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概要と使いどころ
Run a learning retrospective on the Kai harness. Mines gate-failure logs and 30-day performance results into lessons, triages candidate lessons (promote/keep/retire), and graduates repeated lessons into enforced gate checks with golden corpus cases. Use when "retro", "what have we learned", "triage lessons", "promote lessons", "why does this keep failing", "harness retrospective", or monthly / after any heavy content sprint.
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 content performance patterns. Extract what works (hook types, formats, personas). Auto-update learned defaults so future content improves.
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。
Conversion rate optimization audit — analyze a landing page, signup flow, or checkout funnel using the 5-layer CRO stack (technical performance, traffic quality, offer/pricing, design/layout, copy/messaging). Produces prioritized fix list with expected impact. Use when "CRO audit", "conversion audit", "why isn't this converting", "improve conversion rate", "landing page not converting", "optimize funnel", "signup flow audit", or any request to diagnose and fix conversion problems.
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。
Pull performance data for published content across every surface — LinkedIn, Dev.to, Google Search Console, GA4, and AI citation tracking (Perplexity, Anthropic, OpenAI). Grades each piece as winner, average, or underperformer and surfaces whether an AI search engine has cited it.
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。
Plan, evaluate, and produce paid ad campaigns across platforms (Meta, Google, LinkedIn, TikTok, Microsoft, Pinterest, Snapchat, Amazon, X). Evaluate existing ads, map funnel stages (TOF/MOF/BOF), produce ad variants per platform with policy compliance, output ready-to-upload copy. Use when "ad campaign", "create ads", "run ads for", "paid campaign", "media plan", "launch ads", "Meta campaign", "Google Ads campaign", "multi-platform ads", "evaluate my ads", "how are my ads doing", "audit my ads", "ad performance", "analyze ads", or any request to evaluate existing or create new advertising.
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。
评估系统性能 / 容量 / 压测时使用。适用于上线前性能基线、SLA 验证、容量规划、性能退化排查。融合 Google SRE Workbook、Brendan Gregg USE Method、k6/JMeter 实践。
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。
优化前端性能 / 包大小 / 渲染 / 网络时使用。覆盖 Web Vitals (LCP/INP/CLS) / Bundle / Image / Code Splitting / 虚拟列表。融合性能预算 + RUM + 优化优先级。
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 starting a session and suspecting configuration issues, after hook failures, MCP connection errors, or degraded vault performance. Also useful as a periodic health audit.
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 Odoo addon code for correctness, security, performance, migrations, tests, and official Odoo coding guidelines. Use when reviewing Odoo modules, diffs, pull requests, or changed files involving models, fields, XML views, data, controllers, reports, OWL/assets, manifests, access rules, record rules, or OCA migrations.
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。
Odoo 18 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 18 or custom addons, including traceback diagnosis, addons, manifests, XML errors, HTTP controllers, business rules, feature work, refactoring, or 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。
Trace Odoo execution flow from an entry point through controllers, button actions, cron jobs, model methods, overrides, computes, onchanges, constraints, database operations, security checks, and side effects. Use before implementation, during debugging, impact analysis, performance review, or code review when behavior depends on Odoo call chains.
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。
Odoo 17 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 17 or custom addons, including traceback diagnosis, addons, manifests, XML errors, HTTP controllers, business rules, feature work, refactoring, or 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。
Generate A-share end-of-day market review reports with Pandadata data, covering trade-date checks, index performance and valuation, market breadth, limit-up/down sentiment, industries/concepts, 龙虎榜, block trades, margin financing, northbound holdings, risk notes, and optional scheduled after-close automation. Use when the user asks for 今日复盘, 收盘总结, 每日市场报告, A股复盘, 龙虎榜复盘, 北向资金动向, or to set up an automated daily market review.
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。
Run Squirrelscan from a coding agent to audit SEO, performance, security, accessibility, and agent-experience issues, then feed exact fixes into an agent repair loop.
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。
Bubble Tea is a powerful Go framework for building terminal user interfaces based on The Elm Architecture. Developed by Charmbracelet, it supports inline, full-window, and mixed TUI applications with a high-performance cell-based renderer and declarative views.
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。
Astro is a modern web framework for building content-driven websites. It ships zero JavaScript by default, supports multiple UI frameworks (React, Vue, Svelte, Solid), and provides islands architecture for optimal performance.
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。
Agno is a high-performance Python framework for building, running, and managing agentic software at scale. It provides a three-layer architecture — framework, runtime, and control plane — enabling developers to go from agent prototype to production API in roughly 20 lines of code.
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 LinkedIn performance and manage engagement through Publora. Use when the user asks how a LinkedIn post or account performed (impressions, reach, reactions, comments, reshares, follower growth) or wants to react, comment, reshare or resolve an @mention. Statistics run over the REST API, engagement over MCP tools. Not for creating posts (use linkedin-post).
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 SEO content strategy for SaaS and B2B sites. Use when doing keyword research, planning content clusters, creating pillar pages, optimizing on-page SEO, fixing technical SEO issues, building links, refreshing content, implementing programmatic SEO, mapping search intent, optimizing for SERP features, or measuring content performance. Use for SEO audits, content gap analysis, and organic growth 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。
Expert sales enablement strategist for building high-performing sales teams. Use when designing sales training programs, onboarding and ramp plans, sales playbooks, coaching frameworks, certification programs, or competitive intelligence distribution. Covers content strategy, tool adoption, performance measurement, and continuous learning systems. Use for building sales academies, creating enablement content, and optimizing sales productivity.
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。
Perform a rigorous Carmack Council code review. Use when explicitly asked to review code, do a "council review", "carmack review", or invoke /council-review. Carmack's philosophy chairs a council of domain experts — Troy Hunt (security), Martin Fowler (refactoring), Kent C. Dodds (frontend), Matteo Collina (Node.js), Brandur Leach (Postgres), Vercel Performance, Simon Willison (LLM pipelines), Karri Saarinen (UI quality), Vitaly Friedman (UX quality), Kent Beck (test quality). Uses parallel subagents for deep, independent review. Produces prioritised P1/P2/P3 findings. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.
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。
Architect a feature with the Carmack Council before writing code. Use when explicitly asked to plan a feature, do a "council plan", "carmack plan", or invoke /council-plan. Carmack's philosophy chairs a council of domain experts — Troy Hunt (security), Martin Fowler (refactoring), Kent C. Dodds (frontend), Matteo Collina (Node.js), Brandur Leach (Postgres), Vercel Performance, Simon Willison (LLM pipeline), Karri Saarinen (UI quality), Vitaly Friedman (UX quality). Interactive feature discovery followed by parallel subagent dispatch. Produces a sequenced, attributed implementation plan with no code. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.
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。
Produces a rigorous, forward-looking technical audit with severity ratings and improvement directions.
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。
Systematic debugging methods, log analysis, and performance diagnostics. Use when debugging issues, analyzing errors, or troubleshooting incidents.
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。