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Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, per
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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.
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Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or es
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Compiled CLI covering all 52 endpoints of the Anytype local API — objects, properties, tags, search, chat, files — one binary, no MCP server needed.
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Master AngularJS to Angular migration, including hybrid apps, component conversion, dependency injection changes, and routing migration.
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Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.
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Use when reviewing AI-generated code before merge or execution: plan AST/security checks, isolated behavior tests and evidence, with language-specific coverage limits.
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Generate project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, c
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Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and inspect billing balance via the AgentMail API.
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Add an iOS App Clip target to an Expo app. Use when the user mentions App Clip, AASA, apple-app-site-association, appclips, smart app banner, or wants to ship a lightweight iOS Clip invoked from a URL
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Guide for VS Code extension development from scaffolding to Marketplace publication
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Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design.
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REST API gateway for ComfyUI servers. Workflow management, job queuing, webhooks, caching, auth, rate limiting, and image delivery (URL + base64).
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Run Hugging Face models in JavaScript or TypeScript with Transformers.js in Node.js or the browser.
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Guidelines for developing with Kysely, a type-safe TypeScript SQL query builder with autocompletion support
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Detect primitive obsession and missing domain/brand types; check that state is modeled via discriminated unions.
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Interactive Domain-Driven Design modeling sessions based on DDD Distilled (Vaughn Vernon) and Domain Modeling Made Functional (Scott Wlaschin). Guides users through strategic and tactical design via 13 phases: discover, storming, contexts, mapping, aggregates, events, validate, glossary, workflows, types, simulate, publish, sync. Phase 13 (sync) reconciles divergences found by --analyze between the domain model and the implementation — judging whether the model or the code is authoritative, then planning and implementing the fix and re-aligning the model. Phases 9-11 take the conceptual model from phases 1-8 and produce workflow pipeline designs, compilable type definitions (TypeScript, Kotlin, Scala, Rust, C#, F#), workflow verification via type-level tests, and automatic UI field enumeration from the domain model. Phase 12 (publish) renders all docs/domain artifacts into one self-contained, cross-linked HTML site (separate files joined by a shared nav). Each phase is an interactive dialogue where AI acts as facilitator and domain expert challenger. Use when: "DDD", "ドメイン設計", "ドメインモデリング", "Event Storming", "Bounded Context", "集約設計", "ユビキタス言語", "コンテキストマップ", "ワークフロー設計", "パイプライン設計", "ステップ分割", "中間型", "型駆動フロー", "型駆動設計", "Domain Modeling Made Functional", "Railway Oriented Programming", "入力画面項目", "フォーム項目洗い出し", "UI 項目抽出", "HTMLにまとめる", "ドキュメントサイト化", "成果物を1つのHTMLに", "publish", "モデルと実装の差異", "差異解消", "実装との乖離", "モデルとコードを一致", "差異の実装計画", "sync", "複数フェーズを逐次実行", "フェーズをまとめて実行", "/ddd run", "サブコマンド一覧", "/ddd list", "/ddd", "ドメイン分析したい", "モデリングしたい", or any domain design / type modeling activity.
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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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Documentation conventions for generating high-quality AI agent skills from TypeScript source. Use when preparing a library for skill generation, auditing JSDoc quality, fixing audit warnings, writing @useWhen/@avoidWhen/@never tags, or asking about documentation conventions for skills. Use this even if the user just says 'audit my docs', 'improve my JSDoc', or 'make my skills better'.
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Documentation conventions for generating high-quality AI agent skills from TypeScript source. Use when preparing a library for skill generation, auditing JSDoc quality, fixing audit warnings, writing @useWhen/@avoidWhen/@never tags, or asking about documentation conventions for skills.
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TypeDoc plugin that generates AI agent skills (SKILL.md) from TypeScript API documentation Use when working with agent-skills, ai, documentation, llm, skill-md, typedoc, typedoc-plugin.
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Generate structured AI agent skills (SKILL.md) and llms.txt from your TypeScript API documentation
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Generate AI agent skills from TypeScript documentation
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Foundation for writing Estuary derivations — what they are, language choice (SQL/TypeScript/Python), project layout, flowctl workflow, stateless vs stateful shuffling, and universal gotchas. Read this before any other derivation-* skill. Use when user says "write a derivation", "how do derivations work", "create a derivation", "derivation setup", or before starting any transformation work.
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