「insight」の検索結果

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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World-class color theory expertise combining the scientific precision of Josef Albers' "Interaction of Color," the systematic thinking of color systems from Pantone and RAL, and the perceptual psychology insights from researchers like Bevil Conway. Color is not just aesthetics - it's communication, emotion, and usability compressed into wavelengths. Great color work is invisible when done right. Users don't notice "nice colors" - they notice when they can't read text, when buttons don't look clickable, when errors don't feel urgent, or when the interface feels "off" without knowing why. Color theory is the science of making the right thing feel obvious. Use when "color theory, color palette, color scheme, color harmony, complementary colors, analogous colors, contrast ratio, dark mode colors, light mode, color tokens, semantic colors, color accessibility, color blindness, color psychology, color system, brand colors, data visualization colors, color, design, accessibility, contrast, dark-mode, theming, tokens, wcag, palette, harmony" mentioned.

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The art and science of narrative-driven brand communication. This skill transforms brand messages into compelling stories that connect emotionally, build memory, and drive action. In a world of infinite content, story is the only thing that cuts through. Great brands don't sell products—they tell stories. Apple tells the story of creative rebels. Nike tells the story of human potential. Patagonia tells the story of environmental stewardship. This skill teaches you to find, craft, and tell your brand's story across every touchpoint. AI amplifies storytelling capability—generating variations, adapting across channels, maintaining consistency—but the human insight that identifies the true story remains essential. This skill bridges narrative craft with AI execution. Use when "story, storytelling, narrative, brand story, origin story, hero journey, emotional connection, customer story, testimonial, case study, brand film, about us, storytelling, narrative, brand-story, emotional, hero-journey, content" mentioned.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.

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Accessibility isn't a feature - it's a fundamental quality of good software. 1 in 4 adults has a disability. Many more have temporary impairments or situational limitations. Your users include people who can't see, can't hear, can't use a mouse, or can't process information the way you do. This skill covers WCAG guidelines, ARIA patterns, keyboard navigation, screen reader compatibility, and automated testing. Key insight: accessible code is better code. The constraints of accessibility lead to simpler, more semantic, more maintainable implementations. 2025 lesson: Accessibility lawsuits are at an all-time high. "We'll add it later" is both ethically wrong and legally risky. Start accessible, stay accessible. Use when "accessibility, a11y, wcag, aria, screen reader, keyboard navigation, focus trap, alt text, color contrast, skip link, accessible, accessibility, a11y, wcag, aria, screen-reader, keyboard, inclusive, semantic-html" mentioned.

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Angular is opinionated and comprehensive - it gives you everything: routing, forms, HTTP, dependency injection, testing. The learning curve is steep, but once you're in, you move fast. The structure it enforces is why enterprises love it. This skill covers Angular 17+, standalone components, signals, the new control flow syntax, and modern Angular patterns. Key insight: Angular's power is in its DI system and RxJS integration. Master those, and everything else follows. 2025 lesson: Standalone components are the future. NgModules aren't going away, but new projects should start standalone. Signals are Angular's answer to fine-grained reactivity - learn them. Use when "angular, angular component, angular service, angular routing, angular forms, rxjs, ngrx, angular signals, standalone component, angular ssr, angular, typescript, frontend, spa, enterprise, rxjs, signals, standalone" mentioned.

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementations. The LLM never sees your code - it only sees the schema and description. Use when "agent tool, function calling, tool schema, tool design, mcp server, mcp tool, tool use, build tool for agent, define function, input_schema, tool_use, tool_result, agents, tools, function-calling, mcp, json-schema, anthropic, openai, llm-tools" mentioned.

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The specialized craft of writing copy that sells—headlines that stop thumbs, body copy that persuades, CTAs that convert. This is copywriting specifically tuned for advertising: paid media, performance marketing, direct response. Ad copy is different from content writing. Every word costs money (CPM/CPC). Every character competes for attention. There's no time for warm-up—you have one chance to hook, persuade, and convert. The best ad copy feels effortless but is engineered word by word. In the age of AI-generated creative at scale, ad copywriting becomes both more important (you need more variations) and more challenging (everyone has access to the same tools). The differentiation is in the strategy, the insight, and the craft that AI assists but doesn't replace. Use when "ad copy, headline, ad text, CTA, call to action, Facebook ad, Google ad, ad variations, direct response, performance copy, primary text, ad headline, ad-copy, headlines, direct-response, performance, conversion, cta, advertising" mentioned.

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Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", "google api setup", "real CWV data", "impressions", "clicks", "CTR", "position data", "LCP", "INP", "CLS", "FCP", "TTFB", or "Lighthouse scores".

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。

Summarize a user interview transcript into structured insights with key themes, quotes, and action items. Use after conducting user interviews to extract and share findings efficiently.

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 a structured user interview script with warm-up, core exploration, and wrap-up sections. Use when preparing for user research interviews to ensure consistent, insightful conversations.

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 a 4-quadrant empathy map (Says, Thinks, Does, Feels) to synthesize user research into actionable insights. Use when you need to quickly capture and share user understanding across the team.

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。

Organize qualitative research data into an affinity diagram with themes, clusters, and insight statements. Use when synthesizing large amounts of qualitative data from interviews, observations, or surveys.

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。

Google API integration for blog performance: PageSpeed Insights, CrUX Core Web Vitals with 25-week history, Search Console performance, URL Inspection, Indexing API, GA4 organic traffic, NLP entity analysis for E-E-A-T, YouTube video search for embedding, and Google Ads Keyword Planner. Progressive feature availability based on credential tier (API key, OAuth/service account, GA4, Ads). Shares config with claude-seo at ~/.config/claude-seo/google-api.json. Use when user says "google data", "page speed", "core web vitals", "search console", "indexation", "GA4", "keyword research", "nlp entities", "blog performance", "youtube search", "google api 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。

review-analyzer-skill是由buluslan（公众号：新西楼.AI）研发的电商评论深度分析Skill（VOC 客户之声分析），他会帮你把一堆产品评论变成一份能落地的决策依据——22 维 AI 打标提炼用户画像、痛点和真实需求，输出 15 章深度洞察报告和可视化看板，从差评里挖出产品优化机会点、从好评里找到爆款基因。 更多跨境电商 AI 实战内容，请关注公众号「新西楼.AI」。 Deep-dive e-commerce review & VOC analysis: 22-dimension AI tagging, 15-chapter insight report with anomaly signal cards, 6 themed visualization dashboards, CSV-primary multi-source input (SellerSprite optional), Feishu sync. 当用户需要以下功能时触发：分析电商产品评论（Amazon等平台）/ 从评论中提取用户画像、痛点和VOC（客户之声）/ 生成产品洞察报告和机会点分析 / 创建可视化分析看板 / 通过卖家精灵获取评论数据（可选增强；CSV为主源）/ 将分析结果同步到飞书文档。 触发关键词：电商评论分析、评论分析、竞品分析、用户洞察、VOC分析、产品优化、市场调研、评论数据挖掘、卖家精灵、sellersprite。 NOT for 关键词调研/选品雷达类任务（那些有专门工具）。

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Read your X (Twitter) audience and niche from real data. Pull a handle's recent tweets (yours or a competitor's) with likes, replies, and views, see which formats and hooks are working, read the repliers on a tweet (X gates likers, so repliers are the signal), and scan a niche query for top tweets. Powered by Apify, no login. Triggers on "analyze my tweets", "what is working on X", "read the replies", "competitor tweets", "who is engaging". Not for writing a tweet (use x-post-writer).

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。

Summarize meetings using IDEAS framework - Insights, Decisions, Engagements, Actions, Summary. Use after any meeting.

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。

Generates a sprint or milestone retrospective by analyzing completed work, velocity, blockers, and patterns. Produces actionable insights for the next iteration.

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。

Collecting user feedback via NPS surveys, review analysis, sentiment analysis, feedback classification, and insight extraction reports. Use when establishing feedback loops.

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 Overview (AI Mode) scraping via Google Search. Returns AI-summarized key points for a single keyword, ideal for deep research, technical Q&A, long-tail product selection, and cross-border consumer preference analysis using the latest web information. Single-round only; follow-ups require the agent to summarize context and issue a new request. Triggered by: Google AI, AI Overview, AI Mode, Google AI search, AI search, deep research, consumer preference analysis, web summary, long-tail product research, cross-border market insights.

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Nexscope Ozon Russia category drill-down product list by Russian category path. Returns all products in a category with complete metrics: sales, revenue, price, rating, stock, turnover, lost revenue, supporting multi-dimensional numeric filters, sorting, and currency conversion. Use for category bestseller mining, blue-ocean insight discovery, category ranking analysis, brand landscape observation. Trigger when the user mentions Ozon category drill-down, Ozon category products, Ozon blue-ocean mining, Ozon category bestsellers, Ozon category ranking, Ozon subcategory structure, Ozon niche SKUs, Nexscope category, Ozon category drill-down, Russian marketplace niche, Ozon niche mining, Ozon subcategory bestseller. Also trigger when the intent is to view all products and their sales/price/ranking performance under an Ozon category path, even without explicitly mentioning Nexscope.

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。

Retrieve Amazon Ads Sponsored Products audience and search-term impression-share reports through Nexscope.

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Amazon reverse product selection: filter Amazon niches and keywords by 30+ business dimensions (market size & growth, price tiers & share, competition density & top concentration, demographics such as age/gender/income, review highlights & pain points) from a metrics pool aggregated from historical business insight reports. Trigger when users mention reverse product selection, metrics filtering, niche reverse lookup, blue ocean niche discovery, low-competition niche, newcomer-friendly niche, brand-fragmented market, pain-point entry, feature reverse lookup, pricing tier opportunity, demographic-based selection, or similar terms. Even if the user does not explicitly say "reverse product selection," trigger this skill whenever the request involves filtering Amazon niches that match specific business 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。

Fetch and analyze Amazon product reviews by ASIN, supporting 15 marketplaces (including US) with star rating filtering. Triggered when users mention Amazon reviews, US reviews, product reviews, buyer complaints, negative reviews, positive reviews, star ratings, review analysis, review sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback. Even if users do not explicitly say "reviews", this skill should be triggered whenever the task involves reading, filtering, or analyzing Amazon product customer reviews.

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。