1.2万 件 ・ 関連度順
概要と使いどころ
Builds production-quality UIs. Use when building or modifying user-facing interfaces. Use when creating components, implementing layouts, managing state, or when the output needs to look and feel production-quality rather than AI-generated.
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。
Atlassian Administrator for managing and organizing Atlassian products (Jira, Confluence, Bitbucket, Trello), users, permissions, security, integrations, system configuration, and org-wide governance. Use when asked to add users to Jira, change Confluence permissions, configure access control, update admin settings, manage Atlassian groups, set up SSO, install marketplace apps, review security policies, or handle any org-wide Atlassian administration task.
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。
Audit and rewrite content to remove 21 categories of AI writing patterns with a 43-entry replacement table
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。
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
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。
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
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。
Automate Asana tasks via Rube MCP (Composio): tasks, projects, sections, teams, workspaces. Always search tools first for current schemas.
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。
Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy.
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。
Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.
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。
Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.
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。
Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok.
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。
Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.
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。
Analyze competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com, Facebook, Instagram, YouTube, and TikTok.
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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。
Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.
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。
Automate Anthropic Admin tasks via Rube MCP (Composio): API keys, usage, workspaces, and organization management. Always search tools first for current schemas.
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 designing, reviewing, refactoring, or enforcing Angular application architecture with isolated lazy features, eager core/layout, reusable ui/pattern architecture building blocks, one-way dependency graphs, eslint-plugin-boundaries rules, or folder placement decisions. This skill takes precedence over angular-developer when architecture rules conflict with general Angular advice.
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。
Act as a senior Amazon account growth operator that combines Amazon performance-drop analytics, Amazon growth-opportunity analytics, Amazon Ads data, retail readiness, BSR/rank signals, margin, inventory, and campaign/search-term evidence into a prioritized profitable growth action plan. Use when Codex needs to decide what to protect, fix, scale, reduce, pause, harvest, launch, or monitor for an Amazon brand, agency, marketplace, ASIN set, or account; when building daily, weekly, or monthly Amazon account operating plans; when reallocating ad budget; when deciding which ASINs are safe or risky to push; or when turning performance-drop and growth-opportunity findings into approval-gated actions with success/failure criteria.
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。
Diagnose why Amazon advertising performance declined for a product, ASIN, campaign, marketplace, or account. Use when the user asks why sales, orders, ROAS, ACOS, TACoS, profit, BSR, rank, traffic, conversion rate, or campaign performance dropped, asks for dropped campaigns, says investigate drop/drop analysis, or asks about wasted spend, negative keywords, negative targets, keywords, targets, or search terms in a drop context.
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 Amazon Ads optimization analysis, recommendations, approval packets, demos, recordings, or public artifacts must preserve exact source KPIs while masking account/profile/project names, products, ASINs, SKUs, campaigns, ad groups, keywords, search terms, targets, placements, filenames, URLs, and source-derived identifiers. Produces masked approval packets only; it must not directly mutate Amazon Ads.
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。
Replace this with a precise trigger description. State the Amazon PPC workflow, user intent, required data, and when Codex or Claude should use 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。
Trace bugs backward to their origin, not just symptoms
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 planning or upgrading an internal skill/workflow for business operations, especially when turning a messy idea into a structured skill plan, migration plan, acceptance checklist, or packaging decision. Particularly useful for Obsidian knowledge base consolidation, workspace migration, content library cleanup, and integrating multiple workbenches into one operating system.
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。
Trace the purpose of suspicious legacy code using current callers, tests, and Git history when deciding whether it can be changed or removed.
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。
Audit whether tests detect broken behavior by tracing assertions and running narrow controlled mutations in an isolated workspace; use for test-quality reviews, not routine test 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。