「data framework」の検索結果

Build and modernize WPF applications on .NET with correct XAML, data binding, commands, threading, styling, and Windows desktop migration decisions. USE FOR: working on WPF UI, MVVM, binding, commands, or desktop modernization; migrating WPF from .NET Framework to .NET; integrating newer Windows capabilities into a WPF app. 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。

Maintain or migrate EF6-based applications with realistic guidance on what to keep, what to modernize, and when EF Core is or is not the right next step. USE FOR: EF6 codebases; runtime versus ORM migration decisions; EDMX, code-first, ObjectContext, and legacy data-access review. 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。

Build, maintain, or modernize Windows Forms applications with practical guidance on designer-driven UI, event handling, data binding, MVP separation, and migration to modern .NET. USE FOR: working on Windows Forms UI, event-driven workflows, or classic LOB applications; migrating WinForms from .NET Framework to modern .NET; cleaning up oversized form code. 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。

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。

Use when you need framework-agnostic Docker and container image guidance for Java projects - Dockerfile design, multi-stage Maven builds, jlink custom runtimes, micro runtime distributions such as Alpaquita, JVM container ergonomics, non-root execution, image metadata, .dockerignore, reproducible builds, vulnerability scanning, SBOM awareness, and production-safe container defaults. This should trigger for requests such as Review Java Dockerfile; Improve Docker image security; Add jlink runtime to a Java container; Add containerization to a Java project; Optimize Java container image size; Review Docker build reproducibility. 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 or improve integration tests for Micronaut — @MicronautTest, HttpClient, TestPropertyProvider with Testcontainers, transactional test mode where appropriate, and Maven Surefire/Failsafe splits for *Test, *Tests, *IT, and *AT. This should trigger for requests such as Add Micronaut integration tests with Testcontainers; Wire dynamic datasource or broker URLs for @MicronautTest; Write @MicronautTest integration tests with Testcontainers; Configure replacement beans for Micronaut integration tests; Review Micronaut integration test lifecycle and resources. 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 framework-agnostic OpenAPI 3.x guidance — spec structure, metadata and versioning, paths and operations, reusable schemas, security schemes, examples, documentation quality, contract validation (e.g. Spectral), breaking-change awareness, and handoffs to codegen — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Review an OpenAPI; Improve an OpenAPI; Improve API contract; Improve API schema design. 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 design, review, or improve security in Spring Boot applications — including SecurityFilterChain, OAuth2/JWT resource server patterns, form login basics, method security (@PreAuthorize), CSRF and CORS for APIs, session fixation, security headers, exception handling, password encoding, and sensitive-data-safe logging. This should trigger for requests such as Add Spring Boot security support; Review Spring Boot security configuration; Improve API authorization in Spring Boot; Add JWT resource server security in Spring Boot; Harden Spring Boot security headers and CSRF settings. 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 design, review, or improve security in Quarkus applications — including Quarkus Security with JWT/OIDC, basic auth, @RolesAllowed / @Authenticated / @PermitAll, SecurityIdentity, permission checks, path-based authorization in configuration, exception mapping for auth failures, and sensitive-data-safe logging. This should trigger for requests such as Add Quarkus security support; Review Quarkus security configuration; Improve API authorization in Quarkus; Add JWT/OIDC security in Quarkus; Harden Quarkus authorization rules. 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 design, review, or improve security in Micronaut applications — including micronaut-security authentication, @Secured and intercept-url-map rules, JWT/session strategies, SecurityService checks, CORS, CSRF awareness for browser apps, rejection handlers, and sensitive-data-safe logging. This should trigger for requests such as Add Micronaut security support; Review Micronaut security configuration; Improve API authorization in Micronaut; Add JWT security in Micronaut; Harden Micronaut route authorization rules. 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 MongoDB persistence in Micronaut — including @MongoRepository design, document modeling, indexes, query patterns, and error handling. This should trigger for requests such as Add MongoDB in Micronaut; Review Micronaut Data Mongo design; Improve error handling for Micronaut Mongo operations; Model MongoDB documents for a Micronaut service; Configure Micronaut MongoDB codecs indexes or transactions. 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。

Security-first SOP for multi-tenant RAG systems. Activate when a calling agent is building, reviewing, or debugging any retrieval pipeline whose vector store is shared across more than one user, organisation, workspace, customer, or permission scope. Encodes the single non-negotiable rule — **filter at the vector store query, never after retrieval / never after rerank** — together with the per-vendor query-time filter APIs (Pinecone namespaces + `$eq`/`$in`, Weaviate `multiTenancyConfig` + tenant handle, Qdrant `is_tenant` payload index + `Filter.must`, Chroma `where`, pgvector RLS), and the cross-framework adapters (LlamaIndex `MetadataFilters`, LangChain `filter=` dict). Frame the work as preventing CVE-2024-41892 / EchoLeak / Slack-AI-class cross-tenant leakage, not as "adding a filter for relevance".

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。

Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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 performance specialist covering analysis, optimization, load testing, and framework-specific performance. Use when identifying bottlenecks, optimizing code, conducting load tests, analyzing Core Web Vitals, fixing memory leaks, or improving application performance across all layers (application, database, frontend). Includes React-specific optimization patterns.

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。

Security intelligence for code analysis. Detects SQL injection, XSS, CSRF, authentication issues, crypto failures, and more. Actions: scan, analyze, fix, audit, check, review, secure, validate, sanitize, protect. Languages: JavaScript, TypeScript, Python, PHP, Java, Go, Ruby. Frameworks: Express, Django, Flask, Laravel, Spring, Rails. Vulnerabilities: SQL injection, XSS, CSRF, authentication bypass, authorization issues, command injection, path traversal, insecure deserialization, weak crypto, sensitive data exposure. Topics: input validation, output encoding, parameterized queries, password hashing, session management, CORS, CSP, security headers, rate limiting, dependency scanning.

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 a Chinese mathematical-modeling problem-analysis section against the original problem, checking task classification, data reasoning, cross-problem linkage, model-choice rationale, validation planning, chapter boundaries, and the overall solution framework. Use for diagnosis, not automatic full rewriting.

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 a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and 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。

Conduct proactive, hypothesis-driven threat hunts — search SIEM / EDR / logs for adversaries who haven't tripped an alert yet. ATT&CK-driven, hypothesis-based methodology. Use when the user mentions 'threat hunting,' 'proactive hunt,' 'TaHiTI,' 'PEAK framework,' 'MITRE ATT&CK hunt,' 'hypothesis-driven hunt,' 'hunt hypothesis,' 'living off the land,' 'LOLBins,' 'beaconing,' 'lateral movement detection,' 'data staging,' 'persistence hunting,' or wants to find threats that have evaded existing detections.

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".

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。

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

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。

Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.

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。

Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.

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。

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

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。