Results for “json-generation”

Generates structured VGL JSON prompts for Bria's FIBO image generation models, covering text-to-image, editing, inpainting, outpainting, and captioning with a deterministic schema.

Generate A2UI 0.8 protocol compliant UI code for agent-driven interfaces, including JSONL messages for surface updates, data model updates, and rendering.

封装阶段3的Copy Spec为可批量执行的提示词包，生成JSONL请求并调用APIMart API出图。

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

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

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

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

Generates structured VGL JSON for Bria FIBO models, giving deterministic control over objects, lighting, camera, composition, and style instead of natural language prompts.

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Generate validated JSON-LD structured data markup for any page type

Generates 16-step beat sequences from style prompts, producing JSON, MIDI, WAV, and MP3 files.

Generate structured data markup (JSON-LD) for rich results, AI search visibility, and enhanced SERP features. Supports Article, FAQ, HowTo, Product, Review, LocalBusiness, Event, BreadcrumbList, Person, Organization, and more. Use when the user needs schema markup, structured data, rich snippets, JSON-LD, or wants to improve search appearance.

Generate realistic mock data from descriptions, types, or schemas. Use when you need test data fast.

Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.

Generate data-driven user personas for UX research and product design. Usage: /persona generate [options]

Generate process JS files following Babysitter SDK patterns including task definitions, quality gates, breakpoints, and proper io configuration.

Use this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script).

Generate a NullPointer Studio styled PDF penetration test report from findings.json, producing a professional dark-themed PDF with executive summary, risk dashboard, per-finding cards, and remediation summary.

Detect, validate, and generate Schema.org structured data in JSON-LD format for SEO and rich results.

Working effectively with JSON data structures.

jq - JSON Querying and Transformation

Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections for visual canvases, mind maps, and flowcharts.

Creates a machine-readable phased JSON plan bundle from a spec or feature description, producing a compact master manifest plus per-phase step files for resumable execution.

Multi-channel personalized message generation with 3 tiers of personalization

Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.