'json output' 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

Extract fields, filter records, and reshape JSON from API responses, configuration files, or logs using Crush’s built-in jq. Get help writing and debugging transformations, constructing new JSON, and formatting output for other commands.

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• Extract selected fields from API responses
• Filter JSON arrays by conditions
• Reshape configuration data or construct JSON

Generate PDF documents from JSON definitions with headings, tables, images, and page numbers. Use file, in-memory, or streaming output, and extend document layouts with custom components and externally managed application state.

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• Generate invoice PDFs from JSON definitions
• Build documents containing tables and images
• Stream generated PDFs through an HTTP response

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 JSON or CSV data into Markdown notes using Knap templates. Apply formatting rules and conditional sections, generate files in batches, or shape web content extracted by Defuddle into a consistent note format.

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• Generate Markdown notes from JSON or CSV data
• Create multiple notes with a shared template
• Format Defuddle output into a note

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.

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• Extracting errors from large logs
• Summarizing test failures and build warnings
• Inspecting JSON and CSV data

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

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• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

Gemini CLI로 질문 답변, 글 생성, 파일 내용 요약을 단일 요청으로 수행합니다. 모델과 JSON 출력 형식을 지정하고 확장 기능 등의 관리 명령도 다룹니다.

• 메모 파일 요약
• 단일 요청으로 글 생성
• JSON 형식으로 답변 받기

Convert existing scripts, CLI tools, or Crawlee projects into reusable Apify Actors, programs packaged as Docker images. Define JSON inputs and outputs, integrate the SDK, test locally, and deploy to Apify.

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• Turning an existing script or CLI tool into an Actor
• Migrating a Crawlee project to Apify
• Defining JSON inputs and structured outputs

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

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

Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.

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• Extract information into JSON with a defined schema
• Build agents that call tools
• Reuse shared prompts across chat requests

Helps integrate YAML output into json-render, turning incoming text into incremental UI updates. Covers prompt generation, streaming parsing, and targeted edits to existing UI specifications through merge, patch, or diff formats.

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• Render UI updates as YAML arrives
• Generate prompts for YAML output
• Apply partial edits to existing UI specifications

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Set up and run kube-bench to check Kubernetes configuration against CIS security benchmarks. Select the appropriate benchmark and cluster components, then export JSON or JUnit results for automated pipelines and troubleshoot skipped checks or incorrect detection.

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• Setting up kube-bench for the first time
• Selecting benchmark versions and scan targets
• Exporting JSON or JUnit results for CI

Control local language model output with patterns and grammar rules to generate structured JSON, formatted dates, and fixed classification labels. The skill also covers reusable Python functions and workflows that combine multiple generation steps.

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• Generate JSON that follows a defined schema
• Keep dates and email addresses in a specified format
• Classify text using a fixed set of labels

Compare two saved cost-summary JSON snapshots to identify changes in total spending, sessions, and model costs. Set limits on spending increases or token growth to flag cost regressions during pull request checks.

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• Comparing spending before and after a pull request
• Finding models responsible for cost changes
• Failing checks when spending exceeds set limits

Convert Simplicité text logs into structured JSON while keeping only the fields needed for troubleshooting. Preserve multiline errors and stack traces, and reduce the amount of log content passed to an AI agent for analysis.

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• Preparing large logs for AI analysis
• Extracting timestamps, severity levels, and messages
• Preserving multiline errors during conversion

Turn structured JSON into editable SVG diagrams for system architectures, workflows, and pipelines. Specify positions, connections, and groups precisely, reproduce the same output under fixed rendering conditions, and convert figures to PDF for papers.

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• Creating architecture diagrams for technical papers
• Illustrating workflows and processing pipelines
• Adjusting diagram layouts through JSON specifications

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

Continue file reading and data processing when read_file or execute_code_sandbox fails. The skill uses run_shell with short Python commands or multiline scripts to inspect files, parse data, and save transformed output.

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• Reading accessible files after read_file errors
• Inspecting JSON settings through Python
• Checking CSV rows after sandbox execution fails

AI 에이전트와 외부 도구를 잇는 MCP 서버를 명령으로 관리합니다. 서버 목록, 도구 입출력 규격, 연결 설정과 인증을 확인하고 도구를 호출합니다.

• MCP 도구와 인수 규격을 확인합니다
• MCP 연결을 설정하고 인증합니다
• JSON 인수로 도구를 호출합니다