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Use when implementing delays that must survive process restarts (e.g., 24-hour cart abandonment, scheduled follow-ups), waiting for human approval or external events with timeouts (review gates, webhook callbacks, async API completion), polling external services without losing state on crashes, calling other functions and awaiting their results, memoizing expensive operations so they don't re-run on retry, or running async work in parallel inside a workflow. Covers Inngest step methods: step.run, step.sleep, step.waitForEvent, step.waitForSignal, step.sendEvent, step.invoke, step.ai, plus patterns for loops and parallel execution.

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Implement or update iOS SwiftUI UI from Figma designs; also use for implementation plans, token mapping, or asset export for that UI.

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。

Design a conversion loop for a one-person company from reach to lead capture to purchase. Use when Codex needs to explain conversion concepts when needed, verify MVP prerequisites, ask one question at a time, present multiple conversion-path options, and write user-confirmed outputs into `opc-doc/`.

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。

Define the smallest viable experiment and MVP for a selected one-person company opportunity. Use when Codex needs to explain what MVP means when needed, verify prerequisites, ask one question at a time, present multiple MVP options, and write user-confirmed outputs into `opc-doc/`.

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。

Design value propositions for candidate customer segments and help the user choose the strongest one. Use when Codex needs to explain jobs, pains, and gains when needed, check niche-positioning prerequisites, ask one question at a time, present multiple value-proposition options, and write user-confirmed outputs into `opc-doc/`.

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。

社媒互动增长专家团信号挖掘专家（麦恩 · Mine）。负责从社媒评论和内容中识别高转化信号，提取销售线索并进行分级，执行多平台社媒数据爬取，分析竞品内容策略与爆款规律，捕捉趋势信号。 触发词：由 social-engagement-team-lead 主理人调度执行 Phase 4 信号挖掘任务时激活。不直接面向用户。

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 stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

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。

MVP 开发专家团 UI/UX 设计师（颜好看）。擅长 67 种 UI 风格、161 色调色板、57 种字体配对。输出完整设计系统 Token 和页面设计提示词。 触发词：由 mvp-dev-team-lead 主理人调度执行 Phase 1 调研或 Phase 2 设计细化任务时激活。不直接面向用户。

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。

设计系统专家 - 从 71 套品牌级设计系统匹配最佳方案，定制设计令牌和品牌规范

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。

设计原型专家团主理人 - 编排 5 位专业角色按 SOP 完成品牌级设计产出

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可视化分析设计师（达仕 Dash）。使用 ECharts/Chart.js/D3.js/Plotly 等工具设计交互式图表和数据仪表盘；构建数据画像可视化、KPI 卡片布局和叙事性数据可视化。 触发词：由 copilot-team-lead 主理人调度执行，不直接面向用户。当主理人下发 Phase 3 可视化任务时激活。也可响应纯可视化需求。

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。

智数分析专家团主理人（诺亚 Nova · 首席数据编排师）。协调 5 位专业成员（sql-analyst、data-scientist、knowledge-researcher、dashboard-designer、report-composer）按预设 Workflow 完成端到端数据分析交付。 触发词："分析数据"、"生成报告"、"数据画像"、"查数据库"、"数据分析"、"帮我分析"、"数据可视化"、"仪表盘"。 路由逻辑：单一维度任务走单 agent 直调；综合性任务走对应 Workflow。

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Dark web intelligence collection methodology — vendor-first access posture, sourced reference lists for 35+ underground forums and 30+ Telegram channels, OPSEC primer, passive-monitoring strategy, and bundled Python CLIs for onion-indexer search, Telegram channel monitoring, and local keyword matching. Use when the user wants to design or run dark-web collection, build a selector list, pick a vendor, or set up monitoring infrastructure.

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 assigning a confidence level to an analytical judgment, the user asks "how confident are we?" / "what is the confidence on X?", or the orchestrator's tradecraft pipeline calls for a confidence level before publishing. Provides three named levels (High, Moderate, Low) with 0-100 score ranges aligned to STIX, and the rule that confidence cannot exceed what the weakest load-bearing claim supports.

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Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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 robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.

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, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.

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 specialist. Optimizes onboarding, paywalls, and signup flows.

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。

Design, validate, and optimize schema.org structured data for eligibility, correctness, and measurable SEO impact.

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。

Design, implement, and audit PFC3D 5.0 asphalt wheel-tracking and intersection-rutting simulations using RVE-to-strip-to-full-size scaling, PFC5-compatible vector reaction control, Burger-calibrated contacts, vertical-horizontal coupled loads, rut/shear histories, edition-specific metrics, and explicit surrogate validation.

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。

Take one new feature slice from idea to reviewed, committed code in a single guided pass — scope it into the smallest shippable slice, build it test-first with strict TDD, review and fix the diff, then commit it. Chains slice → test-driven-development → the built-in /code-review → git-commit. Not for reviewing an existing PR, upgrades, or open-ended design questions.

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。

Walk through the Designer/Developer wrap-up checklist for offboarding a client engagement — conversationally, one item at a time.

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。

houtu-dependencies enterprise-grade Spring Cloud microservice foundational framework complete usage guide. GroupId is io.github.lujiafa, covering unified response, exception handling, parameter parsing, session authentication, permission control, signature verification, anti-replay, distributed lock, rate limiting, database field encryption, config decryption, access logging, canary routing, weighted load balancing, Feign enhancement, Sentinel circuit breaking, service discovery, Swagger documentation, crypto utilities, HTTP client, monitoring metrics, and other enterprise-grade capabilities. When the project build file (pom.xml or build.gradle/build.gradle.kts) contains io.github.lujiafa or houtu-related dependencies, or when the user mentions houtu or houtu-dependencies, this Skill must be read and code must be generated strictly following framework conventions. Even if the user is simply doing regular Spring Boot development (e.g., writing Controller, Service, Feign), as long as the project includes houtu, the houtu approach must be used instead of native Spring. When the user explicitly wants to use the houtu framework and the project has not yet included it, proactively add the BOM and required Starters; when the project already includes it, proactively identify and import required module dependencies based on business scenarios during coding, naturally integrating framework capabilities to enhance business services based on business semantics, rather than waiting for the user to specify each one.

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"Apply the Technology Acceptance Model (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) to predict technology adoption. Use this skill when the user needs to evaluate user acceptance of a new system, diagnose adoption barriers, design interventions to improve technology uptake, or when they ask 'why aren't users adopting this', 'what drives technology acceptance', or 'how do we increase adoption rates'.".

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。