「sel」の検索結果

Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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 to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

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。

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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。

DevOps & Infrastructure IA — Gère le VPS, Docker, déploiements, monitoring, sécurité, tunnels Cloudflare. Expert Hetzner + Coolify + self-hosting.

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。

Expert en platform engineering (developer experience, IDP, golden paths, self-service, métriques)

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。

Expert en data science (pandas, numpy, scikit-learn, feature engineering, model selection)

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。

Expert en data mesh (domain ownership, data products, self-serve, gouvernance fédérée)

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。

Conversion funnel v3 — TOFU, MOFU, BOFU, lead magnets, tripwires, upsells

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。

Expert en funnels de conversion (TOFU, MOFU, BOFU, lead magnets, tripwires, upsells)

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。

Expert en Coolify avancé (self-hosting, Docker, déploiements, SSL, backups, gestion des ressources, multi-serveurs)

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。

Coolify Specialist IA — Expert en Coolify (self-hosting, Docker, SSL, backups, one-click apps, migration)

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。

Expert en test automatisé (Playwright, Cypress, Selenium, CI integration, visual testing)

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。

Expert en n8n avancé (workflows, nodes, expressions, webhooks, self-hosted, custom nodes, scaling, error handling)

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。

Expert en RAG agentique (retrieval multi-étapes, self-query, chunking adaptatif, tool-augmented generation)

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。

Choose typefaces based on voice and context, not trends. Use when selecting fonts for a project, evaluating type choices, or understanding what a typeface communicates before anyone reads a word.

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。

Hostile self-review of recently written AI workflow code that then applies fixes (agents, tools, chat context, summarization, tokens, attachments). Use after generating AI systems code, or when asked to self-review and harden it. Not for PR peer review (code-review) or no-edit devil's-advocate attacks (adversarial-challenge). Prefer: adversarial-challenge → decide → this skill.

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。

Color selection engine: production-level product intent -> palette -> contrast -> tokens. Use when implementing, reviewing or testing color selection engine for React, Vite, Next.js or Perfect_AI projects.

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。

Self-hosted assets: production-level fonts, icons, dynamic imports, offline. Use when implementing, reviewing or testing self-hosted assets for React, Vite, Next.js or Perfect_AI projects.

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。

When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.

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。

Expert in building and selling Notion templates as a business - not just making templates, but building a sustainable digital product business. Covers template design, pricing, marketplaces, marketing, and scaling to real revenue. Use when: notion template, sell templates, digital product, notion business, gumroad.

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。

Search the public web and crawl URLs via the Unique AI Platform's `unique-cli web-search` command, with automatic per-turn citation tracking so cited web facts render as clickable external reference chips and `<sup>N</sup>` footnotes on the Unique platform. ALWAYS use this skill when the user asks to search the public web, research a topic online, look up news/articles/regulations, fetch the contents of one or more URLs, or perform any "two-phase" web research (search → review snippets → crawl selected URLs for full content). Routes through the Unique AI Platform's `/web-search-api` endpoints, so the engine (Google, Brave, Tavily, Jina, Firecrawl, …) and crawler are resolved server-side from the current environment — you never need to manage engine API keys yourself. Use this instead of fabricating answers from training data whenever fresh / source-cited information is needed. Do NOT use this skill for the internal knowledge base (`unique-cli search`) or for arbitrary tool execution (`unique-cli mcp`).

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。

Guide authors through a structured self-review before requesting peer review.

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。

Review board (Busabase App-in-Skill) that runs a fixed suite of mock test cases against a baseline vs candidate agent version and surfaces rubric-scored regressions before a release. Use when the user invokes $kelly-agent-eval or /kelly-agent-eval, wants to review agent-version regressions, compare baseline vs candidate quality, triage a release, or record a release approve/block decision. Deterministic mock rubric scores only — not a real LLM-judge call, and it never deploys anything.

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。