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
帮助用户提交 Bug Report 或 Feature Request。支持 GitHub Issue（有账户）和本地存档（无账户）两种模式。当诊断发现是代码 Bug 时主动提议，或当用户说"帮我提 issue"、"这是个 bug"、"我想要这个功能"、"submit a bug"、"feature request"时触发。
Analyze ClickHouse Keeper stress-test results from play.clickhouse.com / keeper_stress_tests data warehouse. Use whenever the user asks about Keeper performance, validates Keeper PRs against stress dashboards, investigates regressions or improvements in Keeper nightlies, asks about specific date windows / SHAs / PR-sets in Keeper stress tests, wants per-PR or window-vs-window comparisons, asks "did this PR break Keeper", asks "what changed in Keeper between dates", or wants a summary report of Keeper stress runs. Triggers on terms like "keeper stress", "keeper PR", "keeper p99", "keeper memory", "keeper rps", "keeper nightly", "keeper-stress-tests", "keeper validation", "keeper regression", or any question referencing the keeper-stress Grafana dashboard. ALWAYS prefer this skill over re-deriving the workflow from scratch — it captures hard-learned lessons about cgroup-vs-Keeper memory, bench-harness confounds, noise floors, and per-PR attribution limits.
Evaluate ClickHouse performance test results from existing CI/dashboard data or local perf.py runs. Use to check PR performance changes, run local performance tests, compare with history, assess flakiness, inspect coverage/PR relevance, and summarize whether a result is actionable.
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.
Track, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, growth efficiency, and cash management. Use this skill when defining a metrics framework, calculating CAC/LTV/burn multiple, benchmarking business health, or preparing metrics dashboards for investors or board reporting.
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
>- Govern Power Automate flows and Power Apps at scale using the FlowStudio MCP cached store. Classify flows by business impact, detect orphaned resources, audit connector usage, enforce compliance standards, manage notification rules, and compute governance scores — all without Dataverse or the CoE Starter Kit. assign ownership, detect orphans, audit connectors, check compliance, compute archive scores, manage notification rules, run a governance review, generate a compliance report, offboard a maker, or any task that involves writing governance metadata to flows. Requires a FlowStudio for Teams or MCP Pro+
> Use this skill whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.
Build pre-earnings analysis with estimate models, scenario frameworks, and key metrics to watch. Use before a company reports quarterly earnings to prepare positioning notes, set up bull/bear scenarios, and identify what will move the stock. Triggers on "earnings preview", "what to watch for [company] earnings", "pre-earnings", "earnings setup", or "preview Q[X] for [company]".
Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model (including financial-model integrity checks like BS balance, cash tie-out, and logic sanity). Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this spreadsheet", "sanity check this", "debug model", "model check", "model won't balance", "something's off in my model", "model review".
| Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format.
Generate professional client-facing performance reports with portfolio returns, allocation breakdowns, and market commentary. Suitable for quarterly or annual distribution. Triggers on "client report", "performance report", "quarterly report for [client]", "generate reports", or "client statement".
Build macroeconomic and rates dashboards combining macro indicators, yield curves, inflation breakevens, and swap rates. Use when monitoring macro conditions, analyzing yield curve shape, decomposing real vs nominal rates, assessing policy rate expectations, or evaluating financial conditions.
Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", "this data is messy".
Quickly screen inbound deal flow — CIMs, teasers, and broker materials — against the fund's investment criteria. Extracts key deal metrics, runs a pass/fail framework, and outputs a one-page screening memo. Use when reviewing new deal flow, triaging inbound materials, or deciding whether to take a first call. Triggers on "screen this deal", "review this CIM", "should we look at this", "triage this teaser", or "deal screening".
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.
| Research Langfuse production telemetry with reusable Datadog queries. Use for tenant or project activity, API usage, queue behavior, spans, logs, metrics, or ad hoc measurements across production regions; pair with debug-issue-with-datadog for root-cause analysis.
| Product analytics with posthog. Use when adding a meaningful user action or feature in `web/**`, touching PostHog capture code, or answering product-usage questions.
Add clidash — a zero-dependency, read-only web dashboard that derives its tabs and tables at runtime from any CLI that lists resources as JSON. Ships pre-wired for NanoClaw's ncl CLI (agent groups, sessions, channels, users, roles), plus message-activity charts, a log tail, and a read-only file viewer for group skills/CLAUDE.md/profiles.
Add a monitoring dashboard to NanoClaw. Installs @nanoco/nanoclaw-dashboard and a pusher that sends periodic JSON snapshots.
Google Workspace Admin SDK: Audit logs and usage reports.
Google Sheets: Read and write spreadsheets.
Google Workflow: Today's meetings + open tasks as a standup summary.
Export a Google Sheets spreadsheet as a CSV file for local backup or processing.
Read event data from a Google Sheets spreadsheet and create Google Calendar entries for each row.
Set up a Google Sheets spreadsheet for tracking expenses with headers and initial entries.
Read data from a Google Sheet and create a formatted Google Docs report.
Append a deal status update to a Google Sheets sales tracking spreadsheet.
Export Google Contacts directory to a Google Sheets spreadsheet.
Fallback authoring playbook for building general-purpose personal assistant agents that do not fit a more specific archetype. Use this only after checking the other archetype skills (coding, spreadsheet, research, customer-support, content-writer, ops-automation). Examples include summarizing emails, drafting short answers, capturing notes, or generic personal-helper agents.
Authoring playbook for building agents that read or write tabular data — Google Sheets, Microsoft Excel, CSV, Airtable, Notion databases, or any spreadsheet. Use this when the user wants an agent that updates rows, reads cells, computes totals, generates reports from sheets, syncs data between spreadsheets, or automates anything involving rows, columns, ranges, or worksheets.
Define a North Star Metric and 3-5 supporting input metrics that form a metrics constellation. Classify the business game (Attention, Transaction, Productivity) and validate against 7 criteria for an effective North Star. Use when choosing a North Star Metric, setting up a metrics framework, learning about the North Star Framework, or deciding what to measure.
Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.
Create a comprehensive product strategy using the 9-section Product Strategy Canvas — vision, segments, costs, value propositions, trade-offs, metrics, growth, capabilities, and defensibility. Use when building a product strategy, creating a strategic plan, or defining product direction.
Create professional, dark-themed SVG diagrams of any type — architecture diagrams, flowcharts, sequence diagrams, structural diagrams, mind maps, timelines, illustrative/conceptual diagrams, and more. Use this skill whenever the user asks for any kind of technical or conceptual diagram, visualization of a system, process flow, data flow, component relationship, network topology, decision tree, org chart, state machine, or any visual representation of structure/logic/process. Also trigger when the user says "画个图" "画一个架构图" "diagram" "flowchart" "sequence diagram" "draw me a ..." or uploads content and asks to visualize it. Output is always a standalone .svg file.
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
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.
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.
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
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.
Generate headcount, attrition, diversity, or org health reports. Use when pulling a headcount snapshot for leadership, analyzing turnover trends by team, preparing diversity representation metrics, or assessing span of control and flight risk across the org.
Build a marketing performance report with key metrics, trend analysis, wins and misses, and prioritized optimization recommendations. Use when wrapping a campaign, when preparing weekly, monthly, or quarterly channel summaries for stakeholders, or when you need data translated into an executive summary with next-period priorities.