Data Science

境界値分析と同値分割によるテストケース設計を体系化するスキル。入力領域の分類、境界値抽出、エッジケース追加、組み合わせ最適化を行い、最小のテスト数で検証精度を高める。Anchors:• The Pragmatic Programmer / 適用: テスト設計 / 目的: 実践的改善と品質維持• Software Testing (Glenford J. Myers) / 適用: 境界値設計 / 目的: 代表値選定の明確化• Rapid Software Testing (James Bach) / 適用: 探索的テスト / 目的: エッジケースの発見Trigger:Use when designing test cases, validating input boundaries, applying equivalence partitioning, or optimizing test coverage.

Deterministic wrapper for tonl CLI (npm package) for structured data operations in JSON/TONL formats. Use when converting JSON↔TONL, querying structured data, validating TONL schemas, or generating statistics. Maps directly to CLI subcommands (encode, decode, query, get, validate, stats).

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.

This skill enables Claude to forecast future values based on historical time series data. It analyzes time-dependent data to identify trends, seasonality, and other patterns. Use this skill when the user asks to predict future values of a time series, analyze trends in data over time, or requires insights into time-dependent data. Trigger terms include "forecast," "predict," "time series analysis," "future values," and requests involving temporal data.

This skill enables Claude to create Application Performance Monitoring (APM) dashboards. It is triggered when the user requests the creation of a new APM dashboard, monitoring dashboard, or a dashboard for application performance. The skill helps define key metrics and visualizations for monitoring application health, performance, and user experience across multiple platforms like Grafana and Datadog. Use this skill when the user needs assistance setting up a new monitoring solution or expanding an existing one. The plugin supports the creation of dashboards focusing on golden signals, request metrics, resource utilization, database metrics, cache metrics, business metrics, and error tracking.

Assists with security incident response, investigation, and remediation. This skill is triggered when the user requests help with incident response, mentions specific incident types (e.g., data breach, ransomware, DDoS), or uses terms like "incident response plan", "containment", "eradication", or "post-incident activity". It guides the user through the incident response lifecycle, from preparation to post-incident analysis. It is useful for classifying incidents, creating response playbooks, collecting evidence, constructing timelines, and generating remediation steps. Use this skill when needing to respond to a "security incident".

Industry-specific guide for implementing Fullstory in e-commerce and retail applications. Covers conversion funnel optimization, product interaction tracking, cart abandonment analysis, checkout flow privacy (PCI compliance), and customer journey mapping. Includes detailed examples for product pages, cart, checkout, and post-purchase experiences.

Implements reliability patterns including circuit breakers, retries, fallbacks, bulkheads, and SLO definitions. Provides failure mode analysis and incident response plans. Use for "SRE", "reliability", "resilience", or "failure handling".

Use when encountering any bug, test failure, or unexpected behavior (including race conditions, deadlocks, concurrency issues) - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) with specialized techniques for deep call stack tracing and concurrency debugging

Master high-performance rendering for large datasets with Datashader. Use this skill when working with datasets exceeding 100M+ points, optimizing visualization performance, or implementing efficient rendering strategies with rasterization and colormapping techniques.

Generate comprehensive tests for architectural layers with coverage-first analysis. Use when testing specific layers (core, domain, application, infrastructure, boundary). Reads testing strategy from playbook or uses interactive template selection.

Professional malware analysis workflow for PE executables and suspicious files. Triggers on file uploads with requests like "analyze this malware", "analyze this sample", "what does this executable do", "check this file for malware", or any request to examine suspicious files. Performs static analysis, threat intelligence triage, behavioral inference, and produces analyst-grade reports with reasoned conclusions.

Master quick plotting and interactive visualization with hvPlot. Use this skill when creating basic plots (line, scatter, bar, histogram, box), visualizing pandas DataFrames with minimal code, adding interactivity and hover tools, composing multiple plots in layouts, or generating publication-quality visualizations rapidly.

This skill enables Claude to forecast future values based on historical time series data. It analyzes time-dependent data to identify trends, seasonality, and other patterns. Use this skill when the user asks to predict future values of a time series, analyze trends in data over time, or requires insights into time-dependent data. Trigger terms include "forecast," "predict," "time series analysis," "future values," and requests involving temporal data.

Domain modeling skill for creating accurate representations of business domains through entities, value objects, aggregates, and domain services. Guides systematic analysis of business requirements and translation into robust domain models. Anchors: • Domain-Driven Design (Eric Evans) / 適用: Entity and Value Object identification / 目的: Clear domain boundaries • Implementing Domain-Driven Design (Vaughn Vernon) / 適用: Aggregate design and modeling patterns / 目的: Consistent aggregate boundaries • Domain Modeling Made Functional (Scott Wlaschin) / 適用: Type-driven design / 目的: Compile-time domain validation Trigger: Use when designing domain models, identifying entities and value objects, defining aggregate boundaries, modeling business invariants, creating ubiquitous language, or translating business requirements into domain structures. Keywords: domain model, entity, value object, aggregate, domain service, invariant, ubiquitous language, business logic

Domain specialist for API routing, route discovery, middleware analysis, and parameter validation. Scope: route discovery patterns, automatic route detection, route mapping, middleware analysis, URL patterns, parameter validation, URL injection prevention. Excludes: backend business logic, database queries, security operations beyond URL injection, frontend routing. Triggers: "routing", "route", "URL pattern", "middleware", "parameter validation", "route discovery", "endpoint", "path", "slug".

R programming for data analysis, visualization, and statistical workflows. Use when working with R scripts (.R), Quarto documents (.qmd), RMarkdown (.Rmd), or R projects. Covers tidyverse workflows, ggplot2 visualizations, statistical analysis, epidemiological methods, and reproducible research practices.

Creates effective data visualizations using various libraries and tools, with focus on clarity and insight communication. Trigger keywords: chart, graph, plot, visualization, dashboard, matplotlib, d3, plotly, visualization.

This skill enables Claude to profile application performance, analyzing CPU usage, memory consumption, and execution time. It is triggered when the user requests performance analysis, bottleneck identification, or optimization recommendations. The skill uses the application-profiler plugin to identify performance bottlenecks and suggest code-level optimizations. Use it when asked to "profile application", "analyze performance", or "find bottlenecks". It is also helpful when the user mentions specific performance metrics like "CPU usage", "memory leaks", or "execution time".

Selects and applies mental models for structured problem analysis. Triggers when user asks "why", "what if", "how should we", needs systematic problem-solving, or mentions analyzing a situation. MUST BE USED when comparing options, making decisions, or evaluating trade-offs.