2 399 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 951 tokens or less — that is what one costs your context window when the agent loads it. 545 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 355 copies of these same skills sitting in other people's repositories — counted once here, not 355 times.
2 399 unique 443 authors 1 328 updated this month 230 from vendors
| Grafana Dashboard Creator - Auto-activating skill for DevOps Advanced. Part of the DevOps Advanced skill category.
| Helm Chart Generator - Auto-activating skill for DevOps Advanced. Part of the DevOps Advanced skill category.
| This skill enables Claude to collect comprehensive infrastructure performance metrics across compute, storage, network, containers, load balancers, and databases. It is triggered when the user requests "collect infrastructure metrics", "monitor server performance", "set up performance dashboards", or needs to analyze system resource utilization. The skill configures metrics collection, sets up aggregation, and helps create infrastructure dashboards for health monitoring and capacity tracking. It supports configuration for Prometheus, Datadog, and CloudWatch.
| Kpi Definition Helper - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
| Mermaid Gantt Chart Generator - Auto-activating skill for Visual Content. Part of the Visual Content skill category.
| Metric Calculator - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
| This skill enables Claude to aggregate and centralize performance metrics from various sources. It is used when the user needs to consolidate metrics from applications, systems, databases, caches, queues, and external services into a central location for monitoring and analysis. The skill is triggered by requests to "aggregate metrics", "centralize performance metrics", or similar phrases related to metrics aggregation and monitoring. It facilitates designing a metrics taxonomy, choosing appropriate aggregation tools, and setting up dashboards and alerts.
| Build real-time API monitoring dashboards with metrics, alerts, and health checks. Use when tracking API health and performance metrics. Trigger with phrases like "monitor the API", "add API metrics", or "setup API monitoring".
| Org Chart Creator - Auto-activating skill for Visual Content. Part of the Visual Content skill category.
| Pivot Table Creator - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
| Plotly Chart Generator - Auto-activating skill for Visual Content. Part of the Visual Content skill category.
| Report Generator - Auto-activating skill for Business Automation. Part of the Business Automation skill category.
| Report Template Generator - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
| Retention Calculator - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
| Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow". Trigger with relevant phrases based on skill purpose.
| Execute use when setting up log aggregation solutions using ELK, Loki, or Splunk. Trigger with phrases like "setup log aggregation", "deploy ELK stack", "configure Loki", or "install Splunk". Generates production-ready configurations for data ingestion, processing, storage, and visualization with proper security and scalability.
| This skill enables Claude to define and track Service Level Agreements (SLAs), Service Level Indicators (SLIs), and Service Level Objectives (SLOs) for improved service reliability. It is triggered when the user needs to establish, monitor, or analyze service performance metrics. Use this skill when the user mentions "SLA", "SLI", "SLO", "error budget", "service reliability", or "track service performance". The skill helps to define key metrics, set targets, and monitor performance against those targets.
| Statistical Significance Calculator - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
| Status Report Generator - Auto-activating skill for Enterprise Workflows. Part of the Enterprise Workflows skill category.
| This skill generates comprehensive test reports with coverage metrics, trends, and stakeholder-friendly formats (HTML, PDF, JSON). It aggregates test results from various frameworks, calculates key metrics (coverage, pass rate, duration), and performs trend analysis. Use this skill when the user requests a test report, coverage analysis, failure analysis, or historical comparisons of test runs. Trigger terms include "test report", "coverage report", "testing trends", "failure analysis", and "historical test data".
| Time Series Decomposer - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
Define and track SLAs, SLIs, and SLOs for service reliability including availability, latency, and error rates. Use when establishing reliability targets or monitoring service health. Trigger with phrases like "define SLOs", "track SLI metrics", or "calculate error budget".
Validate application performance against defined budgets to identify regressions early. Use when checking page load times, bundle sizes, or API response times against thresholds. Trigger with phrases like "validate performance budget", "check performance metrics", or "detect performance regression".
| Visualization Best Practices - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
| Window Function Generator - Auto-activating skill for Data Analytics. Part of the Data Analytics skill category.
Role-based access control (RBAC) with permissions and policies. Use for admin dashboards, enterprise access, multi-tenant apps, fine-grained authorization, or encountering permission hierarchies, role inheritance, policy conflicts.
Use when turning any content into clear, professional visualizations - board slides, reports, proposals, research summaries, training materials, technical diagrams, infographics, process flows, timelines, benchmarks, waterfall charts, or data-backed visual specs for any audience.
| API reference for CoinMarketCap market-wide endpoints including global metrics, fear/greed, indices, trending topics, and charts. Use this skill whenever the user mentions market API, asks about fear/greed index, wants global metrics or BTC dominance data, needs k-line charts, or is working with market sentiment. This is the complete reference for CMC market-wide API questions.
| Generates a comprehensive crypto market report using CoinMarketCap MCP data. Use when users ask about overall market conditions, sentiment, or want a summary. Also use for questions about fear/greed, BTC dominance, altcoin season, trending narratives, or general "how's the market" queries.
>- End-to-end research workflow skill for investment analysts and policy researchers. Three scope modes the user picks at trigger time — light (4-5 page decision memo, ~15 min, 0 charts), medium (12-15 page topic brief, ~1 h, 6-10 charts), heavy (flagship report 30-40 pages / 15k+ words, ~2-3 h, 25-35+ charts, multi-stage workflow, multi-LLM, PDF + Word + WeChat + HTML derivations). Reports default to English; the AI replies in the user's chat language. Battle-tested on real macro/policy/equity reports (e.g. Saudi Vision 2030 deep-dive). Triggers when user types /analyst-research, /flagship-research, or describes needs like "research report", "topic analysis", "investment research", "做研报", "投研报告", "主题分析", "深度分析", "policy topic-brief), slide decks (use deckster-slide-generator), one-shot Q&A.
>- Use when verifying information (fact, number, quote, event, statement) against authoritative primary sources, or cross-checking a number via true", "find the original source", "where is this number from", "two sources truthfulness check, (2) completeness / out-of-context quoting, (3) one-level reasoning verification, (4) negative-statement handling, (5) multi-source conflict side-by-side output. Dig into whitelisted primary sources only (user-supplied files, official websites & databases, authoritative industry sources); cited reports / charts / datasets must be downloaded and read locally to count as verified — if download is blocked, hand the link to the user. If nothing can be found, plainly state "cannot verify" rather than guessing, patching, or citing secondary paraphrases. Always reply in the user's question language.
Proactively suggest diagrams when explaining complex systems. Triggers on diagrams, charts, visualizations, flowcharts, sequence diagrams, architecture diagrams, ER diagrams, state machines, Gantt charts, mindmaps, C4, class diagrams, git graphs, kanban boards, sankey, timelines, quadrant charts, XY charts, packet diagrams. Use when user asks for visual representations of code, systems, processes, data structures, database schemas, workflows, or API flows. Generate Mermaid diagrams in markdown.
> Audit the health of a PostHog project's data warehouse — find every broken or degraded pipeline item across sources, sync schemas, materialized views, batch exports, and transformations. Use when the user asks "what's broken in my warehouse?", "give me a health check", "audit my data pipeline", "why are some dashboards stale?", or wants a one-shot triage summary before deciding where to spend time. Produces a prioritized report of issues grouped by severity and type, with recommended next steps.
Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handling`), multivariate user handling ("Exclude" vs "First seen variant"), and how to read results once the experiment is live. Use when the user adds or edits a primary or secondary metric (e.g. "add a secondary metric tracking 'downloaded_file' per user"), sets up a ratio metric (e.g. "revenue from purchase_completed / pageviews"), sets up a retention metric (e.g. "$pageview → uploaded_file, 7-day window"), configures custom exposure (e.g. "only count users who hit /checkout"), changes multivariate handling, or asks "who is in the analysis?", "how do I measure impact?", "is this winning?", "what's the confidence level?", or "should I ship?".
Guides agents through the 3-step experiment creation flow: defining the hypothesis, configuring rollout, and setting up analytics. Delegates rollout decisions to configuring-experiment-rollout and metric setup to configuring-experiment-analytics.\nTRIGGER when: user asks to create a new experiment or A/B test, OR when you are about to call experiment-create.\nDO NOT TRIGGER when: user is updating an existing experiment, managing lifecycle, or only browsing experiments.
> Diagnoses CI and pull-request pipeline health for a GitHub repo using the engineering analytics MCP tools — pull-requests (PR list with CI status), workflow-health (per-workflow CI trends), and pr-lifecycle (a single PR's timeline). Use when asked whether CI is getting faster or slower, which GitHub Actions workflow is the slow or flaky long-pole, how long PRs take from open to merge, how an author's merge time compares to the cohort, which open PRs have failing or pending CI, or where a specific pull request is stuck. Triggers on "engineering analytics", "is CI getting slower", "slow workflow", "flaky CI", "time to merge", "cycle time", "PR throughput", "failing checks", "where is PR <n> stuck", "CI long pole", "what's holding up this PR".
Diagnoses bias, anomalies, and strange-looking results on a specific PostHog experiment. Covers empty / 0-exposure experiments, sample ratio mismatch, identity fragmentation, multi-variant exposure, uneven-split exclusion bias, significance traps (peeking, A/A, Bayesian vs Frequentist), PostHog-vs-SQL discrepancies, and surprises after mid-run edits. Symptom-driven dispatch to the right diagnostic.\nTRIGGER when: user asks 'is my experiment biased?' or 'why 0 exposures?', references the bias banner, says a variant looks strange / wrong / off, sees significance flipping, notices PostHog numbers disagreeing with their SQL, sees an A/A test showing significance, or reports surprises after mid-run edits.\nDO NOT TRIGGER when: creating a new experiment (use creating-experiments), only configuring rollout (use configuring-experiment-rollout) or metrics (use configuring-experiment-analytics), or only asking lifecycle questions (use managing-experiment-lifecycle).
>- Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.
>- Add PostHog product analytics events to track user behavior. Use after implementing new features or reviewing PRs to ensure meaningful user actions are captured. Also handles initial PostHog SDK setup if not yet installed.