「db」の検索結果

Lambda best practices, S3 event patterns, SQS/SNS fanout, and DynamoDB access patterns for serverless AWS architectures.

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。

Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.

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。

Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.

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。

Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security best practices.

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 pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

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。

Assembles component outputs from AI Design Components skills into unified, production-ready component systems with validated token integration, proper import chains, and framework-specific scaffolding. Use as the capstone skill after running theming, layout, dashboard, data-viz, or feedback skills to wire components into working React/Next.js, Python, or Rust projects.

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 AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support. Use when creating ChatGPT-style interfaces, AI assistants, code copilots, or conversational agents. Handles streaming text, token limits, regeneration, feedback loops, tool usage visualization, and AI-specific error patterns. Provides battle-tested components from leading AI products with accessibility and performance built in.

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。

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

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。

Review and optimize application data access that uses Entity Framework Core (EF Core). Use for EF Core performance and correctness reviews of services, repositories, imports, and background jobs: slow or memory-heavy queries, excessive round-trips, hidden SaveChanges, premature materialization, dynamic query construction, result-graph tracking, bulk writes, DbContext lifetime or overlapping operations, search semantics, paging, raw SQL, and model/index coverage. Use whether or not EF Core owns the schema. For EF Core, not Dapper or raw ADO.NET.

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。

Migrate a .NET 8 project to .NET 9 and resolve all breaking changes. USE FOR: upgrading TargetFramework from net8.0 to net9.0, fixing build errors after updating the .NET 9 SDK, resolving behavioral changes in .NET 9 / C# 13 / ASP.NET Core 9 / EF Core 9, replacing BinaryFormatter (now always throws), resolving SYSLIB0054-SYSLIB0057, adapting to params span overload resolution, fixing C# 13 compiler changes, updating HttpClientFactory for SocketsHttpHandler, and resolving EF Core 9 migration/Cosmos DB changes. DO NOT USE FOR: .NET Framework migrations, upgrading from .NET 7 or earlier, greenfield .NET 9 projects, or cosmetic modernization unrelated to the upgrade.

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 or scaffold ASP.NET Core code — Razor Pages, Blazor components, MVC controllers, views, and Minimal API endpoints — without using ASP.NET Core CLI scaffolding/code-generation tools. Use when (1) adding CRUD pages, views, or API endpoints backed by Entity Framework (EF Core) and a database, (2) generating code to create, read, update, and delete data using a DbContext, (3) scaffolding UI components that match the project's existing CSS framework and coding patterns, or (4) creating data-driven forms, tables, and navigation for a model class. Do not use for non-ASP.NET projects or when CLI-based scaffolding is preferred.

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。

Design, tune, or review EF Core data access with proper modeling, migrations, query translation, performance, and lifetime management for modern .NET applications. USE FOR: DbContext, migrations, model configuration, EF queries, tracking, loading, performance, transactions, and EF6 migration decisions. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

Manual QA harness for the senpi coding agent itself. MUST USE after changing packages/ai, packages/agent, packages/coding-agent, or packages/tui — a green typecheck and `npm test` are NOT QA. Drives the real CLI from source in an isolated sandbox (never touches ~/.senpi or real credentials) across four channels: remote RPC (--mode rpc JSONL stdio), TUI smoke (node-pty on Windows, tmux on POSIX), mock loop (a local fake model server for deterministic, zero-token agent-loop runs), and CLI smoke (--help/--print/--list-models). Every helper ships a --self-test. Use whenever someone says qa senpi, test the agent, verify my change, rpc qa, tui qa, mock-loop qa, smoke the cli, or needs evidence that an agent-loop, tool, keybinding, or provider change works end to end. Capture evidence to local-ignore/qa-evidence/.

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 structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.

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。

Structured peer review for analytical work. Use when reviewing teammates' analysis, providing constructive feedback, or establishing analysis quality standards.

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。

Deploy, update, validate, and troubleshoot the AgentKit hybrid-cloud customer-service demo in this directory. Use when a user asks an AI coding agent to follow the README, deploy or update the demo, configure OpenAPI/Runtime/Knowledge/Memory/Sandbox/MCP/Skills/A2A, run customer-view validation, or record manual platform steps and failures.

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。

官方Viking CLI 命令行助手：本CLI覆盖火山引擎/BytePlus VikingDB(向量库)/Knowledge(知识库)/Memory(记忆库)的数据集管理和数据的读写及检索， 可用于扩展Agent的知识检索边界 提升Agent的记忆能力； 当用户对知识库/向量库提问时，使用本Skill; 当用户要操作向量库/知识库 或 从向量库/知识库检索信息时使用本Skill； 当用户要记忆检索和记忆存储时，使用本Skill。

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。

Agent self-evolution system. Collects signals from user feedback, stores execution trajectories (golden + correction pairs), analyzes patterns, proposes mutations to workspace files, and generates HTML reports. Trigger with "/evolve" or when the user asks to improve agent behavior. Also use when the user says "remember this pattern", "don't do X again", or "that was a good approach, save it".

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 kernel threads block on driver synchronization or Verifier reports a lock-order violation; build an owner/waiter graph. Not for treating every watchdog stop as a deadlock or listing every lock type with !locks.

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 a kernel dump reports a Windows bugcheck; decode parameters and recover exception or trap context before investigating your driver. Not for user-mode process crashes or blaming a module from its name alone.

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 an app, service, or user-mode driver host is unresponsive on locks, COM/RPC, I/O, or another process; follow the blocker chain. Not for a crash solely from an exception stack.

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 a native app, service, or user-mode driver host allocation fails; distinguish VA exhaustion, fragmentation, and commit pressure. Not for managed .NET heap growth, proving a leak from one snapshot, or corruption.

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 an app, service, or user-mode driver host TTD recording is available and earlier calls, writes, or lifetimes matter. Not for kernel replay or history from a normal dump.

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 kernel evidence shows stalled I/O, a power IRP, or completion/cancellation misuse; inspect request state and driver ownership. Not for interpreting an empty IRP search in a limited dump as proof of healthy I/O.

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。