全部能力

获取、存储并可视化 GitHub 仓库流量数据（访问量、克隆数、来源、Star），并提供趋势图表。需要仓库的推送权限。

Design system and guidelines for Claude's built-in generative UI — the show_widget tool that renders interactive HTML/SVG widgets inline in claude.ai conversations. This skill provides the complete Anthropic "Imagine" design system so Claude produces high-quality widgets without needing to call read_me first. Use this skill whenever the user asks to visualize data, create an interactive chart, build a dashboard, render a diagram, draw a flowchart, show a mockup, create an interactive explainer, or produce any visual content beyond plain text or markdown. Triggers include: "show me", "visualize", "draw", "chart", "dashboard", "diagram", "flowchart", "widget", "interactive", "mockup", "illustrate", "explain how X works" (with visual), or any request for visual/interactive output. Also triggers when the user wants to display financial data visually, create comparison grids, or build tools with sliders, toggles, or live-updating displays.

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business…

Help create git commits and PRs with properly formatted messages and release notes following CockroachDB conventions. Use when committing changes or creating…

此技能应在用户询问加密货币价格、市场数据、K线图表、资金费率、未平仓合约、多空比、大单交易等信息时使用。

Core DQL syntax rules, common pitfalls, and query patterns. Load this skill when you need to write, build, or fix a DQL query — it prevents syntax errors and guides correct usage. Covers fetch commands, data models, field namespaces, time alignment, entity patterns, metric discovery, and smartscape topology navigation. Trigger: "write a DQL query", "build me a query", "DQL syntax", "how do I query logs/spans/metrics in Dynatrace", "create a timeseries", "fix my DQL", "fetch logs", "smartscapeNodes", "query optimization". Do NOT use for explaining an existing query or answering Dynatrace product questions — those do not require query-construction guidance.

Claude Code skill for 四柱八字 (BaZi) Chinese astrology chart reading and destiny analysis using nine classical texts

A Yahoo Finance (yfinance) powered financial analysis tool. Get real-time quotes, generate high-resolution charts with moving averages + indicators (RSI/MACD/Bollinger/VWAP/ATR), summarize fundamentals, and run a one-shot report that outputs both a text summary and a Pro chart.

Set up and run experiments in LaunchDarkly. Create experiments with metrics and treatments, start iterations to collect data, and monitor results.

Distributed traces, spans, service dependencies, and request flow analysis. Use when investigating span-level details, failures, performance bottlenecks, or trace correlation. Trigger: "trace analysis", "slow requests", "failed spans", "service dependencies", "distributed trace", "span details", "HTTP status codes in traces", "database query spans", "messaging spans", "gRPC calls", "Lambda cold starts", "trace ID lookup", "exception analysis", "correlate logs and traces", "request attributes". Do NOT use for explaining existing queries, product documentation or configuration questions, service-level RED metrics (use dt-obs-services), log searching (use dt-obs-logs), or problem analysis (use dt-obs-problems).

Host and process metrics including CPU, memory, disk, network, containers, and process-level telemetry. Use when analyzing infrastructure health, resource utilization, process consumption, or host discovery. Also use when building timeseries queries for host metrics that feed into analytical workflows like anomaly detection, forecasting, or seasonality analysis. Trigger: "show hosts", "CPU usage", "memory utilization", "disk space", "high CPU", "host with most free disk", "top hosts by CPU", "top processes by memory", "Linux hosts in AWS", "what databases are running", "infrastructure costs by cost center", "hosts running EOL Java", "container monitoring", "listening ports", "process resource consumption", "CPU forecast", "memory anomaly", "host seasonality". Do NOT use for explaining existing queries, product documentation questions, Kubernetes pod/workload queries (use dt-obs-kubernetes), AWS cloud resource inventory (use dt-obs-aws), or service-level metrics (use dt-obs-services).

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Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the…

创建、运行和分析 Arize 实验，用于评估和比较模型性能。涵盖实验的增删改查、导出运行记录、对比结果，以及…

生成指向 Arize UI 的深链接，用于 traces、spans、sessions、datasets、labeling queues、evaluators 和 annotation configs。生成可点击的 URL，用于…

In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.

使用 Python、Jupyter 和现代数据工具实现分析、数据分析和可视化最佳实践。

Betting analysis — odds conversion, de-vigging, edge detection, Kelly criterion, arbitrage detection, parlay analysis, and line movement. Pure computation, no API calls. Works with odds from any source: ESPN (American odds), Polymarket (decimal probabilities), Kalshi (integer probabilities). Use when: user asks about bet sizing, expected value, edge analysis, Kelly criterion, arbitrage, parlays, line movement, odds conversion, or comparing odds across sources. Also use when you have odds from ESPN and a prediction market price and want to evaluate whether a bet has positive expected value. Don't use when: user asks for live odds or market data — use polymarket, kalshi, or the sport-specific skill to fetch odds first, then use this skill to analyze them.

Improve database query performance through indexing, query optimization, and execution plan analysis. Reduce response times and database load.

Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog…