Results for “variation-generation”

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

Generate rights-cleared product photography variants for paid ads from an authorized source image and brand profile.

Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).

Creates SVG logos with scalable geometry, color theory, and variant generation. Use when designing logos, icons, or brand marks, generating favicons or social avatars, or reviewing a logo for scalability, color, or variant coverage.

Define every visual attribute as structured VGL JSON for deterministic, reproducible image generation with Bria FIBO models, covering objects, lighting, camera settings, composition, and style.

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

Identify and design growth loops (flywheels) for sustainable traction, evaluating five proven mechanisms: viral, usage, collaboration, user-generated, and referral.

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

Designs unconventional tactics, growth hacks, and viral campaigns. Use when the user requests 'growth hack', 'viral campaign', 'guerrilla marketing', 'low-budget marketing', or 'marketing stunt'.

Mantém a coerência visual entre peças geradas por IA usando modelo fixo, prompt base, seed e referência de estilo, com teste de coerência e biblioteca de prompts.

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.

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.

Retrieval-Augmented Generation pipelines — ingestion, chunking, embedding, vector stores, retrieval, evaluation. Use when building a RAG pipeline, choosing chunking strategies or embedding models, debugging retrieval quality or hallucinations, evaluating an existing RAG system, or scaling/migrating vector stores.

Uses mutation testing to systematically identify gaps in test vector coverage for cryptographic algorithms, then generates new test vectors that close those gaps. Measures effectiveness by comparing mutation kill rates before and after.

Finetunes the NV-Generate-CTMR MR-brain diffusion UNet from user-supplied NIfTI training volumes using a wrapper that stages configs and delegates to upstream scripts.

Generates synthetic brain MRI volumes using NVIDIA's NV-Generate-CTMR workflow, with configurable modality and random seed.

Finetune the NV-Generate-CTMR MAISI VAE/autoencoder on user-supplied CT or MRI NIfTI volumes using a staged config and datalist workflow.

Generates phrase-grounded bounding box annotations from image-caption pairs using a VLM, producing cleaned captions, referring expressions, and pixel-space bounding boxes.

Generate a structured specification document before writing implementation code, validating scope, drafting specs, and registering them in the project.

Seed minimal agent harness v0 — manifest, eval checks stub, governance. AUTO-INVOKED after project-setup or retroactive-project-setup when docs/harness/manifest.json is missing. Also triggers on: generate harness, scaffold agents, agent bootstrap, first time agents in this repo, new project agent setup, set up agent harness, agent onboarding files, missing agent configuration, agent instructions setup, make agents read project rules, agent reliability setup, agents not configured. Pairs with project-setup. Evolution is harness-evolution.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

Provides deep generative models for single-cell omics analysis, including probabilistic batch correction, transfer learning, differential expression, and multi-modal integration.

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

Generate short videos from a text prompt or from still images using ByteDance Seedance on Volcengine Ark. Use this skill whenever the user wants to create, generate, render, or animate a video, clip, animation, motion graphic, or product demo — including text-to-video ("a drone shot over a forest at sunrise") and image-to-video, where a still becomes the first frame, or two stills are morphed start-to-end. Prefer this skill over describing a video in text.

Gated PRD generation — interrogates problem, user, and metric before drafting; refuses to draft on unknowns. Usage: /prd <feature-or-problem>

Generates or edits images for projects using the OpenAI Image API, with support for batch runs and structured prompt augmentation.

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

Generates short AI videos from text or images across Runway, Kling, Sora, Pika, Seedance, Grok Imagine, and Veo, with provider-specific APIs and prompt guidance.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Measures distributional similarity between original GAN-generated images and their semantically manipulated counterparts using the Fréchet Inception Distance (FID) metric.

Query the 1000 Genomes Project dataset at the individual participant level to find variants, carriers, and relatedness information.

Identifies and designs growth loops (flywheels) for sustainable traction — evaluating 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms or reducing reliance on paid acquisition. Triggers on: growth loop, flywheel, viral loop, referral program, product-led growth, PLG, user acquisition, growth strategy, retention loop, compounding growth.

DALL-E: Zero-Shot Text-to-Image Generation

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.