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Implement background job processing systems with task queues, workers, scheduling, and retry mechanisms. Use when handling long-running tasks, sending emails, generating reports, and processing large datasets asynchronously.

Implement robust batch processing systems with job queues, schedulers, background tasks, and distributed workers. Use when processing large datasets, scheduled tasks, async operations, or resource-intensive computations.

Database schema design, optimization, and migration patterns for PostgreSQL, MySQL, and NoSQL databases. Use for designing schemas, writing migrations, or…

Builds, configures, debugs, and optimizes AWS observability using CloudWatch (Logs Insights, Metrics, Alarms, Dashboards, EMF), X-Ray, CloudTrail, and ADOT.…

Design NoSQL database schemas for MongoDB and DynamoDB. Use when modeling document structures, designing collections, or planning NoSQL data architectures.

Monitor database performance and health. Use when setting up monitoring, analyzing metrics, or troubleshooting database issues.

Expert data analysis covering SQL querying, data visualization, statistical analysis, business reporting, and data storytelling. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven recommendations to stakeholders.

Generate a pre-earnings briefing for any stock using Yahoo Finance data. Use this skill whenever the user wants to prepare for an upcoming earnings report, understand what analysts expect, review a company's beat/miss track record, or get a quick overview before an earnings call. Triggers include: "earnings preview for AAPL", "what to expect from TSLA earnings", "MSFT reports next week", "earnings preview", "pre-earnings analysis", "what are analysts expecting for NVDA", "earnings estimates for", "will GOOGL beat earnings", "earnings beat/miss history", "upcoming earnings", "before earnings", "earnings setup", "consensus estimates", "earnings whisper", "EPS expectations", "what's the street expecting", "earnings season preview", any mention of preparing for or previewing an earnings report, or any request to understand expectations ahead of a company's earnings date. Always use this skill when the user mentions a ticker in context of upcoming earnings, even if they don't say "preview" explicitly.

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

Analyze code complexity, cyclomatic complexity, maintainability index, and code churn using metrics tools. Use when assessing code quality, identifying refactoring candidates, or monitoring technical debt.

Analyze stock liquidity using bid-ask spreads, volume profiles, order book depth, market impact estimates, and turnover ratios via Yahoo Finance data. Use this skill whenever the user asks about liquidity, trading costs, bid-ask spread, market depth, volume analysis, slippage, market impact, turnover ratio, or how easy/hard it is to trade a stock without moving the price. Triggers: "how liquid is AAPL", "bid-ask spread", "volume analysis", "order book depth", "market impact of a large order", "turnover ratio", "slippage estimate", "can I trade 100k shares without moving the price", "liquidity comparison", "spread analysis", "ADTV", "Amihud illiquidity", "dollar volume", "execution cost estimate", "liquidity score", penny stocks, small caps, or thinly traded securities.

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power…

Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures),…

Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).

This skill should be used when the user wants to record bookkeeping entries (仕訳), import transaction data from CSV files, receipts, or invoices, or manage their general ledger. Trigger phrases include: "仕訳を入力", "仕訳登録", "CSVを取り込む", "レシートを読み込む", "請求書を取り込む", "帳簿を付ける", "経費を記録", "売上を記録", "仕訳を修正", "仕訳を検索", "仕訳を削除", "取引を登録", "帳簿の初期化".

Converts PDF pages to images and uses vision analysis to extract content including diagrams, charts, and visual elements. Use for PDFs with rich visual…

使用 Chrome DevTools Protocol 捕获网络流量并通过 AI 生成协议分析报告的 Electron 桌面应用

此技能适用于在 Python 中处理带注释的数据矩阵，特别是用于单细胞基因组学分析、管理实验…

Help the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.

Where is this token moving and why? Large transfers, flow trends over time, and breakdown by wallet label.