'json schema' 검색 결과

Create output schemas, definitions of result fields and display settings, for an Apify Actor by inspecting its source code. Generate dataset and output files, add key-value storage definitions when needed, and update actor.json to reference them.

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• Creating output schemas for a new Actor
• Updating schemas after output fields change
• Configuring result table columns in Apify Console

Add or repair structured data, labels that help search engines understand page content. The skill selects suitable schema types, prepares JSON-LD code, and provides validation checks while distinguishing supported Google search features from retired ones.

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• Add structured data to product or article pages
• Find and fix errors in existing markup
• Implement breadcrumb structured data

Detect and check structured data, machine-readable descriptions of page content, and generate suitable Schema.org JSON-LD snippets. Get a report of missing fields, syntax errors, and markup issues, plus code prepared for implementation.

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• Check existing structured data for errors
• Generate markup for products, articles, or businesses
• Review deprecated schema types

Create or fix structured data, code that describes page content to search engines. The skill selects suitable schema.org types, prepares JSON-LD markup, and provides placement instructions and validation checks for products, articles, FAQs, and other pages.

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• Add structured data to product pages
• Describe articles and FAQs for search engines
• Fix errors in existing structured data

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

Create and audit JSON-LD, structured data that describes page content to search engines. Check JSON syntax, schema types, and required fields for articles, products, FAQs, and breadcrumbs, then guide validation for enhanced search results.

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• Creating structured data for articles and products
• Checking FAQ and breadcrumb structured data
• Finding JSON syntax errors and missing required fields

Plan a 360-degree or peer feedback process and create empty response templates from confirmed requirements. Produces consistent CSV, SQL, JSON Schema, or Notion property mappings while keeping visibility choices and scoring configuration separate.

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• Plan an anonymous peer feedback process
• Create a CSV template for feedback responses
• Align feedback fields across SQL and JSON Schema

Audit, create, and fix structured data, the labels that help search systems understand web pages. Get JSON-LD code for articles, products, and other page types, plus validation findings and guidance on where to place it.

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• Adding structured data to articles or product pages
• Investigating structured data errors in Search Console
• Auditing existing markup and prioritizing fixes

Define the structure and available components for AI-generated JSON screens or videos. Build generation prompts, process incremental updates, connect values to application state, and validate or repair definitions before passing them to a renderer.

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• Define allowed components for AI-generated screens
• Build prompts for generating or editing JSON definitions
• Assemble definitions from streamed AI responses

Prepare a ChatGPT Apps submission JSON file by inspecting an MCP server, which exposes tools to AI agents. Generate app details, tool behavior justifications, and test cases, then report review findings and missing output schemas.

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• Generate a JSON file for a ChatGPT Apps submission
• Check tool annotations against implementation
• Prepare positive and negative review test cases

Turn work history, education, and skills into a complete resume for Reactive Resume. Gather missing facts through conversation and produce JSON, a structured data format, with the chosen template and layout.

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• Create a new resume through conversation
• Convert work history into Reactive Resume JSON
• Choose a resume template and section order

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Generate model responses in defined formats for data extraction and classification. Use JSON schemas, Pydantic data models, regular expressions, or fixed choices to produce structured results that other programs can parse and validate.

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• Extracting company or product details from text
• Classifying reviews into predefined labels
• Turning profile text into structured fields

Review PostgreSQL code for database design, performance, and security issues. Get improvement suggestions covering JSONB, a binary JSON format, arrays, indexes, custom data types, functions, extensions, and rules that control access to individual rows.

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• Reviewing JSONB and array queries
• Checking table types and constraints
• Improving functions and triggers

When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe," "Product," or "Dataset." For SERP feature types and zero-click patterns, use serp-features. For AI search visibility strategy (not markup), use generative-engine-optimization. For HowTo step sections (placement, copy, vs FAQ), use howto-section-generator.

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Generate Schema.org structured data (JSON-LD) for any page type. Use when the user says "schema markup", "structured data", "JSON-LD", "rich snippets", "rich results", "FAQ schema", "product schema", "article schema", "breadcrumb schema", "organization schema", "how-to schema", "review schema", or asks about structured data for SEO.

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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 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

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

Design and improve prompts for language models, then test their accuracy and consistency. Create reusable templates, JSON output schemas that define response structure, evaluation criteria, and test cases while considering token use and model differences.

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• Designing prompts for a new AI application
• Improving inconsistent model responses
• Defining JSON response formats

Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

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MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".

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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