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概要と使いどころ
Garanta estrutura válida de JSON/XML/código durante a geração, use modelos Pydantic para outputs type-safe, suporte modelos locais (Transformers, vLLM) e maximize velocidade de inferência com Outlines - biblioteca de geração estruturada da dottxt.ai
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Framework RLHF de alta performance com aceleração Ray+vLLM. Use para treinamento PPO, GRPO, RLOO, DPO de modelos grandes (7B-70B+). Construído em Ray, vLLM, ZeRO-3. 2× mais rápido que DeepSpeedChat com arquitetura distribuída e compartilhamento de recursos GPU.
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Quantiza LLMs para 8-bit ou 4-bit com redução de memória de 50-75% e perda mínima de acurácia. Use quando a memória GPU é limitada, precisa ajustar modelos maiores ou quer inferência mais rápida. Suporta formatos INT8, NF4, FP4, treinamento QLoRA e otimizadores 8-bit. Funciona com HuggingFace Transformers.
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Modelo de espaço de estados com complexidade O(n) versus O(n²) dos Transformers. Inferência 5× mais rápida, sequências de milhão de tokens, sem cache KV. SSM seletivo com design aware de hardware. Mamba-1 (d_state=16) e Mamba-2 (d_state=128, multi-head). Modelos 130M-2.8B no HuggingFace.
日本語の概要は準備中です。原文の説明を表示しています。
Treinar modelos de Mixture of Experts (MoE) usando DeepSpeed ou HuggingFace. Use ao treinar modelos em larga escala com computação limitada (redução de 5× em custos vs modelos densos), implementar arquiteturas esparsas como Mixtral 8x7B ou DeepSeek-V3, ou escalar capacidade de modelo sem aumento proporcional de computação. Cobre arquiteturas MoE, mecanismos de roteamento, balanceamento de carga, paralelismo de especialistas e otimização de inferência.
日本語の概要は準備中です。原文の説明を表示しています。
Acelere a inferência de LLMs usando especulative decoding, múltiplas cabeças Medusa e técnicas de lookahead decoding. Use ao otimizar velocidade de inferência (aceleração de 1,5-3,6×), reduzir latência em aplicações em tempo real ou fazer deploy de modelos com recursos computacionais limitados. Cobre modelos draft, atenção em árvore, iteração de Jacobi, geração paralela de tokens e estratégias de deploy em produção.
日本語の概要は準備中です。原文の説明を表示しています。
Use this Canvas-owned clean-room skill when a spreadsheet file is the primary input or output. Trigger for `.xlsx`, `.xls`, `.xlsm`, `.csv`, `.tsv`, workbooks, templates, trackers, models, dashboards, charts, formulas, data cleaning, formatting, validation, recalculation, and Google Sheets-targeted handoff.
日本語の概要は準備中です。原文の説明を表示しています。
Use when integrating an app with local AI models through Ollama — connection checks, model listing/selection, local-only mode, cloud-fallback consent, visible run states, and recovering from Ollama errors.
日本語の概要は準備中です。原文の説明を表示しています。
高级财务建模套件，包含DCF估值、敏感性分析、蒙特卡洛模拟和场景规划。当用户需要对企业、项目或并购交易进行投资分析、股权估值、IRR/MOIC计算、WACC建模，或要求建立DCF模型、运行概率模拟、制作敏感性表格时，请主动使用此技能。
日本語の概要は準備中です。原文の説明を表示しています。
Agent Media OS, the single skill for every media need in a HyperFrames project. Resolve BGM, SFX, image, icon, brand logo, voice, color grade, or LUT into a frozen local file or paste-ready block + ledger record (one verb, `resolve`); generate via TTS / music / image models when the catalog misses; produce voiceover, transcription, captions, and background removal through one shared audio engine; operate on media (cut / reframe / transform); and reuse assets across projects. Also use for vague feedback that real footage looks dark, flat, boring, should feel retro/camcorder/print/ASCII, needs privacy, or needs a media reveal.
日本語の概要は準備中です。原文の説明を表示しています。
Scaffold backend API, data models, ORM setup, and endpoint inventory with OpenAPI output.
日本語の概要は準備中です。原文の説明を表示しています。
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
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Structured Domain Modeling Architecture (SDMA) principles. Trigger when: - Designing domain models, building ologs (ontological graphs), verifying protocol equivalence, selecting schemas, or measuring architectural entropy. - Modeling structures using Category Theory, Coalgebra, or Information Theory. - Prompt contains keywords: sdma, olog, Category Theory, Coalgebra, Information Theory, entropy, schema, pullback, functor, monad.
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SOP for formal mathematical domain modeling. Trigger when: - Creating new formal models or applying mathematical lenses to analyze existing specifications. - Navigating phases: Identify, Select, Construct, Validate, Record, Connect. - Prompt contains: /form, form workflow, categorical, coalgebraic, linear logic, information-theoretic, validation.
日本語の概要は準備中です。原文の説明を表示しています。
AID Phase 2 - Technical Specification. Use for system architecture, API contracts, data models, security architecture, transitioning from PRD to implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Create, revise, and validate publication-ready academic paper figures and tables. Use for LaTeX tables, related-work comparison tables, result tables, notation/dataset/taxonomy tables, precise source-data-driven experiment plots from CSV/JSON/logs, generated conceptual figures such as system overviews/pipelines/architectures/threat models, captions, artifact specs, source-data traceability, and paper-ready PDF/SVG/PNG/LaTeX exports. Do not use for prose-only paper writing, self-review, reviewer response, rebuttal drafting, or external literature-management workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Exasol BucketFS file system management via the `exapump bucketfs` CLI. Covers listing, uploading, downloading, and deleting files and directories in BucketFS, the `bfsdefault` service and other bucket services, bucket structure, `bfs_*` profile settings, the `/buckets/<service>/<bucket>/<path>` UDF path, and staging JARs, models, and Script Language Containers for UDFs.
日本語の概要は準備中です。原文の説明を表示しています。
Add Ollama MCP server so the container agent can call local models and optionally manage the Ollama model library.
日本語の概要は準備中です。原文の説明を表示しています。
Add Ollama MCP server so the container agent can call local models and optionally manage the Ollama model library.
日本語の概要は準備中です。原文の説明を表示しています。
Compose a comparison newsletter from Ahmad's OpenMark saves — A vs B vs C, side-by-side, table-driven. Use when Ahmad wants to compare tools, models, frameworks, approaches, or platforms across his saves. Trigger phrases include "compare", "X vs Y", "head to head", "which should I use", "differences between", "trade-offs between", "/newsletter-comparison". Output centers a markdown table with consistent columns, supports it with short prose blocks, ends with a clear recommendation.
日本語の概要は準備中です。原文の説明を表示しています。
Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
日本語の概要は準備中です。原文の説明を表示しています。
Build reactive, type-safe, production-grade backends. ALWAYS use this skill when the user asks to build, plan, design, or implement backend features, APIs, data models, server logic, database schemas, web apps, full stack apps, or mobile apps. This includes planning and architecture discussions.
日本語の概要は準備中です。原文の説明を表示しています。
Build reactive, type-safe, production-grade backends. ALWAYS use this skill when the user asks to build, plan, design, or implement backend features, APIs, data models, server logic, database schemas, web apps, full stack apps, or mobile apps. This includes planning and architecture discussions.
日本語の概要は準備中です。原文の説明を表示しています。
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, co...
日本語の概要は準備中です。原文の説明を表示しています。