Total 50,522 skills, AI & Machine Learning has 8481 skills
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Japanese version of the PUA Universal Motivation Engine. It compels exhaustive problem-solving using corporate PUA rhetoric and structured debugging methodology in Japanese. MUST trigger under the following conditions: (1) Any task has failed 2+ times, or you're stuck in a loop of tweaking the same approach; (2) You're about to say 'I cannot', suggest manual handling to the user, or blame the environment without verification; (3) You find yourself being passive — not searching, not reading source code, not verifying, just waiting for instructions; (4) The user expresses frustration in any form: 'try harder', 'stop giving up', 'figure it out', 'why isn't this working', 'again???', 'もっと頑張れ', 'なんでまた失敗したの', 'もう一回やって', 'なんとかしろ', or any similar sentiment regardless of phrasing. It should also trigger when facing complex multi-step debugging, environment issues, configuration problems, or deployment failures where early surrender is tempting. Applies to ALL task types: code, configuration, research, writing, deployment, infrastructure, API integration. DO NOT trigger on first-attempt failures or when a known fix is already executing successfully.
Generate 3D models using each::sense AI. Create 3D assets from text or images for games, products, architecture, characters, vehicles, and more with PBR textures.
Mechanize Pattern 15 — the seven-pass adversarial review protocol for academic manuscripts. Spawns 7 forked subagents in parallel (abstract, intro, methods, results, robustness, prose, citations), then synthesizes a prioritized revision checklist. Use for submission-ready or R&R-stage papers where single-pass review isn't enough.
DeepFRI 的 TensorFlow 到 PyTorch 转换与昇腾 NPU 迁移 Skill，适用于蛋白质功能预测场景下的 TF 模型分析、PyTorch 重写、权重逐层映射、NPU 推理与精度验证，尤其适合需要在 Ascend 上运行 DeepFRI CNN 或 GCN 路径时使用。
Deep research on any topic using Perplexity, DeepWiki, and Context7. Use for comprehensive investigation of technologies, libraries, patterns, or domain questions.
Get AI-powered match predictions for Premier League and Champions League including scores, next goal, and corners.
Feed-forward 3D foundation model for streaming scene reconstruction using Geometric Context Transformer
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Orchestrates multi-advisor council debates on high-impact architecture, technology, or product decisions. Dispatches 3-5 domain archetype subagents (pragmatic-engineer, architect-advisor, security-advocate, product-mind, devils-advocate, the-thinker) through opening statements, tensions, position evolution, and synthesis phases. Preserves dissent and delivers actionable recommendations with captured risks. Use when evaluating trade-offs, stress-testing a PRD or tech spec, resolving dilemmas with multiple viable options, or when a decision needs diverse expert perspectives. Don't use for simple yes/no questions, factual lookups, creative brainstorming without tradeoffs, or tasks where a single expert perspective suffices.
Run a structured multi-perspective council on a hard decision, design choice, debugging question, strategy problem, or tradeoff. Use when the user wants multiple viewpoints, explicit cross-examination, and a compact final verdict.
Creates and orchestrates multi-agent pipelines on the iii engine. Use when building AI agent collaboration, agent orchestration, research/review/synthesis chains, or any system where specialized agents hand off work through queues and shared state.
MLA (Multi-Latent Attention) cost models, regime analysis, and kernel selection guide. Use when: (1) reasoning about which kernel approach to use for a given regime, (2) understanding cost model tradeoffs between FlashMLA, FlashAttention, and MLAvar6+, (3) analyzing roofline behavior across decode/speculative/prefill regimes, (4) setting optimization targets, (5) understanding MLA math and absorption trick.