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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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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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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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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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通过正则表达式和语法规则约束本地语言模型的输出，生成符合指定格式的JSON、日期和固定分类标签，并用Python构建可重复使用的多阶段生成流程。
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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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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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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定期检查RSS、JSON API和GitHub，与状态文件中已记录的ID比对，只提取新内容。可设置有更新才报告、无更新时保持静默的监控流程。
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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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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通过Gemini CLI单次执行问答、文本生成和文件内容摘要，可指定模型及JSON输出格式，并使用管理扩展、技能、钩子和MCP连接的命令。
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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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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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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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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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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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.
中文简介正在准备中。当前显示英语简介。
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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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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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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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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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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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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