2.5K skills · sorted by relevance
Generate images and videos using AI models on fal.ai. The skill covers Flux, SDXL, ideogram, and other community-hosted model endpoints, providing an entry point for requesting generation through an agent.
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.
Create and edit images or generate short videos with xAI Grok. The skill explains how to write prompts, combine source images, assign reference images to video roles, and check generation progress before downloading results.
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.
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.
Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.
Build a new classroom course through an agreed page plan, teacher and learner personas, ordered page generation, and final checks. It defines when pages and narration audio are complete and how to handle generation failures.
Plan customer acquisition through paid ads, SEO, and partnerships for growing B2B SaaS startups. Includes campaign templates, customer acquisition cost calculations, and HubSpot tracking guidance to connect marketing activity with sales opportunities.
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.
Helps speed up LLM text generation by comparing methods that predict and verify multiple tokens at once. Covers draft model selection, Medusa prediction heads, Lookahead Decoding settings, and deployment examples for inference services.
Generate images from text or reference images using multiple image services. Choose aspect ratios, sizes, and quality settings, or generate a collection of images in batches from saved prompt files.
Reduce LLM generation delays with speculative decoding, which proposes tokens ahead of verification. This skill compares small draft models, Medusa, and Lookahead Decoding, with implementation examples, tuning guidance, and deployment using vLLM.
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 PowerPoint presentations with specified slide content and structure. If generation through shell_agent fails, the workflow switches to Python, captures error details, locates the output, and checks the file format, size, and slide count.
Create PowerPoint presentations with shell_agent, then switch to Python and python-pptx if generation fails. The workflow captures error details, locates output files, and checks that the presentation opens and contains the expected slides.
Troubleshoot PDF creation by trying pandoc, then reportlab, then fpdf2. Check tool availability, install missing Python libraries as needed, run generation with an explicit working directory, and verify that the output file exists.
Start and monitor an Ouroboros development loop that revises project concepts and acceptance criteria, the conditions for judging completion. Run and evaluate successive generations, focus revisions on failed criteria, inspect progress, or rewind to an earlier generation.
Make any UI appear as if an AI agent is generating it. A dotted wireframe is drawn from the element's own structure (outline, boxes, text lines, icon paths, with guides, corner handles and size labels), then the real UI is revealed behind a scan line with a fade on a smooth ease-out. Two modes, skeleton and skeleton + generation texts (an agent log, the motion CSS being written, and a step line). One dependency-free runtime that works with any framework (vanilla, React, Next.js, Vue, Svelte). Use when the user asks for a "generation animation", "AI generating the UI", "agent building the component", "build-in animation", "wireframe to UI reveal", "skeleton generation", "dotted wireframe", "matrix dots", "make it look generated", "futuristic reveal", "generative UI loading", or for an AI product that should show its output being built. Also the commands transitions generate, transitions generate texts and transitions generate preview.
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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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Unified skill for all MATLAB and Simulink report generation tasks. Covers report generation, PDF report, document generation, mlreportgen, Report Generator, template, TitlePage, Chapter, multipage HTML, HTMLPage, html-multipage, custom reporter, customizeReporter, template hole, page header, page border, company style package, branded reporters, buildTemplates, Simulink report, model documentation, slreportgen, block diagram report, model report, subsystem documentation, SystemDiagramFinder, SystemIO, ExecutionOrder, LookupTable, template style, page footer, TemplateSrc, report package, table variants, multiple HTML pages, PDF report generation, Word report, DOM API, FormalTable, BaseTable, Figure, FormalImage, Section.
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Generate text, images, video, speech, and music via the MiniMax AI platform. Covers text generation (MiniMax-M3 model), image generation (image-01), video generation (Hailuo-2.3), speech synthesis (speech-2.8-hd, 300+ voices), music generation (music-2.6 with lyrics, cover, and instrumental), and web search. Use when the user needs to create AI-generated multimedia content, produce narrated audio from text, compose music, or search the web through MiniMax AI services.
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Build a demand generation strategy and multi-channel campaigns. Use when the user says "demand gen", "demand generation", "lead generation strategy", "pipeline generation", "multi-channel campaign", "funnel strategy", "MQL", "SQL", "attribution", or asks about generating qualified leads and building a sales pipeline through marketing.
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Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups. Includes CAC calculator, channel playbooks, HubSpot integration, and international expansion tactics. Use when planning demand generation campaigns, optimizing paid media, building SEO strategies, establishing partnerships, or when user mentions demand gen, paid ads, LinkedIn ads, Google ads, CAC, acquisition, lead generation, or pipeline generation.
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Master AI-powered game asset pipelines using ComfyUI, Stable Diffusion, FLUX, ControlNet, and IP-Adapter. Creates production-ready sprites, textures, UI, and environments with consistency, proper licensing, and game engine integration. Use when "AI game art, generate game assets, ComfyUI game, stable diffusion sprites, AI texture generation, character consistency AI, procedural art generation, SDXL game assets, FLUX textures, train LoRA game, AI tileable texture, spritesheet generation, " mentioned.
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