「schema」の検索結果

Scores shortlisted accounting packages against 57 evidence-backed fields, emitted as CSV, SQL, JSON Schema or Notion on request. Use for choosing accounting software.

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Automate Addresszen tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Automate Adobe tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Automate Abstract tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.

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Admin account register: system, main and backup admin, seats, plan, 2FA, shared logins and access-review dates, as CSV, SQL, JSON Schema or Notion on request. Use for access reviews.

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Orchestrate comprehensive SEO work across technical, content, schema, backlinks, local, ecommerce, programmatic SEO and AI-search/GEO. Use for full-site audits, SEO plans, or multi-specialist SEO investigations.

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When the user wants to plan, map, or restructure their website's page hierarchy, navigation, URL structure, or internal linking. Also use when the user mentions "sitemap," "site map," "visual sitemap," "site structure," "page hierarchy," "information architecture," "IA," "navigation design," "URL structure," "breadcrumbs," "internal linking strategy," "website planning," "what pages do I need," "how should I organize my site," or "site navigation." Use this whenever someone is planning what pages a website should have and how they connect. NOT for XML sitemaps (that's technical SEO — see seo-audit). For SEO audits, see seo-audit. For structured data, see schema.

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Design, validate and implement structured data that accurately represents visible page content and current search-engine eligibility rules.

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Use when the user asks to "optimize entity presence", reconcile an entity identity, or update canonical Knowledge Graph facts; audits and maintains machine-facing identity, sameAs, schema, disambiguation, and AI-recognition evidence through the entities registry. Not for page-level AI-citation readiness - use geo-content-optimizer; not for human-facing brand canon - use narrative-registry. 实体注册/知识图谱

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Ensure OpenAPI specs define consistent request/response schemas, error model, auth, pagination, and backward compatibility.

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Ensure data model/DB designs cover constraints, integrity, indexes, migrations, rollback, and operational impacts.

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Ensure event-driven designs specify delivery guarantees, ordering, idempotency, schema evolution, and replay/backfill strategy.

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スキーマ/データ移行の安全性を framework 非依存で審査する。破壊的スキーマ変更・ロック誘発・backfill・ロールバック可逆性・expand-contract の段階適用を、prisma / typeorm / Rails / Django / Alembic / 生 SQL などに横断適用する。

Select a current video model and endpoint against duration, references, motion, audio, ratio, quality, cost, and continuity requirements using live schemas.

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Route a broad video brief into the smallest useful production workflow, choose live model schemas, and return an evidence-backed execution plan.

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Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.

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MCP server documentation conventions for high-quality generated skills. Use when writing tool descriptions, `_meta` annotations, or input schemas for MCP extraction.

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Create a capture for ANY Estuary connector using dynamic schema discovery. Use when the user wants to capture from a source that doesn't have a dedicated skill (e.g., Kafka, Salesforce, HubSpot, Stripe, S3, GCS, Kinesis, or any of the 148+ connectors). Use when user says "capture from <source>", "stream from <source>", "connect <source> to Estuary", or "set up <source> capture" and no connector-specific skill exists.

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Override default column types in materializations using castToString or custom DDL. Use when column type needs different precision (e.g., NUMERIC(22,6)), VARCHAR length is wrong, need JSONB instead of JSON, integers lose precision, or need to force a string representation. Use when user says "change column type", "wrong precision", "VARCHAR too short", "integer truncated", "cast to string", or "custom DDL".

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Add projections to collections to rename fields or map nested JSON paths to flat column names. Use when renaming fields for destination conventions, flattening nested paths to columns, or creating field aliases. Use when user says "rename field", "flatten nested JSON", "map JSON path to column", "field alias", or "projection".

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Handle NULL values and set default values for missing fields in collections and materializations. Use when fields need defaults, nullable primary key errors occur, or source data has gaps. Use when user says "NULL values", "default value", "missing field", "nullable primary key error", or "fill in missing data".

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Control which fields are included or excluded in materializations using depth modes and field overrides. Use when reducing table width, including nested fields, excluding PII, or optimizing performance. Use when user says "exclude fields", "include nested field", "too many columns", "select specific fields", or "field depth".

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Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

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