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Found 200 Skills
Generate 2D pixel art game assets, characters, sprite sheets, background removal, and game backgrounds. Trigger for "pixel art character", "sprite sheet", "walk cycle", "game sprites", "isometric sprites", "side-scroller assets", "RPG character sprites", "idle animation", "attack animation", "jump animation", "game background", "parallax background", "isometric map", "2D game art", "pixel art animation". Covers character generation (nano-banana-pro / gpt-image-2), sprite sheet animation (nano/edit or gpt-image-2/edit), background removal (Bria), and background generation (parallax layers or isometric map).
This skill should be used when debugging full-stack issues that span UI, backend, and database layers. It provides a systematic workflow to detect errors, analyze root causes, apply fixes iteratively, and verify solutions through automated server restarts and browser-based testing. Ideal for scenarios like failing schedulers, import errors, database issues, or API payload problems where issues originate in backend code but manifest in the UI.
Guidance for recovering PyTorch model architectures from state dictionaries, retraining specific layers, and saving models in TorchScript format. This skill should be used when tasks involve reconstructing model architectures from saved weights, fine-tuning specific layers while freezing others, or converting models to TorchScript format.
Provides brand messaging architecture, value proposition, and brand pillar development frameworks including Peep Laja's Message Layers, Osterwalder's Value Proposition Canvas, Geoffrey Moore positioning template, April Dunford's Five Components, StoryBrand SB7, Andy Raskin's Strategic Narrative, the Messaging House, and MECLABS quality tests. Auto-activates during messaging framework development, value proposition creation, and brand pillar definition. Use when discussing messaging architecture, value proposition, brand pillars, message layers, messaging house, messaging hierarchy, elevator pitch, Peep Laja, Geoffrey Moore, April Dunford, StoryBrand, Andy Raskin, or MECLABS.
Optimize ToolUniverse skills for better report quality, evidence handling, and user experience. Apply patterns like tool verification, foundation data layers, disambiguation-first, evidence grading, quantified completeness, and report-only output. Use when reviewing skills, improving existing skills, or creating new ToolUniverse research skills.
Guidance for implementing tensor parallelism in PyTorch, including ColumnParallelLinear and RowParallelLinear layers. This skill should be used when implementing distributed tensor parallel operations, sharding linear layers across multiple GPUs, or simulating collective operations like all-gather and all-reduce for parallel computation.
Use when the user asks to implement FSD, use Feature-Sliced Design, organize architecture, structure project folders, set up FSD layers, create feature slices, refactor to FSD, or mentions feature slices, layered architecture, FSD methodology, views layer, entities layer, shared layer, Next.js with FSD. Provides comprehensive guidance for implementing Feature-Sliced Design (FSD v2.1) in Next.js applications. Optional monorepo (Turborepo) support - see references/monorepo.md.
Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.
Build production database layers with SQLAlchemy ORM and PostgreSQL. This skill should be used when teaching students to define data models, manage sessions, perform CRUD operations, and connect to PostgreSQL/Neon databases.
Full closed-loop QA combining issue discovery and software testing. Scout -> Strategist -> Generator -> Executor -> Analyst with multi-perspective scanning, progressive test layers, GC loops, and quality scoring. Supports discovery, testing, and full QA modes.
This skill should be used when the user asks about Effect-TS patterns, services, layers, error handling, service composition, or writing/refactoring code that imports from 'effect'. Also covers Effect + Next.js integration with @prb/effect-next.
Frosted glass effect with translucent layers, subtle blur, and luminous borders for depth and modern elegance.