'schema' 검색 결과

Analyze KiCad projects and PDF schematics: schematics, PCB layouts, Gerbers, footprints, symbols, netlists, and design rules. Reviews designs for bugs, traces nets, cross-references schematic to PCB, extracts BOM data, checks DRC/ERC, DFM, power trees, and regulator circuits. Every finding carries a confidence label and evidence source with trust_summary rollup. Analyzes PDF schematics from dev boards, reference designs, eval kits, and datasheets. Supports KiCad 5–10. Use whenever the user mentions .kicad_sch, .kicad_pcb, .kicad_pro, PCB design review, schematic analysis, PDF schematics, reference designs, Gerber files, DRC/ERC, netlist issues, BOM extraction, signal tracing, power budget, DFM, or wants to understand, debug, compare, or review any hardware design. Also for "check my board", "review before fab", "what's wrong with my schematic", "is this ready to order", "check my power supply", "verify this circuit", OSHWA certification readiness, or any electronics/PCB design question.

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Analyze KiCad projects and PDF schematics: schematics, PCB layouts, Gerbers, footprints, symbols, netlists, and design rules. Reviews designs for bugs, traces nets, cross-references schematic to PCB, extracts BOM data, checks DRC/ERC, DFM, power trees, and regulator circuits. Every finding carries a confidence label and evidence source with trust_summary rollup. Analyzes PDF schematics from dev boards, reference designs, eval kits, and datasheets. Supports KiCad 5–10. Use whenever the user mentions .kicad_sch, .kicad_pcb, .kicad_pro, PCB design review, schematic analysis, PDF schematics, reference designs, Gerber files, DRC/ERC, netlist issues, BOM extraction, signal tracing, power budget, DFM, or wants to understand, debug, compare, or review any hardware design. Also for "check my board", "review before fab", "what's wrong with my schematic", "is this ready to order", "check my power supply", "verify this circuit", OSHWA certification readiness, or any electronics/PCB design question.

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Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.

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• Recording agent evaluation results in a shared format
• Comparing a candidate agent with a baseline
• Tracking actual cost per solved task

Design tools that AI agents can understand and call, covering input schemas, clear descriptions, validation, and error handling. The guidance includes JSON Schema, a format for defining inputs, and MCP, a protocol for connecting agent tools.

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• Design inputs for a new agent tool
• Improve tool descriptions that guide model choices
• Review validation and error handling

Design tools that AI agents can understand and call correctly. Covers JSON Schema, a format for defining inputs, along with clear descriptions, input examples, validation, and error responses that help agents recover from failures.

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• Designing inputs for an AI agent tool
• Improving tool descriptions and input examples
• Reviewing validation and error responses

Audit website structured data, the markup that describes businesses and content to machines. Detect errors and missing details, then generate JSON-LD code and a report with recommendations for improving AI readability.

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• Audit structured data on a company website
• Generate markup for products, articles, or local businesses
• Review links connecting official profiles

Handles the full DMS Schema Conversion lifecycle including creating migration projects, converting database schemas to a target engine, running compatibility assessments, navigating metadata trees, exporting converted DDL to S3, applying schema changes to a target database, and converting SQL statements between database engines. Applies when migrating database schemas between heterogeneous engines using AWS DMS Schema Conversion.

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Generate complete JSON-LD schema markup for blog posts with Article/BlogPosting, Person, Organization, BreadcrumbList, ImageObject, and optional FAQPage. Validates against Google requirements and warns about deprecated types. Use when user says "schema", "blog schema", "json-ld", "structured data", "schema markup", "generate schema".

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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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显式 schema 校验工具。读取结构化数据与用户提供的 schema，检查字段存在性、字段类型、枚举、范围、长度、正则格式、日期格式、表级行数与缺失率约束，并输出校验报告。 当用户提到 schema 校验、结构校验、模式校验、字段存在性检查、字段类型检查、枚举校验、范围校验、格式校验、表头结构校验等需求时使用此 skill。 即使用户没有明确说出"schema_validator"，只要任务是在检查结构化数据是否符合明确 schema 规则，就应该使用此 skill。 不负责数据清洗、去重、业务合理性判断、跨字段一致性、参照完整性、分布异常分析或数据转换。

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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

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World-class database schema design - data modeling, migrations, relationships, and the battle scars from scaling databases that store billions of rowsUse when "database schema, data model, migration, prisma schema, drizzle schema, create table, add column, foreign key, primary key, uuid, auto increment, soft delete, normalization, denormalization, one to many, many to many, junction table, polymorphic, enum type, index strategy, database, schema, migration, data-model, prisma, drizzle, typeorm, postgresql, mysql, sqlite" mentioned.

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementations. The LLM never sees your code - it only sees the schema and description. Use when "agent tool, function calling, tool schema, tool design, mcp server, mcp tool, tool use, build tool for agent, define function, input_schema, tool_use, tool_result, agents, tools, function-calling, mcp, json-schema, anthropic, openai, llm-tools" mentioned.

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Generate complete JSON-LD schema markup for blog posts including BlogPosting, Person, Organization, BreadcrumbList, FAQPage, and ImageObject. Validates against Google requirements and warns about deprecated types. Use when user says "schema", "blog schema", "json-ld", "structured data", "schema markup", "generate schema".

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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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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

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Zod 4 — TypeScript-first schema validation with static type inference. Use when writing Zod schemas, validating data, defining types with Zod, parsing input, creating form validation schemas, defining API request/response schemas, working with z.object, z.string, z.number, z.enum, z.array, z.union, z.discriminatedUnion, z.file, z.jwt, z.email, z.uuid, z.url, z.codec, z.toJSONSchema, z.fromJSONSchema, z.int, z.stringbool, z.templateLiteral, z.record, z.partialRecord, or any other Zod API. Also use when migrating from Zod 3 to Zod 4, or when the user's package.json shows zod@^4. CRITICAL: Always use Zod 4 APIs. Never use deprecated Zod 3 patterns unless user explicitly requests Zod 3 compatibility.

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Design and review PostgreSQL database schemas by choosing suitable column types, keys, constraints, and indexes. It also guides decisions about large-table partitioning, flexible JSONB data, and schema changes on a live database without downtime.

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• Designing or reviewing a new PostgreSQL schema
• Choosing data types, constraints, and indexes
• Planning partitions for a large table

Review and shorten tool instructions for AI agents by testing overlap with parameter schemas, the definitions of accepted inputs. It also guides writing clear usage rules while preserving warnings backed by past failures.

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• Audit tool instructions for repeated parameter explanations
• Test schema overlap before removing prompt lines
• Write tool selection rules and practical examples

Define custom entities and relationships in cognee, a memory system for AI applications. This skill guides model design, identity matching, searchable fields, and troubleshooting when entities are duplicated or relationships are missing.

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• Designing custom entities and relationships
• Preventing duplicate entities across imports
• Investigating missing search results or relationships

Adapt a knowledge collection's schema, the rules defining page types and relationships. Review untyped pages, propose new types, update classification settings, and apply those types to existing pages after previewing the changes and checking consistency.

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• Adding page types for researchers and papers
• Classifying existing pages that have no type
• Proposing new types from stored content

Develop TypeScript services and workflows with Effect v4 by checking its source and existing project examples. Guides data schemas, typed errors, explicit dependencies, and tests that follow the project's established patterns.

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• Implementing services with Effect v4
• Defining data schemas and typed errors
• Organizing dependencies and HTTP handlers

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

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• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Build Apache Beam connectors that read and write external data. This guide covers Java implementation, schema support for structured records, access from Python and Go, Managed API integration, and testing.

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• Add a connector for an external data system
• Make a Java connector available to Python and Go
• Integrate a connector with Beam YAML and the Managed API