'json' 검색 결과

Continue analysis after repeated tool failures by embedding known data in Python scripts run through run_shell. Save intermediate results as JSON with timestamps and source metadata, creating records that can be checked and reused in later steps.

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• Continuing analysis when search and execution tools fail
• Comparing prices using competitor data already available
• Saving intermediate results for later review

음성 파일을 OpenAI 전사 API로 보내 결과를 텍스트나 JSON으로 저장합니다. 모델과 언어를 지정하고 화자별로 발언을 구분할 수 있습니다.

• 회의 녹음을 텍스트로 변환합니다
• 인터뷰에서 화자를 구분합니다
• 전사 결과를 JSON으로 저장합니다

Java의 tinystruct로 터미널과 웹에서 함께 호출할 기능을 구현합니다. 시작 스크립트 생성부터 API, 데이터 저장, 테스트까지 개발 절차를 안내합니다.

• 터미널과 웹에서 사용할 기능 구현
• 누락된 시작 스크립트 생성
• JSON API와 데이터 저장 구현

Export recorded AI usage costs and budgets as Prometheus metrics or JSON. Feed external dashboards with totals, model tier breakdowns, session costs, and budget utilization, or send the same data to a webhook endpoint.

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• Refreshing cost metrics for Grafana or Prometheus
• Sending a one-time cost report to a webhook
• Providing budget utilization data for monitoring

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.

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

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 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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

Retrieve GTEx associations between a genetic variant and gene expression in individual tissues. Supply one rsID or genome coordinate, and receive JSON with the resolved variant, association rows, counts, and warnings.

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• Retrieve tissue-specific eQTL associations for an rsID
• Query GTEx using GRCh37 coordinates
• Limit association rows returned as JSON

Retrieve gene data, search downloads, and tissue or cell-line pages from Human Protein Atlas. Get concise summaries by default, with raw JSON or HTML saved to a file only when requested.

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• Look up gene data by Ensembl ID
• Review Human Protein Atlas search results
• Check tissue and cell-line pages

Use ccwf to validate workflow JSON files, preview their steps in a browser, and produce Mermaid flowcharts. Export workflows as skill files for supported agents, or generate the files and launch Claude Code to proceed with execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Previewing a workflow in a browser
• Validating hand-edited or AI-generated workflow JSON
• Sharing workflow steps as Markdown or Mermaid diagrams

Imperative guidance for the System.Text.Json APIs added in .NET 11: the built-in `JsonNamingPolicy.PascalCase` naming policy, and the strongly-typed generic `JsonSerializerOptions.GetTypeInfo` and `JsonSerializerOptions.TryGetTypeInfo` metadata accessors (generic overloads that return a typed `JsonTypeInfo`). USE ONLY when the user is targeting net11.0 or later and needs PascalCase JSON property or dictionary-key names without writing a custom naming policy, a strongly-typed generic `JsonTypeInfo` instead of the non-generic `JsonTypeInfo`, or a no-throw way to probe whether a type's serialization metadata is resolved. DO NOT USE when the target is earlier than net11.0, the requested behavior uses an established pre-net11 naming policy, or the user explicitly selected another JSON library.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

macOS의 Bear에서 노트를 생성하고 읽거나 내용을 추가합니다. grizzly로 태그 검색을 수행하고 노트 내용과 태그 목록을 JSON 형식으로 가져올 수 있습니다.

• 태그가 있는 업무 메모 생성
• 기존 노트에 추가 정보 작성
• 태그로 노트 검색

앱의 실제 표시 위치와 용어를 확인해 JSON 번역 파일을 갱신합니다. 누락된 번역과 원문 변경을 검토하고 기존 구조와 동적 값 자리표시자를 보존합니다.

• 새 화면에 추가한 문구 번역
• 앱의 지원 언어 추가
• 원문 변경 후 기존 번역 검토

Retrieve a single snapshot of recorded AI costs, including total spending, model breakdowns, the highest-cost session, and budget usage. Export JSON for scripts and dashboards, or Markdown for a quick review.

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• Reviewing total spending and budget usage
• Checking costs by model and model tier
• Fetching cost data for a dashboard

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Review PostgreSQL queries and data designs using execution plans, which show how queries run, and targeted index recommendations. Get guidance on JSONB, arrays, full-text search, analytical queries, extensions, and routine database maintenance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow queries and choosing indexes
• Reviewing JSONB searches and full-text search
• Writing running totals and ranking queries

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Create a Google Model Armor template for checking AI prompts and responses. Specify a Google Cloud project, location, and template ID, then use a preset or provide custom settings in JSON.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a template with the jailbreak preset
• Defining template settings in JSON
• Preparing a template for prompt and response checks