Use when assessing how well a survival model's predicted probabilities agree with observed outcomes by fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file. NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis.

Acknowledges limitations in sample, design, measurement, and validation in a professional way that improves credibility without undermining the whole paper. Use when writing the limitations paragraph of a Discussion section, preparing a grant risk assessment, responding to reviewers about study weaknesses, or framing scope boundaries for a paper. Also triggers on "write my limitations", "how should I address the limitation of", "reviewer said my sample is too small", or "help me word this limitation".

使用独立 Research Core 把模型比较、软件评估或其他可证伪问题转化为可恢复、可复现、证据可追溯的研究。 仅用于需要真实执行对照实验的场景：模型/方案/系统的实证对比、基线比较、模型替代评估、消融、盲评、可复现实验。 不用于简单事实查询、只读审计/盘点/调查（即使涉及"证据"）、无需实验的低风险判断，或用户只要求普通文案/解释的场景。

从源仓库向其他设备或用户级 Skill 目录恢复已登记的 Codex Skill，提供只读审计、显式应用、SHA-256 校验，并且不删除目标端额外文件。用于复现 Codex 工作环境、比较已安装 Skill 与备份仓库，或准备安全的跨设备恢复。

系统化问题解决与优化流程（领域无关）：量化基线→全量列问题→根因分类→结构性方案（拒绝临时补丁）→借鉴同类方案→归纳取舍→展示计划确认实施→实施→验证生效→度量闭环。核心原则：规则/流程存在≠被执行，能落到"无法绕过的机制"就不写建议；优化必须可度量。 用于用户要求"优化""改进""复盘""为什么反复出问题""效率低/成本高/质量差/总是复发"等任何领域的系统性改进，或接手反复失败的任务时。 不用于单点小修复（用 minimal-implementation）；不用于只读审查不出方案的场景。

审计、验证并安全同步 Codex Skill 仓库：检查包结构、manifest 和元数据，识别私有或运行时文件，审查目录组织，并将已登记的包显式同步到其他设备。用于维护 Skill 备份仓库、检查 Skill 是否完整、准备发布，或比较源仓库与已安装 Skill 目录。

Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.

Authors Apache Airflow DAGs declaratively from dag-factory YAML configs. Use when building DAGs declaratively from YAML via dag-factory; creating/editing dag-factory templates/YAML configs,reating/editing dag-factory YAML configs, defaults, dynamic tasks, datasets, or callbacks; or validating dag-factory configurations; upgrading or re-pinning dag-factory.

Use when building real-time communication systems with WebSockets or Socket.IO. Invoke for bidirectional messaging, horizontal scaling with Redis, presence tracking, room management.

Builds Vue 3 components with Composition API patterns, configures Nuxt 3 SSR/SSG projects, sets up Pinia stores, scaffolds Quasar/Capacitor mobile apps, implements PWA features, and optimises Vite builds. Use when creating Vue 3 applications with Composition API, writing reusable composables, managing state with Pinia, building hybrid mobile apps with Quasar or Capacitor, configuring service workers, or tuning Vite configuration and TypeScript integration.

Writes and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and integrations on the Salesforce platform. Use when developing Salesforce applications, customizing CRM workflows, managing governor limits, bulk processing, or setting up Salesforce DX and CI/CD pipelines.

Writes, reviews, and debugs idiomatic Rust code with memory safety and zero-cost abstractions. Implements ownership patterns, manages lifetimes, designs trait hierarchies, builds async applications with tokio, and structures error handling with Result/Option. Use when building Rust applications, solving ownership or borrowing issues, designing trait-based APIs, implementing async/await concurrency, creating FFI bindings, or optimizing for performance and memory safety. Invoke for Rust, Cargo, ownership, borrowing, lifetimes, async Rust, tokio, zero-cost abstractions, memory safety, systems programming.

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.

Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.

Use when building Next.js 14+ applications with App Router, server components, or server actions. Invoke to configure route handlers, implement middleware, set up API routes, add streaming SSR, write generateMetadata for SEO, scaffold loading.tsx/error.tsx boundaries, or deploy to Vercel. Triggers on: Next.js, Next.js 14, App Router, RSC, use server, Server Components, Server Actions, React Server Components, generateMetadata, loading.tsx, Next.js deployment, Vercel, Next.js performance.

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.

Use when building, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources. Invoke to implement tool handlers, configure resource providers, set up stdio/HTTP/SSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server/client projects using TypeScript or Python SDKs.

Use when designing GraphQL schemas, implementing Apollo Federation, or building real-time subscriptions. Invoke for schema design, resolvers with DataLoader, query optimization, federation directives.

Use when building C# applications with .NET 8+, ASP.NET Core APIs, or Blazor web apps. Builds REST APIs using minimal or controller-based routing, configures database access with Entity Framework Core, implements async patterns and cancellation, structures applications with CQRS via MediatR, and scaffolds Blazor components with state management. Invoke for C#, .NET, ASP.NET Core, Blazor, Entity Framework, EF Core, Minimal API, MAUI, SignalR.

Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.