Results for “structured output”

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Construa agentes de IA prontos para produção com PydanticAI — type-safe tool use, structured outputs, dependency injection, e suporte multi-modelo.

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给定 PMID/PMCID 取结构化全文（NLM BioC XML/JSON）。封装 BioC PubMed（标题+摘要）和 BioC PMC OA（开放获取全文）端点，输出按章节切分的段落、表格上下文、RAG 友好 chunk，适合大批量入向量库。Fetches structured fulltext from NLM BioC API for PMID/PMCID, outputs section-segmented paragraphs and RAG-ready chunks. 触发词：全文抽取 / BioC / RAG 入库 / 文献全文 / PMC 开放获取 / structured fulltext / 段落抽取 / 表格抽取 / chunking / fulltext to vector store / paragraph extraction.

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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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Build autonomous AI agents with Claude Agent SDK. Structured outputs guarantee JSON schema validation, with plugins system and hooks for event-driven workflows. Prevents 14 documented errors. Use when: building coding agents, SRE systems, security auditors, or troubleshooting CLI not found, structured output validation, session forking errors, MCP config issues, subagent cleanup.

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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.

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

Integrate single-page extraction into an application using Firecrawl scrape. Retrieve Markdown, HTML, links, screenshots, metadata, or structured data from a known URL, and configure how recently cached content may be reused.

• Preparing known web pages for summarization
• Enriching records from company or product pages
• Extracting pricing, changelogs, or documentation

Turn document analysis into a structured PPT presentation with images generated in a chosen style. The skill uses the NanoBanana stack to combine document-based content preparation and image generation within a presentation creation workflow.

• Turning a document into a PPT presentation
• Creating slides with styled, generated images
• Combining document analysis and presentation creation

Audit or create a dedicated About page that explains a website’s purpose and identifies its people or organization. Review the rendered output and supporting information, including expertise and contact details, to find gaps.

• Reviewing a website’s About page
• Creating a page about the organization and its mission
• Checking how operator information appears in rendered output

Build and improve Python command interfaces for GUI applications with OpenClaw. The skill guides source analysis, command design, testing, and packaging, including interactive sessions and structured JSON output for agents.

• Build a command interface for a GUI application
• Add missing commands to an existing interface
• Test workflows against the actual application

Set up and inspect an Ethereum node through a command-line interface. Install execution and consensus clients, configure validator metadata, expose RPC access for external requests, and retrieve structured health reports for agent workflows.

• Set up a new Ethereum node host
• Install chosen execution and consensus clients
• Configure RPC access through Nginx or Caddy

Build and debug Apify Actors, cloud programs for web scraping, automation, and data processing. Get guidance on language templates, input and output schemas, runtime code, local testing, and deployment, with practices for handling credentials and external data.

• Creating an Actor for web scraping
• Defining Actor inputs and structured outputs
• Debugging an Actor with local test data

Plan structured adversarial tests for AI applications to assess safety bypasses, data leaks, and unauthorized tool use. Organize test prompts, record findings, and prioritize fixes using reporting guidance and an adaptable testing template.

• Assess an AI feature before release
• Compare security behavior of candidate models
• Retest after a prompt injection or jailbreak incident

Explore a web application as a user to find reproducible bugs and usability problems. Produce a structured report with reproduction steps, screenshots, and videos for interactive issues, giving responsible teams concrete evidence to investigate.

• Checking a web app's quality before release
• Testing forms and common user workflows
• Finding confusing interfaces and visual problems

Extract identity, ownership, control, and source-of-funds details from investor or client onboarding documents into a structured JSON record. Inventory the submitted documents and flag obvious missing or expired items before passing the information to KYC screening rules.

• Organize investor onboarding documents
• Extract beneficial owners and controlling persons
• Check for missing or expired documents

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.

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

Guides language model training with GRPO in TRL, comparing several responses and rewarding better results. Covers dataset preparation, scoring rules for correctness and format, training configuration, monitoring, and saving models for later use.

• Training models to follow JSON or XML formats
• Designing rewards for math and coding tasks
• Adjusting training to available GPU memory

Turn a product description into a landing page with headlines, feature sections, pricing, FAQs, and calls to action. Receive Next.js/React components styled with Tailwind CSS, plus search metadata, structured data, and alternative copy.

• Creating a landing page for a new product
• Building a campaign or lead capture page
• Comparing headline and page variants

Guide an AI agent through stalled work using structured investigation, different hypotheses, and checks backed by actual output. The skill combines systematic problem solving with forceful Japanese workplace rhetoric and applies across coding, research, writing, and planning.

• Changing approach after repeated failures
• Checking assumptions against original sources
• Verifying results before reporting completion

Turn completed research, analysis, reports, or plans from the current session into a self-contained HTML artifact. A renderer controls layout, typography, and color, using structured content and a choice of two visual styles.

• Turn research findings into an HTML artifact
• Format a cost analysis with tables and callouts
• Present investigation findings as a designed document

Evaluate AI agent outputs with code checks and model-based grading. Run repeated trials to measure reliability, compare prompts or models, and check whether changes reduce quality, with saved transcripts for reviewing the results.

• Comparing two prompts on the same test cases
• Checking quality after agent configuration changes
• Turning real failures into repeatable evaluation cases