「consistency」の検索結果

Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

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Designs social-platform-native visuals (Feed, Story, Reel, Carousel formats) for Instagram, TikTok, Facebook, Pinterest, RedNote, LinkedIn, X/Twitter and other social platforms — with platform-correct dimensions, safe zones, industry visual DNA, engagement hooks, and cross-slide consistency. Use when the user says "social post", "Instagram post", "IG post", "TikTok post", "Facebook post", "Pinterest pin", "social carousel", "feed post", "story post", "make a post", "design a social visual", "社媒帖", "做一个社媒图", or any underspecified social-media visual request. Routed from `product-shots` for the organic-social branch (non-ad).

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Designs Amazon A+ Content (formerly Enhanced Brand Content) — the 8 module suite that appears below the standard product carousel for Brand Registered sellers. Covers Hero Banner (21:9 / 2388×1024), Pain Points / Selling Points / Technology / Data / How-to-Use / Variants (3:2 / 1536×1024), and Endorsement (21:9), with mobile safe-area rule, 30pt text floor, and cross-module consistency anchored to the main image URL. Use when the user says "A+ page", "A+ content", "Amazon A+", "Brand Content", "Enhanced Brand Content", "亚马逊详情页", "A+ 详情页", "21:9 banner", "Hero Banner", "Amazon detail page module", or any request for the brand-registered detail-page module suite. For the 1:1 main image and carousel secondary images, see the `product-shots-main-image` skill.

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Generates 9 consistent multi-angle fashion-editorial portraits from a single reference image, with locked identity (face/skin/eyes), preserved hairstyle structure, faithful outfit/accessories, and a unified photography style across all frames. Use when the user says "multi-angle", "multi-angle shots", "九连拍", "多角度九连拍", "9-angle portraits", "fashion lookbook", "model consistency series", "consistent portraits from one photo", "generate 9 angles of this model", or "e-commerce model multi-angle pack". Part of the product-shots ecosystem for cross-border e-commerce apparel and accessory listings.

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Designs Amazon-compliant main product images and the 7 secondary image types (Infographic / Multi-angle / Detail Shot / Lifestyle / Variants / What's in Box / Size Reference) — with platform-mandatory main-image rules, conversion-rate-tuned secondary types per product category, and multi-image consistency anchored to the main image URL. Use when the user says "Amazon main image", "亚马逊主图", "product main image", "white background product image", "Amazon listing image", "亚马逊副图", "secondary images", "product carousel images", or any request for the 1:1 product image suite that lives on the Amazon detail page carousel. For A+ Content / detail-page modules (21:9 Hero Banner and 3:2 module layouts), see the `product-shots-detail-page` skill.

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Use when auditing self-media videos, scripts, covers, subtitles, voiceover, product links, account copy, comments, articles, or publishing packages for platform violation risk; especially before final delivery or publishing after video production or clipping. Covers e-commerce product consistency, Qianchuan material quality, regulated qualifications, efficacy claims, prices, gifts, activities, and pre-selection risk across WeChat, Douyin, Kuaishou, Bilibili, Xiaohongshu, TikTok, and related platforms.

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Designs or reviews relational, document, key-value, graph, or search data models with ownership, invariants, access paths, migrations, consistency, retention, privacy, and recovery behavior. Use for schema changes, new data stores, indexes, migrations, persistence boundaries, or query-driven design. Not for a query-only bug without a model change or for API contract design.

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Designs runtime topology in a chosen solution. Use when choosing monolith vs services, cells, consistency, ADRs. Not for cross-system design (software-solution-architecture).

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Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document reads.

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Reviews a game design document for completeness, internal consistency, implementability, and adherence to project design standards. Run this before handing a design document to programmers.

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Holistic cross-GDD consistency and game design review. Reads all system GDDs simultaneously and checks for contradictions between them, stale references, ownership conflicts, formula incompatibilities, and game design theory violations (dominant strategies, economic imbalance, cognitive overload, pillar drift). Run after all MVP GDDs are written, before architecture begins.

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Analyzing pre-change impact across vertical (dependency chains, files) and horizontal (pattern consistency, naming) dimensions. Use to estimate blast radius before a refactor. No code.

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Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them. (gstack)

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Reviews PRs that add or modify Agent Skills, checking structural validity, design quality, and marketplace consistency. Use when reviewing skill file changes, auditing SKILL.md quality, or running automated skill PR reviews.

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Audit existing brand assets and usage — find inconsistencies, off-brand applications, and gaps. Use when asked to "audit our brand assets", "find off-brand usage", or "check brand consistency".

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Expert 5-dimension design critique — philosophical, scored, actionable. Use when asked to "critique this design", "expert review", "score my design", "how does this look", "is this good design", "design feedback", or "review this UI". Different from /form-audit (which is technical QA for consistency/compliance) — form-critique evaluates design as a craft object: philosophy, hierarchy, execution, function, and innovation each scored 0–10 with a punch list. Add "as a report" or "give me a visual report" to produce an HTML file with SVG radar chart, evidence cards, and Keep/Fix/Quick-wins action lists — useful for design reviews and stakeholder presentations instead of CLI output.

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Design an icon system spec or audit existing icons for consistency and accessibility. Use when asked to "design an icon system", "audit our icons", or "are our icons consistent".

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Pipeline from a URL or DESIGN.md to tokens, decisions and a UI spec. Use when building UI for an existing brand, auditing a design system or fixing visual inconsistency.

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。

Shape language across type, radius and icons carries brand personality. Use when starting a design system, picking icons or radius tokens, or reviewing visual consistency.

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Choose a layout paradigm (feed, board, table, canvas, master-detail, dashboard) and reuse it across screens. Includes a landing page narrative framework. Use when structuring a screen, page template or landing page.

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Buttons, inputs, badges and pills share radius, colour logic, shadow, border and spacing. Use when building or reviewing a component library.

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Service-oriented and microservices architecture patterns for cloud-scale systems. Covers service boundaries, API versioning and backward compatibility, eventual consistency at the API layer, idempotency keys, saga and choreography patterns, bulkheads, circuit breakers, service meshes, and the Vogels "you build it, you run it" operating principle. Use when designing service decomposition, API contracts, inter-service communication, or reviewing a proposed microservices architecture for coupling and resilience.

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。

OpenStack Swift object storage service. Provides S3-compatible distributed object storage with containers, ACLs, ring architecture, eventual consistency, large object support (SLO/DLO), tempURL for unauthenticated access, object versioning, and object expiry. Use for deploying, configuring, operating, and troubleshooting OpenStack object storage.

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Mathematical verification for physical calculations: unit tracking algebra (exponent maps), PhysicalQuantity pattern for compound units, SI/Imperial mixed-unit handling, Buckingham pi theorem for dimensionless groups, and common engineering dimensionless numbers. Activates for unit verification, dimensional consistency checks, scaling analysis, and calculation validation across all infrastructure domains.

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。