentation for a new feature, or when auditing an existing setup that has drifted.
Deploy and manage the ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation and analysis. Configure log pipelines, create visualizations, and implement log-based monitoring. Use when centralizing logs, implementing search functionality, or building log analytics platforms.
Configure Grafana Loki for log aggregation and analysis. Set up Promtail for log collection, write LogQL queries, and integrate with Grafana for visualization. Use when implementing lightweight log aggregation, especially in Kubernetes environments.
Set up metrics collection and visualization with Prometheus and Grafana. Configure scrape targets, create PromQL queries, build dashboards, and implement alerting. Use when implementing monitoring, metrics collection, or visualization for applications and infrastructure.
Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.
>- 学术不端/数据造假/统计自洽/结论夸大/幻觉与撤稿 引用/自我抄袭/隐私/版权/署名与 AI 披露/软著专利权属/论文工厂洗稿等风险,把"别造假别夸大"从口头建议 落成**可机检、可阻断、可被总控 run_checkpoint 聚合的机读门**(产 light.findings.v1,Critical fail → exit 1)。 AI 不能自评 → 一律"机读门 + 人工复核";查不到写"待核查/UNRESOLVED",绝不编造;全程在线核实、零本地知识库、零付费 key。
>- Light 科研主线第 7 步·结果分析：不描述好坏、解释「为什么」，把每条结论**绑死到 claim + 证据强度**，并防 p-hacking。 何时用：实验跑完要解读结果 / 问「这些数说明什么」/ 要做显著性检验 + 效应量 + 置信区间 + 多重比较校正 / 担心 p-hacking (多重比较不校正、选择性报告、HARKing) / 要给每条 claim 定证据强度供写作校准措辞 / 判结果支不支撑假设、可不可复现。 触发词：结果分析 / 解读数据 / 这些结果说明什么 / 显著性 / p 值 / 效应量 effect size / Cohen's d / 置信区间 CI / 多重比较 / BH-FDR / Bonferroni / 校正 / p-hacking / 选择性报告 / garden of forking paths / HARKing / 证据强度 / claim 证据绑定 / SHAP / 消融分析 / 切片分析 / 配对检验 / result analysis。 核心纪律：**统计错误 / p-hacking = critical**（spec §4.2，STAGE_GATES[7]=[stat_validity,evidence_strength]）； 过度解读 / 效应量缺失 = warn；**显著性看 q 不看 p**、不显著只能报「未见显著差异」、措辞强度必须匹配证据强度； 统计检查有边界，绝不吹「证明了方法有效 / 因果成立」。本技能是 **7→5（不支撑假设）/ 7→6（不可复现）回炉发起方**。