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Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

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This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. All analysis and output are conducted in English.

日本語の概要は準備中です。原文の説明を表示しています。

Generate personalized cold email sequences (7-14 emails) with A/B test subject lines, follow-up timing recommendations, and integrated social proof. Creates multi-touch campaigns optimized for response rates. Use when users need outbound email campaigns, sales sequences, or lead generation emails.

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Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.

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The 100th skill! Your intelligent guide to all 99 other skills. Recommends the perfect skill for any task, creates skill combinations, and helps you discover capabilities you didn't know you had.

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Head-to-Head Stock Comparison — takes two tickers and compares them across valuation, growth, profitability, technical setup, sentiment, risk profile, and analyst consensus with a scored comparison table and overall recommendation.

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Options Strategy Advisor — analyzes implied volatility, IV rank/percentile, expected moves, put/call ratios, max pain, unusual activity, and recommends specific options strategies with risk/reward profiles based on the trader's directional outlook.

日本語の概要は準備中です。原文の説明を表示しています。

Builds a complete Google Ads keyword strategy with search intent mapping, ad group structure, match type recommendations, negative keyword lists, estimated CPC ranges, Quality Score optimization, and ready-to-implement campaign architecture

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Fix Gorse recommendation engine failing to connect to ClickHouse Cloud with EOF errors. Use when: (1) Gorse pods crash with "failed to connect data database" and "EOF" error, (2) Using ClickHouse Cloud (*.clickhouse.cloud) as Gorse data store, (3) TLS connection works (verified with openssl) but Gorse still fails, (4) Tried secure=true, different ports (8443, 9440), timeout params without success. The Gorse ClickHouse driver is incompatible with ClickHouse Cloud's native protocol.

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Fix Gorse recommendation engine pods crashing with "user: unknown userid 65532" error. Use when: (1) Gorse master/server/worker pods show CrashLoopBackOff or Error status, (2) Logs show "failed to get user directory" with unknown userid, (3) Running gorse containers with runAsNonRoot security context and custom UID. The gorse images call os.UserHomeDir() at startup which requires the UID to exist in /etc/passwd.

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Assess an openai-guardrails-python GitHub issue or pull request as a maintainer. Use to verify the claimed need and practical impact, compare supported alternatives or competing approaches, separate code quality from repository readiness, recommend the maintainer action, and draft a copy-ready comment when evidence, changes, or closure should be requested.

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Documentation-first project planning that produces implementation-ready blueprints and single-shot coding agent prompts. Generates 4 interconnected docs — SPECIFICATION.md, IMPLEMENTATION.md, TASKS.md, BRANDING.md — plus a self-contained PROMPT.md for autonomous execution by any coding agent. Includes interactive tech stack selection, design pattern recommendations, and architecture decisions with trade-off analysis. Trigger when user wants to: plan a project, create specs, architect a system, break work into tasks, choose a tech stack, get design pattern advice, generate a coding agent prompt, or do documentation-first development. Phrases: "plan my project", "spec this out", "architect", "help me plan", "what stack should I use", "generate a prompt", "break this into tasks", "project docs", "I want to build X".

日本語の概要は準備中です。原文の説明を表示しています。

Detect and analyze areas with high maintenance cost, poor design, or accumulated technical debt. Use this skill when reviewing codebases for quality issues, planning refactoring efforts, conducting code audits, assessing project health, identifying maintenance hotspots, or prioritizing technical improvements. Analyzes code smells, architectural issues, dependency problems, test quality, documentation gaps, and provides actionable recommendations with priority rankings.

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Creates visual representations of system structure including architecture diagrams, data flow diagrams, deployment diagrams, and sequence diagrams. Use when Claude needs to visualize system components, infrastructure, data flows, or interactions. Supports Mermaid (recommended for Markdown/GitHub), PlantUML (for detailed UML), and Graphviz/DOT (for complex networks). Trigger when users request diagrams, visualizations, or ask to "show", "diagram", "visualize", or "map out" system architecture, infrastructure, data flows, or component interactions.

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Compares HEAD with the latest published version to analyze real changes, group by type, and recommend version bumps. Use before publishing a release to understand what actually changed.

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Proposes secure remediation strategies for detected security vulnerabilities including buffer overflows, injection risks, insecure deserialization, improper authentication, and unsafe cryptographic usage. Provides recommended security checks, safer API alternatives, design-level changes, code examples, trade-off analysis, and prioritized remediation plans. Does NOT automatically modify code unless explicitly requested.

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Identify and analyze unused or redundant code including unused functions/methods, unused variables/imports, unreachable code, and redundant conditions. Use when cleaning up codebases, improving maintainability, reducing technical debt, or conducting code quality audits. Analyzes Python code using AST analysis and produces markdown reports listing dead code locations with line numbers, severity ratings, and recommendations. Triggers when users ask to find dead code, remove unused code, identify unused imports, find unreachable code, or clean up redundant logic.

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Recommend relevant Isabelle/HOL or Coq standard library theories, lemmas, and tactics based on proof goals. Use when: (1) Users need library lemmas for their proof, (2) Proof goals match standard library patterns, (3) Users ask what libraries to import, (4) Specific lemmas are needed for list/set/arithmetic operations, (5) Users are stuck and need to know what library support exists, or (6) Guidance on find_theorems/Search commands is needed. Supports both Isabelle/HOL and Coq standard libraries.

日本語の概要は準備中です。原文の説明を表示しています。

Identify, analyze, and manage software dependencies before deployment. Use this skill when preparing applications for deployment, resolving dependency conflicts, updating dependencies, auditing security vulnerabilities, managing package versions, or troubleshooting dependency-related issues. Supports multiple package managers (npm, pip, maven, cargo, go mod, composer) and provides actionable recommendations for dependency management.

日本語の概要は準備中です。原文の説明を表示しています。

Recommends appropriate software design patterns based on problem descriptions, requirements, or code scenarios. Use when designing software architecture, refactoring code, solving common design problems, or choosing between design approaches. Analyzes the problem context and suggests suitable creational, structural, behavioral, architectural, or concurrency patterns with implementation guidance and trade-off analysis.

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Conduct comprehensive code reviews identifying bugs, security issues, performance problems, code quality concerns, and best practice violations. Use when reviewing pull requests, examining code changes, evaluating new code, assessing code quality, or providing feedback on implementations. Analyzes code for correctness, security vulnerabilities, performance bottlenecks, maintainability issues, test coverage, documentation quality, and adherence to coding standards. Produces structured markdown reviews with categorized findings, severity ratings, specific examples, and actionable recommendations. Triggers when users ask to review code, check pull requests, evaluate implementations, find bugs, or assess code quality.

日本語の概要は準備中です。原文の説明を表示しています。

Identify and report code smells indicating poor design or maintainability issues in Python code, including duplicate code, magic numbers, hardcoded values, God classes, feature envy, inappropriate intimacy, data clumps, primitive obsession, and long parameter lists. Use when conducting code quality audits, preparing for refactoring, improving codebase maintainability, or performing design reviews. Produces markdown reports with severity ratings, locations, descriptions, and specific refactoring recommendations with before/after examples. Triggers when users ask to find code smells, identify design issues, suggest refactorings, improve code quality, or detect maintainability problems.

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Triage incoming support tickets by categorizing issues, assigning priority (P1-P4), and recommending routing. Use when a new ticket or customer issue comes in, when assessing severity, or when deciding which team should handle an issue.

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Synthesize user research findings from interviews, surveys, and analytics. Create insight reports, customer journey maps, and actionable recommendations based on research data and qualitative findings.

日本語の概要は準備中です。原文の説明を表示しています。