「schema」の検索結果

Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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Scaffolds eval.yaml evaluation specs for skills, custom agents, and redistributable gh-aw workflow packages in the dotnet/skills repository. Use when creating skill or workflow-package tests, writing evaluation stimuli, defining graders and rubrics, sizing an eval for statistical power, or setting up test fixture files. Handles the Vally eval.yaml schema, fixture organization, and overfitting avoidance. Do not use for running or debugging existing evals (use improve-skill-quality) nor for skills authoring (use create-skill).

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Review and optimize application data access that uses Entity Framework Core (EF Core). Use for EF Core performance and correctness reviews of services, repositories, imports, and background jobs: slow or memory-heavy queries, excessive round-trips, hidden SaveChanges, premature materialization, dynamic query construction, result-graph tracking, bulk writes, DbContext lifetime or overlapping operations, search semantics, paging, raw SQL, and model/index coverage. Use whether or not EF Core owns the schema. For EF Core, not Dapper or raw ADO.NET.

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Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

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。

火山引擎 Supabase . 管理 AIDAP Supabase 项目、工作区、数据库、分支、Auth、Realtime、Edge Functions、Storage、API Keys、SQL、Schema 变更、TypeScript 类型生成和 MCP 集成。

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Comprehensive SEO/GEO/AEO analysis toolkit for optimizing content visibility across traditional search engines (Google, Bing), AI platforms (ChatGPT, Perplexity, Claude, Gemini, Grokipedia), answer engines (Google AI Overviews, Bing Copilot, featured snippets), voice assistants (Google Assistant, Siri, Alexa), and social media (Facebook, Twitter, LinkedIn, WhatsApp, Instagram). Analyzes HTML/Markdown/JSX files for metadata completeness, schema markup, keyword optimization, entity extraction, and generates multi-format audit reports with platform-specific recommendations.

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Use when a database schema or data-meaning change needs Parallel Change, including expand, migrate, and contract sequencing for column renames, type or data reinterpretation, large-table backfills, relationship-table changes, enum/status transitions, timezone/default changes, and index or uniqueness changes. This should trigger before framework-specific Flyway implementation guidance when deciding whether a migration needs a compatibility window or whether a simpler migration is sufficient. Part of Plinth Toolkit

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Use when you need to review a plan, OpenSpec change, specification, or implementation proposal for breaking-change risk across commands, skills, generated outputs, XML sources, README/docs, tests, CI, APIs, schemas, configuration, data, migration, and release guidance. This should trigger for requests such as Review breaking changes in this spec; Check compatibility risks; Avoid breaking changes in this OpenSpec change; Review migration impact before release; Assess command and skill compatibility. Part of Plinth Toolkit

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Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.

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。

Execute read-only SQL queries against multiple Microsoft SQL Server databases. Use when: (1) querying MSSQL/SQL Server databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.

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。

Execute read-only SQL queries against multiple MySQL databases. Use when: (1) querying MySQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.

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Create components using ng-alain (@delon/abc) and ng-zorro-antd UI libraries. Use this skill when building enterprise UI features with ST (Simple Table), SF (Schema Form), ACL (Access Control), PageHeader, ReuseTab, and other @delon components. Ensures proper integration with ng-alain architecture, theming system, responsive layouts, and accessibility standards while following Angular 20 patterns.

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Patterns for SQLite databases in Python projects - state management, caching, and async operations. Triggers on: sqlite, sqlite3, aiosqlite, local database, database schema, migration, wal mode.

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Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.

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Tool skill — the *first move* in any LM-debugging session: dump the actual rendered prompt the framework sent to the model, before changing anything else. Activate when an LM call produced an unexpected output (wrong answer, schema violation, refusal, truncation, cost spike, latency spike, infinite loop, "model got dumber after upgrade"). The skill enforces a 30-second inspect step BEFORE any prompt edit, model swap, retry, or temperature tweak. Cross-framework cheat sheet: DSPy `inspect_history`, LangGraph `get_state_history`, CrewAI `step_callback`, LangChain `set_debug`/`set_verbose`, Aider `/diff`+`--verbose`, raw OpenAI/Anthropic via `OPENAI_LOG=debug`/`ANTHROPIC_LOG=debug` or HTTPX event hooks. Do NOT activate for first-time prompt authoring, exploratory prompt design, or non-LM bugs.

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Flags irreversible decisions before commit. Use for data models, infra, auth boundaries, API contracts, event schemas, CI/CD.

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Kalshi exchange mechanics — RSA-PSS auth, order schema, YES/NO orderbook convention, WebSocket, and endpoint surface. Market-type-agnostic shared layer for all Kalshi skills.

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。

Creates reusable Convex components with defineComponent, a clean client wrapper, their own schema, and an npm publish setup. Use when extracting a feature into a package, building something for the Convex component directory, or when a component's functions are not showing up in the parent app.

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Consult PostgreSQL's parser and grammar (gram.y) to understand SQL syntax, DDL statement structure, and parsing rules when implementing pgschema features

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 a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

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Generate structured planning documentation for web projects with context-safe phases, verification criteria, and exit conditions. Creates IMPLEMENTATION_PHASES.md plus conditional docs (DATABASE_SCHEMA, API_ENDPOINTS, UI_COMPONENTS, CRITICAL_WORKFLOWS). Use when: starting new Cloudflare Workers/React projects, adding major features to existing apps, breaking large work into manageable phases, or need verified planning before coding begins.

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 guide to PostgreSQL migrations - common errors, generated columns, full-text search, indexes, idempotent migrations, and best practices for database schema changes

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This skill should be used when user asks to "query MongoDB", "show database collections", "get collection schema", "list MongoDB databases", "search records in MongoDB", or "check database indexes".

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 or edit images using AI models (FLUX, Gemini). Use for scientific illustrations, diagrams, schematics, infographics, concept visualizations, and artistic images. Supports image editing to modify existing images (change colors, add/remove elements, style transfer). Useful for figures, posters, and visual explanations.

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。