152 skills · sorted by relevance
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.
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.
Maximum control over AI image generation — write structured VGL (Visual Generation Language) JSON that explicitly controls every visual attribute. Define exact object placement, lighting direction, camera angle, lens focal length, composition, color scheme, and artistic style as deterministic JSON instead of ambiguous natural language. Use this skill when you need reproducible image generation, precise control over scene composition, or want to convert a natural language image request into a structured JSON schema for Bria FIBO models. Triggers on requests for structured prompts, controllable generation, VGL JSON, deterministic image descriptions, or Bria/FIBO structured_prompt format.
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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.
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.
Create character, scene, and product images from structured descriptions of style, composition, lighting, and color. Guide generation with reference images and aspect ratio settings, using a Python script connected to a supported image generation service.
Build prompts for FLUX image generation and editing, with guidance on model selection, lighting, composition, text, and precise colors. It also covers structured scene descriptions using JSON and editing with multiple reference images.
Comprehensive patterns for building AI-powered code generation tools, code assistants, automated refactoring, code review, and structured output generation using LLMs with function calling and tool use. Use when "code generation, AI code assistant, function calling, structured output, code review AI, automated refactoring, tool use, code completion, agent code, " mentioned.
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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
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Add on-device language model features to iOS 26+ apps with Apple's FoundationModels framework. Covers text generation, summarization, extracting data into Swift types, calling custom app functions, and progressively updating screens as responses are generated.
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.
Generate slides, documents, websites, and diagrams from natural language prompts through AnyGen OpenAPI. Manage reference uploads, check generation progress, and download finished files using commands with structured output for AI agents.
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.
Helps build AI features in Go with Genkit, including text generation, structured data output, streaming, tool calls, and conversational agents. Guides HTTP serving and trace inspection to check model responses and tool execution.
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.
Audit website structured data, the markup that describes businesses and content to machines. Detect errors and missing details, then generate JSON-LD code and a report with recommendations for improving AI readability.
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice design, voice replication, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. Covers SDK usage and best practices for Gemini models and agents in Python and TypeScript.
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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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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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Generate model responses in defined formats for data extraction and classification. Use JSON schemas, Pydantic data models, regular expressions, or fixed choices to produce structured results that other programs can parse and validate.
Generate videos from structured prompts describing subjects, visual style, camera work, and other scene details. The workflow creates English JSON prompts and runs a Python script, with optional reference images to guide the result.
Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.
Turn image briefs into structured prompts for GPT-Image-2 using reusable JSON templates, organized fields for visual requirements. Specify composition, text, lighting, and constraints for product images, posters, infographics, and other design work.
Invokes Google Gemini models for structured outputs, image generation, text-to-speech narration, multi-modal tasks, and Google-specific features. Use when users request Gemini, image generation, Gemini TTS or a synthesized voice, structured JSON output, Google API integration, or cost-effective parallel processing.
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