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Autonomously improve an MCP skill via the skillit audit→draft→review loop (build or runtime mode) Also: skillit, refine, mcp, skill-generation, cli, audit, overlay.

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Bootstrap an AI-agent skill from a TypeScript codebase by running the deterministic skillit generate/audit loop and enriching repo source (JSDoc, README, config-type properties, MCP tool annotations, examples, package.json) until the skill reaches its grade target. Use for cli, typedoc, config, or mcp (build-mode) projects; never edit SKILL.md/references directly.

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Write Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.

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Control which fields are included or excluded in materializations using depth modes and field overrides. Use when reducing table width, including nested fields, excluding PII, or optimizing performance. Use when user says "exclude fields", "include nested field", "too many columns", "select specific fields", or "field depth".

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在授权测试中核验 Node.js 权限模型（--permission / --experimental-permission，及旧 policy.json）能否被内部 API 绕过。当目标依赖 Node.js Permission Model 做沙箱隔离、需要判断给定 Node 版本下是否存在已知逃逸（inspector 断点、process.binding、fs.statfs/openAsBlob、process.mainModule.require、Module._load、path.resolve 覆盖、Uint8Array 路径、符号链接重命名）时使用。适用目标类型 Node.js 运行时 / 依赖权限模型的服务或 CLI。触发场景包括用户说"这个 Node 权限模型靠谱吗""--permission 能被绕过吗""测下 policy.json 沙箱逃逸""inspector/process.binding 能突破权限吗"。输出：给定 Node 版本下各绕过原语的命中矩阵 + 建议升级版本（含 killed 记录）。

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Generative UI (GenUI) for Flutter using AI models like Google Gemini, Anthropic Claude, or OpenAI via the genui package. Use this skill when building dynamic AI-driven interfaces, conversational UI flows, server-rendered screen layouts, LLM-powered dashboards, chat-based UI generation, or implementing prompt-to-UI systems. Supports AI-generated widgets, reactive data binding, ContentGenerator integration, and A2uiMessageProcessor for real-time UI updates from AI responses. Ideal for apps where UI structure is determined by AI/LLM output rather than hardcoded layouts.

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Implements type-safe reactive SQL persistence in Flutter using Drift v2.32 (formerly Moor) built on SQLite with automatic code generation. Activates when defining table schemas with Drift DSL, writing type-safe join or subquery operations, handling schema migrations with MigrationStrategy, using reactive watch() streams for live UI updates, implementing ACID transactions, defining foreign key constraints or cascade deletes, enabling WAL mode for write performance, troubleshooting generated .g.dart type mismatches, migrating from sqflite or sqlite3 v2 to v3, using DAOs for query separation, or building offline-first apps requiring strong relational data integrity and compile-time SQL verification.

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Covers Flutter testing across all three layers: unit tests (business logic, ViewModels, repositories), widget tests (UI rendering, interactions, golden regression), and integration/E2E tests (full app flows, native OS interactions). Use this skill when writing any Flutter test, setting up mocking with mocktail or mockito, implementing golden file tests, debugging async failures (pumpAndSettle timeouts, Timer leaks, FakeAsync deadlocks), fixing cross-platform golden pixel diffs in CI/CD, testing state management (Riverpod/BLoC/Provider), handling permission dialogs or native system UI with Patrol, or troubleshooting MediaQuery/Theme missing errors in widget tests.

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SharedPreferences (v2.5.5) for Flutter providing simple persistent key-value storage for non-sensitive user settings and app preferences. Use this skill when storing user preferences (theme mode, language selection, font size), app settings and configuration, feature flags or A/B test assignments, simple cache data, onboarding completion status, last selected values (filters, sort options), user consent flags, or any non-sensitive data that survives app restarts. Supports String, int, double, bool, and StringList types. NOT suitable for storing tokens, passwords, API keys, or any sensitive/encrypted data (use flutter_secure_storage instead). Ideal for quick persistent storage without database overhead.

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Dart 3 modern features (through 3.10) and Effective Dart best practices including records, patterns, sealed classes, dot shorthands, null-aware collection elements, and wildcard variables. Use when adopting Dart 3.7+ syntax, using dot shorthands for enums or static members, using null-aware elements in lists or maps, using wildcard _ parameters, improving code quality, or reviewing Dart code conventions.

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Develops full-stack Dart backends using the Serverpod framework with PostgreSQL, Redis, and Docker. Use when building type-safe API endpoints, defining YAML data models, configuring Serverpod auth, writing server-side tests, running database migrations, deploying to Docker/AWS/GCP, or using Serverpod Mini for database-less BFF proxy endpoints.

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AI-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA pipelines.

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Control risky structural change by proving a refactor is necessary and that behaviour, contracts, data, security boundaries, and operational properties can be preserved through a staged migration. Use whenever the user wants to refactor, restructure, clean up, modernise, extract, migrate, rewrite, or re-architect existing code, or asks whether they should. Prefer this over starting the refactor. DO NOT REFACTOR is a first-class outcome. Part of the audit suite; inherits the shared constitution while its own job is proving necessity and behavioural preservation.

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Design a test strategy around product risk, business invariants, failure modes, and existing controls, deciding what deserves testing and at which boundary rather than maximising coverage. Use whenever the user asks how to test something, wants a test plan or strategy, is deciding between unit/integration/contract/E2E/property/concurrency testing, or wants to know whether existing tests actually protect what matters. Prefer this over writing tests immediately. Part of the audit suite; inherits the shared constitution while its own job is proving risk 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。

Use this skill whenever the user asks to inspect, audit, review, assess, or evaluate a codebase/project for maintainability, extensibility, architecture, reliability, security, testing, engineering quality, or AI-agent/prompt/tooling risks. Also use this when invoked by `/inspect`. It performs a read-only, evidence-backed, multi-agent project inspection with complete file coverage, per-dimension scoring, blunt technical findings, and explicit degraded-mode disclosure.

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We find that the evolution of cooperation differs markedly across base models, with societies of Cla

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Reactアプリケーションを最新バージョンにアップグレードし、クラスコンポーネントからフックへ移行し、並行機能を採用します。Reactコードベースのモダナイゼーション、React Hooksへの移行、または最新Reactバージョンへのアップグレード時に使用します。

async/await、分割代入、スプレッド演算子、アロー関数、Promise、モジュール、イテレータ、ジェネレータ、関数型プログラミングパターンなどのES6+機能を習得し、クリーンで効率的なJavaScriptコードを書きます。レガシーコードのリファクタリング、モダンパターンの実装、JavaScriptアプリケーションの最適化時に使用してください。

Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.

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Use when a project follows a structured operating model with: two work lanes (Domain, System), unresolved work tracked as Epic, bounded execution tracked as Ticket, role-based behavior (architect, runner), helper-backed SQLite workflow tables, inbox and outbox files as task and result bodies.

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Elite code review expert specializing in modern AI-powered code

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SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its...

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You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C

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。