31 件 ・ 関連度順
概要と使いどころ
Build local-first applications with ElectricSQL — sync Postgres data to client devices in real-time. Use when someone asks to "sync database to client", "local-first app", "ElectricSQL", "offline-first sync", "real-time Postgres sync", "sync Postgres to SQLite", or "build an app that works offline". Covers shape-based sync, real-time subscriptions, offline support, and conflict resolution.
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。
Expert guide for Local-First & Offline-First architectures, CRDT conflict-free sync (RxDB, ElectricSQL, PowerSync), OPFS SQLite, Service Worker v3, and PWABuilder deployment / Panduan ahli arsitektur Offline-First & Local-First PWA.
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。
Best practices for building and maintaining applications with PowerSync: Cloud and self-hosted setup, sync configuration, client SDK usage, backend integration (Supabase, custom Postgres, MongoDB, Azure DocumentDB, MySQL, MSSQL), schema changes, watch and reactive queries (useQuery), attachments, the upload queue, and debugging. Use this skill whenever the user mentions PowerSync, a @powersync/* package (@powersync/web, @powersync/react-native, @powersync/node), the powersync Flutter/Dart package, the powersync Rust crate, wa-sqlite, offline-first sync, local-first architecture, sync rules, sync streams, uploadData, fetchCredentials, disconnectAndClear, logout or user switching in a synced app, real-time data replication, Electric Cloud migration, Electric Shapes, an app stuck on Syncing, data not syncing, or wants to add offline-capable sync to a mobile or web app, even if they do not explicitly name PowerSync.
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。
Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.
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。
OpenDesign's enterprise AI-adoption brief: local-first agents at work, the risk controls, the ROI, and the rollout plan. Built as a decision-grade AI literacy deck for leadership, IT, security.
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。
Two related workflows for a locally-cloned codebase, in one skill. Documentation mode produces a single, comprehensive, verifiable architecture document primarily by reading files on disk (local-first) — use it whenever the user wants to understand, map, document, research, or onboard onto a codebase ("research this repo", "write up the architecture", "do an architecture deep dive", "document how this codebase works", "map the system design", "create an onboarding doc"). Modernization mode generates a phased plan to modernize, migrate, upgrade, or rewrite a legacy system ("modernize this", "plan the migration", "how would we rewrite this", "how do we get off this legacy stack"); if no architecture document exists yet it first runs Documentation mode, then continues straight through to the plan. It assumes the legacy stack may be dead, runs a time-boxed feasibility spike, and picks the highest achievable rung on a safety ladder instead of demanding a fully-green legacy CI gate up front.
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 every Kortix test task, behavior change, bug fix, refactor, API route change, CLI change, SDK change, browser journey, test failure, coverage question, local benchmark, or testing infrastructure change. Enforce the single local-first runner, black-box flow contracts, package-local SDK tests, browser-only Playwright tests, and real input/output verification.
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。
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, resolving TinyBase import paths, configuring browser or database persistence, configuring MergeableStore synchronization over WebSockets or Cloudflare Durable Objects, or diagnosing an existing TinyBase application.
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。
Trading Super Intelligence Team. Named real-world personas across 8 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
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。
Sales, GTM & Marketing Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
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。
Data & Analytics Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
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。
Risk & Compliance (GRC) Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
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。
Strategy Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
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 for EverOS local-first Markdown memory service setup, HTTP memory and knowledge APIs, cascade/OME operations, and observability integration.
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 ClawBio for local-first bioinformatics agent workflows: install and run its skill library, route genomic or omics inputs, create reproducible reports, connect optional MCP or Nextflow integrations, and author or validate skills.
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 code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations.
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。
Expert in building custom tools that solve your own problems first. The best products often start as personal tools - scratch your own itch, build for yourself, then discover others have the same itch. Covers rapid prototyping, local-first apps, CLI tools, scripts that grow into products, and the art of dogfooding. Use when "build a tool, personal tool, scratch my itch, solve my problem, CLI tool, local app, automate my, build for myself, " mentioned.
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。
Run an authorized, local-first application security assessment on the current project using this agent's own reasoning. Use when the user invokes Myrqen, asks to security-test or pentest the app they are building, asks whether their app has real vulnerabilities before shipping, wants a Myrqen finding validated, or wants a Myrqen remediation applied and retested.
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。
Install local-first security hardening: pre-commit secret detection, offline dependency scans, static analysis, reports, and gated free CI. Use when hardening repos or adding security hooks. Don't use for incident response or cloud security reviews.
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。
Designs real-time and collaborative systems. Use when building chat, presence, live dashboards, collaborative editing, WebSockets, SSE, WebTransport, or local-first sync.
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Builds local-first note vault retrieval for Obsidian, markdown notebooks, and NotebookLM-adjacent exports. Use when packaging notes into LLM-ready context.
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 when a user needs to inspect, score, repair, or regression-test academic paper formatting in DOCX files, especially thesis, journal, or conference documents with a separate format specification. The skill guides Codex through local-first document handling, rule extraction, formatting repair, report review, and contribution-safe validation.
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。
Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.
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。
Local-first architecture with sync queues
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。