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概要と使いどころ
Expert guidance for Evidence, the open-source BI framework that generates beautiful, interactive dashboards from SQL queries and Markdown. Helps developers build data reports as code, deploy them as static sites, and create self-service analytics without heavy BI tools.
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。
Creates interactive data visualizations with Apache ECharts, the open-source JavaScript charting library. Use when a user asks to build charts, dashboards, or data-driven graphics using ECharts in React, Vue, or vanilla JavaScript applications, render a chart to SVG on the server, or upgrade from ECharts 5 to 6.
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。
dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.
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。
Mask, redact, and anonymize sensitive data (PII, PCI, PHI) in databases, logs, and APIs. Use when protecting PII in dev/staging environments, redacting sensitive data from logs, anonymizing data for analytics, or applying k-anonymity and differential privacy for GDPR-compliant data sharing.
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 guidance for Apache Arrow, the cross-language columnar memory format for analytics workloads. Helps developers use Arrow for high-performance data interchange between systems, zero-copy reads, and efficient columnar processing in Python (PyArrow) and JavaScript (Arrow JS).
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。
World-class marketing expertise combining Seth Godin's permission marketing philosophy, Neil Patel's data-driven growth tactics, and the strategic frameworks from brands like Apple, Nike, and Dollar Shave Club. Marketing is the art and science of creating customers. Great marketing isn't about shouting louder - it's about being more relevant. The best marketers understand that attention is earned, not bought. They build systems that compound, create word-of-mouth, and turn customers into advocates. Marketing is the bridge between what you've built and the people who need it. Use when "marketing, campaign, go-to-market, gtm, launch, promotion, advertising, ads, acquisition, demand gen, lead gen, channel, funnel, conversion, cac, customer acquisition, reach, awareness, consideration, marketing, advertising, acquisition, campaigns, gtm, demand-generation, analytics, channels" mentioned.
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 Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.
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 community tool selection and implementation - Discord bots, community platforms, analytics tools, CRMs, moderation systems, and automation. Knows the tool landscape, integration patterns, and how to build a cohesive community tech stack. Use when "community tools, discord bots, community platform, community crm, moderation bots, community automation, orbit, common room, commsor, " mentioned.
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。
UTM parameter strategy, campaign tracking, and marketing attribution modeling. Build consistent UTM taxonomies, configure attribution models, measure cross-channel performance, and connect marketing spend to revenue. Use when the user asks about UTM parameters, campaign tracking, attribution models, marketing measurement, or cross-channel analytics.
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。
Looker Studio (formerly Google Data Studio) expertise. Build dashboards, design data visualizations, connect data sources, and create marketing reports. Use when the user asks about Looker Studio, Data Studio, marketing dashboards, data visualization, report building, or connecting analytics data sources.
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。
Shopify e-commerce platform marketing expertise. Audit store performance, optimize conversion funnels, configure tracking, and integrate marketing tools. Use when the user asks about Shopify, e-commerce optimization, Shopify analytics, store conversion, product feeds, or Shopify app integrations.
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。
Manage Buffer content via the GraphQL API. Use when creating, scheduling, editing, or deleting posts, saving ideas, reading scheduled queues, or pulling post analytics. Not for general API debugging.
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。
独立开发者与微型 SaaS — 单人或极小团队 (≤3 人) 构建可持续订阅收入的软件产品商业, 有别于自由职业/咨询、企业级 SaaS 和开源维护: (a) 产品发现与验证 (自己的痒 vs 市场优先 Nugent/Walling; 着陆页冒烟测试; 先接 Stripe 再写代码; JTBD 访谈适配独立开发者场景; Reddit/HN/X/社区痛点挖掘; Mom Test 验证框架; 公开构建作为验证机制); (b) 独立开发者技术架构 (无聊技术论 McKinley; 单体优先; serverless vs VPS 在 ≤$100/月预算下的取舍; Rails/Django/Laravel/Next.js 等框架的出货速度选型; 托管服务优先于自建; Supabase/PlanetScale/Neon 数据库即服务; Clerk/Auth0 认证即服务; Stripe/Paddle/LemonSqueezy 支付; Vercel/Fly.io/Railway 部署; AI 辅助编码 Cursor/Copilot 作为力量倍增器); (c) 无营销团队的分发与增长 (SEO 作为微型 SaaS 护城河; Product Hunt 发布; AppSumo 终身授权利弊; 冷邮件; Twitter/X 公开构建; IndieHackers 社区分发; 集成市场 Shopify/WordPress/Zapier/Slack 应用目录; 联盟计划; 一人内容营销); (d) 定价与变现 (SaaS 定价心理学; freemium vs 免费试用 vs 纯付费; 按席位 vs 按用量 vs 固定费率; 年付折扣; 老用户保价; 微型 SaaS 流失率控制; MRR/ARR/LTV/CAC 在微型规模的含义; $10K MRR 里程碑心理学); (e) 独立创始人心智模型与生活设计 (default alive vs default dead Graham; 拉面盈利 Levels; 生活方式生意 vs 增长生意的光谱; 独立开发者倦怠预防; 时间管理与上下文切换成本; 地理套利与远程优先; 一次构建反复销售的资产思维; 社区作为支持网络 IndieHackers/WIP/MicroConf); (f) 法务与运营基础 (公司注册地选择 LLC/Ltd/GmbH 税效; Stripe Atlas vs Firstbase vs 本地律师; 服务条款与隐私政策; GDPR/CCPA 低成本合规; 知识产权基础; 首次雇人的承包商 vs 员工选择; 独立开发者会计 SaaS — Xero/QuickBooks; 多辖区税务义务)。中国独立开发者生态: 出海作为主导策略 (从中国构建面向全球市场的产品以避开国内竞争/监管); 即刻/V2EX/少数派作为社区枢纽; 微信小程序作为替代分发; 小报童/爱发电作为中文市场变现; 人民币定价心理学; 支付宝/微信支付集成; ICP 备案要求; 中国区 App Store 审核差异; 独立开发者周刊。诚实处理: 生存偏差 (每一个 Pieter Levels 背后是数千个从未达到 $1K MRR 的人), AI 对微型 SaaS 生态位的颠覆 (wrapper 产品脆弱), 独自构建的孤独与心理健康成本, 公开构建的表演性质 vs 真正透明, AppSumo 终身授权上瘾毁坏单位经济, 样板代码销售的伦理边界。不含: 企业级 SaaS (不同 GTM/销售/融资模型), 不含: 自由职业/咨询 (用时间换钱非建资产), 不含: 开源维护 (不同可持续性模型), 不含: 风投支持的创业 (不同增长期望), 不含: 无代码/低代码平台构建 (相邻但不同受众), 不含: 内容创作作为主业。 (Independent Developer and Micro-SaaS — the craft of single-person or tiny-team (≤3 people) software product businesses that generate sustainable recurring revenue without venture capital, distinct from freelancing/consulting, enterprise SaaS, and open-source maintainership: (a) product discovery and validation (scratching your own itch vs market-first Nugent/Walling; landing page smoke tests; Stripe checkout before code; Jobs-to-Be-Done interview adapted to solo context; Reddit/HN/X/community pain-point mining; idea validation frameworks Fitzpatrick Mom Test; build-in-public as validation mechanism); (b) technical architecture for solo operators (boring technology thesis Mckinley; monolith-first; serverless vs VPS tradeoffs for ≤$100/mo infra; framework selection for shipping speed — Rails/Django/Laravel/Next.js/Remix/SvelteKit; managed services over self-hosted whenever possible; database-as-a-service Supabase/PlanetScale/Neon/Turso; auth-as-a-service Clerk/Auth0/Supabase Auth; payment integration Stripe/Paddle/LemonSqueezy; deployment Vercel/Fly.io/Railway/Render; monorepo vs polyrepo for solo; AI-assisted coding Cursor/Copilot as force multiplier); (c) distribution and growth without a marketing team (SEO as moat for micro-SaaS Ahrefs-style content marketing on 1-person budget; Product Hunt launches; AppSumo lifetime deals tradeoffs; cold email outreach; Twitter/X building in public; IndieHackers community distribution; integration marketplaces Shopify/WordPress/Zapier/Slack app directories as channels; affiliate programs; content marketing at scale-of-one); (d) pricing and monetization (SaaS pricing psychology Patel/Campbell; freemium vs free trial vs paid-only; per-seat vs usage-based vs flat-rate; annual discount strategies; grandfathering existing customers; churn reduction for micro-SaaS — Baremetrics/ProfitWell/ChartMogul analytics; MRR/ARR/LTV/CAC at micro scale; the $10K MRR milestone psychology); (e) solo founder mental model and lifestyle design (default alive vs default dead Graham; ramen profitability Levels; lifestyle business vs growth business spectrum; burnout prevention for solo operators — no team to delegate to; time management and context-switching costs; geographic arbitrage and remote-first; the 'build once sell twice' asset mindset; community as support network IndieHackers/WIP/MicroConf); (f) legal and operational basics (incorporation LLC/Ltd/Gm
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。
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
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 the user wants to inspect the real live GTM runtime before schema generation or compare multiple live GTM containers.
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 the work starts from `site-analysis.json` and the user wants page-group authoring, grouping adjustments, or page-group approval.
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 the user wants crawl coverage, platform detection, dataLayer discovery, or a fresh artifact directory before grouping and schema work.
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 the platform is Shopify or the run needs the Shopify-specific schema, sync, install, or verification branch.
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 the user wants schema preparation, event design, selector validation, schema review, or event-spec 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。
Use when the user wants preview QA, failure interpretation, release readiness, or an explicit publish handoff.
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 the user wants GTM-ready config generation, GTM workspace sync, or container selection from an approved schema.
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 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。
Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".
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 plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
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。