183 件 ・ 関連度順
概要と使いどころ
Cloudflare-first platform selection. Decision trees for Workers, D1, R2, KV, DO, Queues, Vectorize, Containers, Sandboxes, Flagship, Agent Memory, Workflows v2. Default stack, override conditions, auth, data patterns, reliability.
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 you need to visually interact with a GUI — test buttons, fill forms, verify visual layouts, fuzz web pages, automate user flows, take screenshots, or perform end-to-end QA on any application. Works on cloud VMs, Docker containers, local machines, and sandboxes. Install: pip install cua.
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 business-model canvas (Lean Canvas, Business Model Canvas, or Value Proposition Canvas) for the stage and fill it with specifics — one segment, one primary canvas, top-3 assumptions, no fluff in the moat or channel boxes. Load when the user asks to fill a business model canvas, lean canvas, value proposition canvas, model this business, map the business model, says "fill the BMC", "make a Lean Canvas", "Value Proposition Canvas for this", "model this idea", "what's the business model", "design the business model". Sub-skill of `venture-exploration`. Hard-bans "everyone" segments, generic channels ("SEO/social/content/ads"), and "unfair advantage = AI/data/network effects" with no concrete asset. Does NOT score viability — for that use `idea-evaluation`.
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 pack 3D boxes into containers, optimize 3D space utilization, or solve container loading problems. Also use when the user mentions "3D packing," "container packing," "box packing," "3D bin packing problem," "cube packing," "three-dimensional packing," "cargo loading," or "space optimization." For pallet-specific loading, see pallet-loading. For container logistics, see container-loading-optimization.
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。
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
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。
Generate ASCII art using pyfiglet (571 fonts), cowsay, boxes, toilet, image-to-ascii, remote APIs (asciified, ascii.co.uk), and LLM fallback. No API keys required.
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。
Comprehensive Flow Nexus platform management - authentication, sandboxes, app deployment, payments, and challenges
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 and deploy neural networks in distributed E2B sandboxes with Flow Nexus
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。
Read and manage email via IMAP (ProtonMail Bridge, Gmail, etc.). Check for new/unread messages, fetch content, search mailboxes, and mark as read/unread. Works with any IMAP server including ProtonMail Bridge.
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。
Detect VM/sandbox escape vulnerabilities in packages using node:vm, simpleeval, or custom sandboxes that can be bypassed to achieve code execution.
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。
Read and manage email via IMAP (ProtonMail Bridge, Gmail, etc.). Check for new/unread messages, fetch content, search mailboxes, and mark as read/unread. Works with any IMAP server including ProtonMail Bridge.
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。
Email has the highest ROI of any marketing channel. $36 for every $1 spent. Yet most startups treat it as an afterthought - bulk blasts, no personalization, landing in spam folders. This skill covers transactional email that works, marketing automation that converts, deliverability that reaches inboxes, and the infrastructure decisions that scale. Use when: keywords, file_patterns, code_patterns.
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。
API-first email platform designed for AI agents. Create and manage dedicated email inboxes, send and receive emails programmatically, and handle email-based workflows with webhooks and real-time events. Use when you need to set up agent email identity, send emails from agents, handle incoming email workflows, or replace traditional email providers like Gmail with agent-friendly infrastructure.
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。
Design RL simulation sandboxes, trajectory datasets, QLoRA/LoRA adaptation plans, eval harnesses, and rollback unhooks for agents learning specific coding behaviors beyond the base model. Use when building or reviewing tool-use training loops, offline trajectories, reward functions, self-improvement harnesses, or Port Daddy agent behavior curricula. NOT for generic ML tutorials, full model training operations, or production deployment without eval gates and safety unhooks.
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 you need to visually interact with a GUI — test buttons, fill forms, verify visual layouts, fuzz web pages, automate user flows, take screenshots, or perform end-to-end QA on any application. Works on cloud VMs, Docker containers, local machines, and sandboxes. Install: pip install cua.
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。