「entity」の検索結果

AI citation readiness audit as part of SEO, covering classic Google search and AI search surfaces together. Use whenever the user wants their content to rank or be cited in ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com, Google AI Overviews, or Google AI Mode. AI citation optimization audit scoring blog posts for major answer surfaces. Evaluates passage-level citability, Q&A formatting, entity clarity, structured data, and AI crawler accessibility. Generates citation capsules and a 0-100 AI Citation Readiness score. Use when user says "geo", "ai citation", "ai optimization", "citation audit", "aeo", "perplexity optimization", "chatgpt citation".

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Generate JSON-LD for ONE blog post (BlogPosting with Person, Organization, BreadcrumbList, ImageObject, VideoObject; FAQPage only as optional entity markup) in a single @graph, and check it against Google's current structured data support. Use when the user says "generate schema", "json-ld for this post", "blog schema". Not for auditing schema across a whole site or other schema types (claude-seo seo-schema), not for product pages, and not a promise of rich results: FAQ rich results ended 2026-05-07.

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Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.

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Defines and applies visual brand — logo usage, palette, typography, imagery direction, and the guidelines that keep expression consistent across product and marketing surfaces. Use this to establish a brand from scratch, audit whether an existing brand is being applied consistently, build brand guidelines, or decide how a brand should express itself in an unfamiliar format.

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Executes joiner, mover and leaver processes — provisioning, group membership, access changes on role change, and complete deprovisioning. Use this to set up or fix joiner-mover-leaver, deprovision someone completely, clean up accumulated access, audit group membership, or find accounts that outlived their owners.

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Administers the cloud the company runs on rather than the one it sells — tenant and subscription structure, the SaaS estate and who owns each app, identity as the real perimeter, cloud spend that arrives as a surprise, and what the provider does not do for you. Use this to structure subscriptions or tenants, get control of sprawling SaaS, cut a cloud bill, work out who owns an application nobody admits to buying, or decide what corporate workloads belong in cloud at all.

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Maintains the corporate record and the governance machinery — entity records, board and committee support, resolutions and minutes, delegations of authority, insurance, and business continuity. Use this to prepare board or committee materials, record a decision that needs to be minuted, set or check approval authority, review insurance and continuity coverage, establish a retention policy, or work out where a responsibility belongs when it falls between functions.

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Monsters, dragons, world reveals. Each entity gets its own definition block, the shots are cut by timecode, and scale is bought with reference objects and low angles rather than words like massive. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (Epic fantasy and sci-fi spectacle; 中文触发词: 奇幻科幻大场面, 奇幻, 科幻, 巨龙, 怪兽, 史诗), or asks how published cases of this type were prompted. Read references/cases.md before drafting.

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Use this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.

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A toolkit for fuzzy string matching and data reconciliation. Useful for matching entity names (companies, people) across different datasets where spelling variations, typos, or formatting differences exist.

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。

spaCy NER model training and entity extraction for conversational AI

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Rasa NLU pipeline configuration and training for intent and entity extraction

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Manage agent sessions including initialization, handoffs, revival (seance), and persistent identity for Polecats and Crew agents.

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Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI

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Master C#/.NET backend development patterns for building robust APIs, MCP servers, and enterprise applications. Covers async/await, dependency injection, Entity Framework Core, Dapper, configuration, caching, and testing with xUnit. Use when developing .NET backends, reviewing C# code, or designing API architectures.

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Expert .NET backend architect specializing in C#, ASP.NET Core, Entity Framework, Dapper, and enterprise application patterns. Masters async/await, dependency injection, caching strategies, and performance optimization. Use PROACTIVELY for .NET API development, code review, or architecture decisions.

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Generate, visually expand, optimize, diagnose, and route structured AI image prompts for adult female portraits. Use for lifestyle, curve-focused, fashion, oriental, fantasy, realism, beauty, CCD, low-key cinematic, studio, sport, travel, and e-commerce portrait requests; onboarding or usage help; parameter recommendations; prompt rewrites; image-to-prompt preparation; direct image generation; or identity- and product-preserving edits from authorized reference images. Preserve explicit user parameters and locked reference subjects while creating a coherent photography-directed scene.

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Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.

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Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.

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Audit Git configuration with one read-only baseline command, then propose confirmed, non-destructive fixes for identity, editor, and diff and merge tooling, with signing and safe.directory help only on request. Use when setting up or checking Git on a workstation.

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Author and validate durable data and ML feasibility studies using the Feasibility Study Interchange Profile, constrained YAML authority, UUID URN identity, lifecycle lineage, and evidence traceability. Use when assessing whether available data and technical evidence support a proposed outcome.

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Create and enrich durable data catalogs using the native DS_CATALOG_V1 Markdown contract, declared entity relationships, privacy citation fields, and stable relationship IDs. Use when inventorying engagement data, recording semantic relationships, or preparing a catalog for ERD rendering.

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Design Thinking coaching foundation knowledge: coach identity and philosophy, quality and fidelity constraints, method sequencing, coaching state schema, and the canonical deck workflow

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Structure a Bevy app around its Entity Component System: build the App with plugins, define Component/Resource types, write systems with Query/Res/Commands, filter and order systems, and use the Time resource for frame-rate-independent motion. Use when building or debugging a Bevy game in Rust — when the user mentions Bevy, ECS, App::new, add_systems, Query, Commands, components/systems, or a Cargo.toml depending on bevy.

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。