「models」の検索結果

Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.

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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

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Optimal ViewModel and Wizard creation patterns for Avalonia using Zafiro and ReactiveUI.

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Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.

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Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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Review or refactor an existing SKILL.md and its bundled resources — a smaller always-loaded core, conditional references, preserved behavior, lean/guided consumer profiles, and an A/B protocol to compare a candidate with the previous version. Use when a skill repeats itself, loads references on every run, triggers too broadly or misses legitimate requests, or needs its efficiency measured across models. Do NOT use for writing a new skill from scratch, refactoring application code, translating a skill verbatim, or performing the target skill's own task.

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Use when the user wants a backend-only action plan — API endpoints, services, models, migrations, jobs and their tests — 'plano de backend para o endpoint X', 'plan the orders API'. Writes .planning/feat/features/{slug}/plan.md with the PWDEVIA method. Do NOT use for UI work (feat-frontend), full features spanning backend and UI (feat-feature), writing code (feat-exec) or quick 1–3 file fixes (feat-quick).

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Design scalable, normalized database schemas with proper relationships, indexes, constraints, and migration strategies for relational and NoSQL databases. Use when designing database schemas, planning table relationships and foreign keys, creating indexes for query optimization, defining constraints and validations, designing data models for scalability, planning database migrations, choosing between SQL and NoSQL, implementing sharding strategies, optimizing query performance, or establishing data integrity rules.

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Architect scalable backend services using layered architecture, dependency injection, middleware patterns, service classes, and separation of concerns. Use when building API services, implementing business logic layers, creating service classes, setting up middleware chains, implementing dependency injection, designing controller-service-repository patterns, handling cross-cutting concerns, creating domain models, implementing CQRS patterns, or establishing backend architecture standards.

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Design clean, scalable, and maintainable REST and GraphQL APIs following industry best practices. Use when designing public or internal APIs, planning endpoint structures, defining request/response contracts, establishing versioning strategies, implementing authentication patterns, designing data models, creating API documentation, ensuring consistent error handling, optimizing for performance, or establishing service contracts between microservices.

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Review a local diff or PR for unnecessary durable surface: new files, public types, interface methods, DTOs/view models, helpers, routes, DI registrations, services, repositories, and dependencies that should reuse existing owners instead.

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Three-axis orchestrator: (1) clean section boundaries — naming, routes, views, ViewModels, DB ownership, cross-section access; (2) internal cohesion — no EF in service layer, caching in service only, proper interfaces, reusable ViewComponents, architecture-test coverage; (3) focused tests — grouped under the section, covering invariants/negatives/triggers, pruned of redundancy. Push-and-bot-review loop. Use when a sizable PR landed, a section shows arch drift, or /pr-review returned a non-trivial violation list. Camps is the gold-standard reference.

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Create effective content models for your blocks that are easy for authors to work with. Use this skill anytime you are building new blocks, making changes to existing blocks that modify the initial structure authors work with.

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Refactor and review SwiftUI view files for consistent structure, dependency injection, and Observation usage. Use when asked to clean up a SwiftUI view’s layout/ordering, handle view models safely (non-optional when possible), or standardize how dependencies and @Observable state are initialized and passed.

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Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.

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Think through the consequences of consequences — not just what happens immediately, but what happens next, and next after that, across time. Load when a decision looks obviously good or obviously bad on initial read, when the user is optimising for a short-term outcome that might create a long-term problem, when unintended consequences are a concern, or when deep-thinking diagnoses a second-order frame. Triggers on "what are the downstream effects", "what happens after that", "unintended consequences", "think ahead on this", "long-term vs short-term", or "what comes after that". Based on Howard Marks second-level thinking and Farnam Street mental models. Most powerful for decisions with delayed consequences or systemic effects.

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Apply Boyd's OODA loop to navigate fast-moving, uncertain, or competitive situations — Observe what is actually happening, Orient through mental models and context, Decide on the clearest path, Act quickly, then loop again. Load when the situation is changing faster than the current plan, when a competitive response is needed, when the user is stuck in analysis paralysis in a dynamic environment, when shipping under uncertainty, or when deep-thinking diagnoses a fast-moving / competitive frame. Triggers on "what should we do right now", "the situation is changing", "how do we respond to this", "competitive response", "we need to move fast", or "we're stuck deciding". Based on John Boyd's OODA loop — Observe, Orient, Decide, Act — adapted for product and business contexts (OODA Canvas 2026).

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Gracefully retire a skill that is redundant, superseded, or no longer earning its place in the context window. Load when improve-skills finds a skill scoring 0-5/14 AND research confirms the domain is now handled natively by current models, when two skills have overlapping triggers and one subsumes the other, when the user asks to remove a skill, retire a skill, delete a skill, or clean up redundant skills, or when validate-skills flags a skill as a duplicate trigger risk. Handles removal cleanly: updates all callers, removes from AGENTS.md, updates README, and archives rather than deletes so the skill can be recovered if needed.

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Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.

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Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations.

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This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI 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。

Extract text, tables, and structured data from documents using prebuilt and custom models.

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。

Optimal ViewModel and Wizard creation patterns for Avalonia using Zafiro and ReactiveUI.

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。

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

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。