「aso」の検索結果

Amazon keyword research and market opportunity analysis for sellers. Retrieve autocomplete suggestions (long-tail keywords), analyze competitor landscape, and assess market opportunity for any keyword on 12 Amazon marketplaces (US/UK/DE/FR/IT/ES/JP/CA/AU/IN/MX/BR). No API key required. Make sure to use this skill whenever the user mentions Amazon product research, finding products to sell on Amazon, Amazon keyword ideas, niche analysis, competition analysis for Amazon, market opportunity on Amazon, comparing Amazon keywords, evaluating whether a product is worth selling, Amazon autocomplete data, seasonal demand for Amazon products, or anything related to researching what to sell on Amazon — even if they don't explicitly say 'keyword research'. Also trigger when the user asks vague questions like 'is this a good product to sell?', 'what's the competition like for X on Amazon?', 'should I sell X or Y?', or 'what are people searching for on Amazon?'.

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。

Start a substantial build with Codex on Crack. Inspect the user's own configured models and readiness, recommend a lead/worker/tool shape with reasons, and let the user decide before work starts. Use before delegating; keep small or solo tasks direct.

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 uncommitted code changes to find unreasonable design, unfinished requirements, potential bugs, missed reuse opportunities, and optimization opportunities. Use when the user asks for a review of pending, uncommitted, or unpublished code changes and expects both findings and concrete improvements.

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。

Weekly. Works dependency and supply chain debt one project at a time, sorting every candidate into patch and lockfile only, minor, and anything major or breaking. It bumps the first class on a branch behind that project's own gate and pushes it for the member to merge. It holds the other two with a reason and a card carrying the evidence, so the member decides rather than being handed a red build.

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 a PERSONAL brand needs its OWN community — Skool, Mighty Networks, Discord, Circle, or Slack: a three-layer public, member, and inner-circle blueprint, seven-day onboarding, engagement rituals, a moderation playbook, and activation metrics. Trigger on 'build a community', 'Skool community', 'Mighty Networks setup', 'Discord for my audience', 'paid community', 'my group is dead and nobody posts'. Also use when the user has members and no reason for them to come back. Not for — posting inside communities you do NOT own, see `38-community-seeding-global`; pricing and packaging the membership, see `27-personal-brand-monetize-global`; referral loops, see `18-referral-program-global`.

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 positioning needs defining or sharpening — a statement built on Promise, Proof, and Path, a differentiation matrix against competitors, category entry points, a three-level message hierarchy, and tagline directions. Trigger on 'positioning', 'positioning statement', 'what makes us different', 'USP', 'why should customers choose us over them', 'we sound the same as everyone else'. Also use when the user can list features but not a reason to pick them. Not for — competitor teardown research, see `08-competitor-research-global`; how the brand sounds in writing, see `35-brand-voice-global`; the offer and its value stack, see `31-offer-design-global`; the launch built on it, see `59-go-to-market-global`.

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 a price has to be set or changed — value-based pricing, anchoring, charm pricing, decoy tiers, good-better-best packaging, discount policy, and margin math, with currency psychology for US, EU, SEA, and LATAM plus dropshipping markup and BE-ROAS. Trigger on 'pricing strategy', 'how much should I charge', 'should I raise prices', 'price tiers', 'customers say it is too expensive', 'dropshipping markup'. Also use when the product is not selling and the user suspects price is the reason. Not for — bonuses, guarantee, and value stack around the price, see `31-offer-design-global`; persuasion beyond price, see `16-marketing-psychology-global`; ad budget math, see `10-reverse-kpi-global`.

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。

Phase 3 of the prd-taskmaster pipeline: smart mode selection and user handoff. Detects installed capabilities (superpowers, ralph-loop, task-master-ai, playwright, research providers), recommends ONE execution mode (A/B/C) with reasoned justification, appends the task-execution workflow to CLAUDE.md, surfaces a structured AskUserQuestion multi-option picker for user agency, and dispatches the chosen mode. Mode D (Atlas Fleet) is selectable only when detect_capabilities returns tier=premium (licensed atlas-launcher detected); otherwise it is a locked Atlas Pro teaser. Plan Mode is NOT used (spec section 13.5): AskUserQuestion is the sole user-agency mechanism. Declares HANDOFF complete so EXECUTE can follow.

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Your AI research and engineering brain trust. 59 named personas across 8 cells covering frontier labs, applied product, model architecture, reasoning/RL/agents, alignment and interpretability, theory and science of DL, multimodal and…

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 AI applications with OpenAI Agents SDK - text agents, voice agents, multi-agent handoffs, tools with Zod schemas, guardrails, and streaming. Prevents 11 documented errors. Use when: building agents with tools, voice agents with WebRTC, multi-agent workflows, or troubleshooting MaxTurnsExceededError, tool call failures, reasoning defaults, JSON output leaks.

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 working with this machine's local LLM setup (LM Studio + mlx-dspark) -- checking status, changing context window, diagnosing a reasoning hang or dead request, understanding RAM/speed tradeoffs, or wiring a new script to the local inference endpoint.

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。

The consumer panel. Spins up a swarm of buyer sub-agents (default 100, --quick for 20) trained on the user's target customer, each with a different income, age, buying behaviour and objection, shows every one the same pitch, and runs the business through 100 buyer scenarios to see who buys, who doesn't, and why. Tallies the answers into buy rates by segment, the reasons people pass in their own words, what would flip a no, and price answers for founder-pricing. Use when the user says "would people buy this", "test it on customers", "run the consumer panel", "simulate buyers", "who is my customer", or "validate demand".

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。

Ultra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when "why" isn't obvious. Use when user says "write a commit", "commit message", "generate commit", "/commit", or invokes /caveman-commit. Auto-triggers when staging changes.

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。

Make 100 versions of Claude fight to the death over one task. Spins up N sub-agents (default 100, --quick for 16), gives every one the exact same task plus a different strategy card (reasoning mode, workflow, strategy), then runs a single-elimination bracket: they attack each other's solutions, defend and revise, and a judge scores every match on a written rubric until one solution survives. Use when the user is not satisfied with an answer, calls it a bad answer, says try again or do better, says "arena", or asks to make them compete.

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。

Interactive briefing of a plan file — explains reasoning, schema; Use when developers need to understand a plan…

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。

Interactive briefing of a plan file — explains reasoning, schema decisions, component choices. Use when developers need to understand a plan before approving.

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。

Interactive briefing of a plan file — explains reasoning, schema decisions, component choices. Use when developers need to understand a plan before approving.

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。

The Balanced Coupling model for software design. Use when: designing modular architectures, evaluating coupling between components, reviewing code modularity, deciding whether to split or merge modules/services, assessing integration patterns, classifying coupling as balanced or unbalanced, applying DDD strategic and tactical patterns, reasoning about cohesion vs coupling trade-offs, identifying distributed monolith risks, or explaining why a system is hard to change. Provides the three-dimensional framework (integration strength, distance, volatility) and the balance rule for making coupling decisions.

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时序/数值异常标记工具。对指定或全部数值列进行 Z-Score 异常和差分突变检测， 输出 anomaly_flag 与 anomaly_reasons，不删除数据，适用于时序稳态/突变异常识别。

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数据排序工具。读取结构化数据文件，根据指定字段进行升序或降序排序，最后输出为相同格式的文件。 当用户提到数据排序、升序排列、降序排列、按某字段排序等需求时使用此skill。 即使用户没有明确说出"排序"，只要任务涉及对数据进行排序，就应该使用此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。

AI-powered web search, research, and reasoning via Perplexity

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。

Implements media and file management components including file upload (drag-drop, multi-file, resumable), image galleries (lightbox, carousel, masonry), video players (custom controls, captions, adaptive streaming), audio players (waveform, playlists), document viewers (PDF, Office), and optimization strategies (compression, responsive images, lazy loading, CDN). Use when handling files, displaying media, or building rich content experiences.

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。

Write, run, or repair .NET tests that use TUnit. Use when a repo uses `TUnit`, `TUnit.Playwright`, `[Test]`, `[Arguments]`, `ClassDataSource`, `SharedType.PerTestSession`, or Microsoft.Testing.Platform-based execution. Preserve TUnit's parallel-by-default model and constrain only destructive shared-state collisions. DO NOT USE FOR: xUnit projects; MSTest projects.

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Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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。