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Use when you need programmatic JDBC in Quarkus — Agroal DataSource, parameterized SQL, transactions, batching, and Dev Services. This should trigger for requests such as Review JDBC or SQL data access in a Quarkus project; Improve transactions and parameter binding for Quarkus JDBC; Translate SQLException to domain exceptions or stream large result sets; Fix CDI self-invocation bypassing @Transactional in Quarkus; Review Agroal DataSource usage in Quarkus JDBC. Part of Plinth Toolkit

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Use when you need to write or review programmatic JDBC with Spring — including JdbcClient (Spring Framework 7+) as the default API, JdbcTemplate only where batch/streaming APIs require JdbcOperations, NamedParameterJdbcTemplate for legacy named-param code, parameterized SQL, RowMapper mapping to records, batch operations, transactions, safe handling of generated keys, DataAccessException handling, read-only transactions, streaming large result sets, and @JdbcTest slice testing. This should trigger for requests such as Review Java code for Spring JDBC (JdbcTemplate, JdbcClient, NamedParameterJdbcTemplate); Apply best practices for Spring JDBC data access in Java code; Detect and fix SQL injection risks in JDBC code; Improve transaction boundaries or exception handling for JDBC operations; Review JdbcClient usage in Spring Framework 7. Part of Plinth Toolkit

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。

Use when you need programmatic JDBC in Micronaut — pooled DataSource, parameterized SQL, io.micronaut.transaction.annotation.Transactional, batching, and domain exception translation. This should trigger for requests such as Review JDBC or SQL data access in a Micronaut project; Improve transactions and parameter binding for Micronaut JDBC; Translate SQLException to domain exceptions or stream large result sets; Fix self-invocation bypassing @Transactional in Micronaut; Review Hikari or pooled datasource usage in Micronaut JDBC. Part of Plinth Toolkit

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。

Use when you need to write fast unit tests for Quarkus applications — including pure tests with @ExtendWith(MockitoExtension.class), @QuarkusTest with @InjectMock for full CDI mock replacement, @InjectSpy for partial CDI bean mocking, REST Assured for resource-focused tests, @ParameterizedTest with @CsvSource / @MethodSource, QuarkusTestProfile for test-specific configuration overrides, and naming conventions (*Test → Surefire, *IT → Failsafe). For framework-agnostic Java use @131-java-testing-unit-testing. This should trigger for requests such as Add or improve unit tests in a Quarkus project; Reduce slow @QuarkusTest usage with Mockito-first tests; Add @InjectSpy partial mocking or QuarkusTestProfile configuration in Quarkus tests; Convert repeated test methods to @ParameterizedTest with @CsvSource or @MethodSource; Write fast pure unit tests for Quarkus services. Part of Plinth Toolkit

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Diagnoses product-market fit from survey, usage and pipeline data and returns a scored verdict with the next moves. Use when you ask 'do we have product-market fit', 'is our PMF survey good enough', 'what does 35% very disappointed mean', 'should we scale yet', 'why is growth stalling', or need a Sean Ellis survey, retention check, or PMF level read. Draws on 137 insights from 66 Lenny's Podcast guests incl. Sean Ellis, Todd Jackson and Rahul Vohra.

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Builds a pricing model, packaging/tier structure and willingness-to-pay research plan, then grades your existing pricing page or plan. Use when you ask 'how much should we charge', 'what should our pricing model be', 'seat vs usage vs outcome pricing', 'good-better-best tiers', 'what goes in the free plan', 'should we raise prices', 'freemium vs free trial', or 'how do we price our AI product'. Draws on 199 insights from 79 Lenny's Podcast guests including Madhavan Ramanujam, Naomi Ionita and Patrick Campbell.

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。

Auto-detect network issues and force proxy usage with proxychains4. Use this skill when encountering connection timeouts, DNS failures, or blocked network access. Default proxy is http://127.0.0.1:9910

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。

This skill should be used when reviewing or auditing existing CSS code for consistency with established patterns. Triggers on "review CSS", "audit styles", "check CSS", "validate stylesheet", "CSS review". Checks semantic naming, dark mode coverage, Tailwind usage, and test coverage.

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Display Claude Router usage statistics and cost savings

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Search code by AST structure using ast-grep. Find semantic patterns like function calls, imports, class definitions instead of text patterns. Triggers on: find all calls to X, search for pattern, refactor usages, find where function is used, structural search, ast-grep, sg.

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Review the symbol-notation section of a mathematical-modeling paper against the full paper. Check table coverage, symbol meanings, units, overloading, case and subscript consistency, first-use definitions, formula usage, and visible table formatting. Use for symbol-table diagnosis, not general paper review.

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。

Assess project-wide structural technical debt: complexity hotspots, deprecated API usage, duplication clusters, architecture rot, and low-value tests. Ranks findings by impact and refactor effort into a report at .turbo/technical-debt.md. Use when the user asks to "assess technical debt", "find technical debt", "review technical debt", "what should we refactor", "find refactoring candidates", "where is the code rot", "what's our worst code", "find low-value tests", or "which tests can we delete". Analysis-only — does not modify code.

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。

Scans an existing codebase and generates project-specific skills that capture inferred conventions such as naming, file organization, framework usage, data access, error handling, and testing style. Writes into the project's chosen skill directory (e.g., `.claude/skills/`, `.agents/skills/`, or a custom path). Use when the user asks to "extract skills from the codebase", "create project skills", "infer project conventions as skills", "codify patterns as skills", or "mine the repo for best practices".

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Review code for bugs, security vulnerabilities, API misuse, consistency issues, simplicity problems, or test coverage gaps and low-value tests by running internal reviews and a peer review in parallel and returning combined findings. Single-concern with a type argument, or full review with no argument. Use when the user asks to "review my code", "full code review", "review my changes", "check for bugs", "scan for bugs", "review correctness", "security audit", "find vulnerabilities", "review security", "check API usage", "verify against docs", "check for cross-file duplication", "review consistency", "check for code reuse", "review simplicity", "find untested code", "find redundant tests", or "review test coverage".

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。

Writes Convex queries, mutations, actions, and internal functions in the object form with args and returns validators, correct ctx usage, runtime boundaries, and error handling. Use when adding or changing anything in convex/*.ts that exports a function, or when deciding between query, mutation, and action.

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This skill should be used when user asks to "query MongoDB", "show database collections", "get collection schema", "list MongoDB databases", "search records in MongoDB", or "check database indexes".

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。

Optimize provider selection, routing, and credit usage across 150+ enrichment sources for company/contact intelligence.

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。

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

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。

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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。

Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

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。

PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS).

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。

Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".

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。

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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。

Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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。