Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Detect duplicate MoonBit code using AST-based similarity analysis. Use when working with .mbt files and looking for code duplication, refactoring opportunities, or enforcing code quality.
Detect duplicate Python code using AST-based similarity analysis. Use when working with .py files and looking for code duplication or refactoring opportunities.
Detect duplicate Rust code using AST-based similarity analysis. Use when working with .rs files and looking for code duplication or refactoring opportunities.
Use when changing AtomUI or Avalonia resource bindings, DynamicResource, TokenResourceBinder, non-Visual AvaloniaObject lifecycle, IResourceHost/IThemeVariantHost, owner/container attach cleanup, or investigating memory leaks and retained controls.
Perform structured code reviews of C# source code covering naming conventions, performance, security, readability, and .NET best practices. Trigger phrases include "review this C# code", "check my C# for best practices", "analyze this C# class", "find issues in my C# code".
Use when building the replication package for an American Journal of Political Science (AJPS) manuscript. AJPS is famous for MANDATORY third-party PRE-publication verification — an independent verifier re-runs your deposited code and confirms it reproduces the numerical results in the main text before the article is published, with materials deposited to the AJPS Dataverse on Harvard Dataverse. Prepares the package; it does not waive requirements.
当你在为《自动化学报》(Acta Automatica Sinica, AAS) 提升实验可复现性、组织复现包、防范数据污染时调用。覆盖环境与依赖固定、随机种子与多次运行、数据划分与防泄露、控制仿真的求解器/步长/系统参数记录、超参数与调参协议披露、以及配合双盲的匿名化复现材料，帮助控制/自动化/模式识别方向的中文稿在 Acta Automatica Sinica 外审中经得起"结果能否复现"的追问。
Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper consistency.
Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.
Use when strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code/data release statements, and checklist-to-claim consistency audits.