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概要と使いどころ
Use when implementing Kafka, RabbitMQ, Pulsar, or JMS producers and consumers in Spring Boot 4. Covers modular starters, event contracts, idempotency, retries, dead letters, and outbox delivery.
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Use when implementing Kafka, RabbitMQ, Pulsar, or JMS producers and consumers in Spring Boot 3. Covers event contracts, idempotency, retries, dead-letter handling, ordering, and outbox delivery.
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 evolving aggregate invariants, value objects or domain events in an existing DDD-style Spring Boot 4 application, or when DDD is explicitly requested. Preserve existing API identifiers and architecture when changing domain behavior.
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Use when evolving aggregate invariants, value objects or domain events in an existing DDD-style Spring Boot 3 application, or when DDD is explicitly requested. Preserve existing API identifiers and architecture when changing domain behavior.
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。
Designs and develops specs following the spec-driven method. Asks clarifying questions before proposing structure, and builds the spec section by section. Use it when starting a large feature, before writing code.
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Guides privacy program effectiveness measurement including leading and lagging indicators, KPI definition, benchmarking methodology, executive reporting formats, board-level privacy dashboards, and metric-driven program improvement. Covers operational, compliance, risk, and strategic privacy metrics across the program lifecycle. Keywords: privacy metrics, KPIs, benchmarking, executive reporting, dashboard, program effectiveness.
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Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas. Covers automated field population, data flow discovery, and system-to-RoPA mapping. Activate for automated RoPA, system inventory, data discovery, auto-population, IT-driven records.
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Managing privacy risks from AI-driven inferences about individuals including derived data classification, profiling under GDPR Art. 22, inference accuracy obligations, and controlling automated personality/behaviour predictions. Keywords: AI inference, derived data, profiling, automated predictions, GDPR.
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Analyzes an existing codebase using strategic Domain-Driven Design to discover bounded contexts, surface ubiquitous language and semantic collisions, find places where code boundaries diverge from domain boundaries, and produce an evidence-backed domain and context map. Use when the goal is DDD-specific: bounded context discovery, ubiquitous language analysis, domain model discovery, or mapping contested ownership and responsibility. The entire repository is a valid scope; no module or directory must be named first. Does not assess architectural coupling, cohesion, or technical debt — use architectural-analysis. Does not design service communication, integration patterns, or deployment topology — use architectural-analysis or plan-a-feature. Does not refactor code, diagnose bugs, or review code quality — use refactor, investigate, or code-review.
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Plans an architecture-driven change to code that already exists: module boundaries, type responsibilities, layering, coupling, and the public surface a revision moves. Produces a buildable change plan that names the types, modules, and methods involved and records the surface delta each change makes. Use when the user wants to plan, scope, or sequence a restructure, extraction, split, consolidation, responsibility shift, or API revision of existing code, including "these responsibilities are wrong, plan the fix". Does not assess an area and stop at findings — use architectural-analysis. Does not specify new behavior — use plan-a-feature. Does not diagnose a bug — use investigate. Does not write the code — use refactor to restructure it or tdd to build 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。
Narrative-driven animation skill for explanatory content and visual storytelling. **Triggers when:** - User wants to explain a concept or process - Request involves step-by-step demonstrations - Content is educational or tutorial-like - User mentions "explain", "show how", "demonstrate", "walk through" **Capabilities:** - Breaking concepts into digestible visual steps - Progressive revelation of information - Highlighting cause-and-effect relationships - Building intuition through visual metaphors
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Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.
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Ask Grok or Gemini via opencli (browser-session based, no API cost). Use when the user says "问 grok", "问 gemini", "ask grok", "ask gemini", "用 grok 查", "grok 怎么看", "让 gemini 分析", or wants a second opinion from Grok/Gemini without paying API tokens. Routes to opencli which drives the already-logged-in Chrome session.
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Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.
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This skill channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.
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Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
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。
Master metrics definition, KPI tracking, dashboarding, A/B testing, and data-driven decision making. Use data to guide product decisions.
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Analyzes social media campaign performance across platforms with engagement metrics, ROI calculations, and audience insights for data-driven marketing decisions
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Master product strategy, market analysis, competitive positioning, and long-term product vision. Define business models and craft go-to-market strategies that drive success.
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Master prioritization frameworks, roadmap planning, timeline estimation, and resource allocation. Create executable roadmaps that drive focus and alignment.
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Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.
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Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis
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Export, write, and manage Feishu/Lark cloud documents. Supports docx, sheets, bitable, wiki, WeChat article import/export, drive management, and browser-based export for public or browser-readable docs. Use this skill when you need to read, analyze, write, or manage content in a Feishu knowledge base.
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Fix Gorse recommendation engine failing to connect to ClickHouse Cloud with EOF errors. Use when: (1) Gorse pods crash with "failed to connect data database" and "EOF" error, (2) Using ClickHouse Cloud (*.clickhouse.cloud) as Gorse data store, (3) TLS connection works (verified with openssl) but Gorse still fails, (4) Tried secure=true, different ports (8443, 9440), timeout params without success. The Gorse ClickHouse driver is incompatible with ClickHouse Cloud's native protocol.
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。