Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Build quick interactive HTML maps to inspect Earth Engine results. Use layered overlays on a satellite basemap with per-layer opacity, clear/show-all, and a click-to-get-coordinates tool. Use when you need to eyeball "where / what" (coverage gaps, disagreement, masks) when interacting with Earth Engine outside of the Code Editor or Colab.
React 无 useEffect 编码规范 - 禁止直接使用 useEffect，使用替代模式代替
Screenshot-to-HTML prototype generator with iterative refinement and learning memory. Use this skill whenever the user provides a screenshot, mockup, wireframe, or image of any UI and wants it reproduced as a working HTML prototype — or when they want to modify an existing prototype they previously generated. Also triggers on: "照这个做原型", "参考这个图", "把这个页面画出来", "this UI needs to be prototyped", "replicate this design", "convert this mockup to HTML", "帮我出个原型", "根据截图输出 HTML", or any image attachment combined with requests like "输出 HTML", "做成页面", "帮我实现". Even if the user just sends a screenshot with a brief instruction like "加一个字段" or "这个也一样", this skill applies — it means they want you to modify or replicate the UI shown. When in doubt, if there's a UI screenshot in the conversation, use this skill.
Clone live web pages into a local Next.js + Tailwind CSS project for rapid prototyping. Use this skill whenever a user shares a URL or screenshot and wants to replicate, redesign, or iterate on that page locally — whether they say "clone this site", "make a prototype based on this page", "recreate this UI", "build something that looks like this", or simply paste a link and ask you to turn it into code. Also trigger when the user wants to add new pages, modify existing pages, or extend a prototype project that was previously created by this skill. Even if the user just says "I want to build a page like X" with a reference link or image, this skill applies.
Use when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.
Use when choosing the solution route and delivering the algorithm section of a 《中国管理科学》 (Chinese Journal of Management Science) manuscript — exact vs heuristic vs learning-based, with pseudocode, property analysis, and baseline comparison. Covers solving the model; the experiments that showcase it belong to cjms-numerical-experiments.
Use when making a CoRL robot-learning paper reproducible — pinning simulator and driver versions, releasing training configs, demonstration data and checkpoints, documenting hardware setups that cannot be rerun, seed policy, evaluation scripts, and honest availability statements for code, data, and robot platforms.
Use when packaging code, models, datasets, or demo videos for a CVPR paper at either review time or release time, covering anonymous supplement packaging under the external-link ban, the dataset-release-by-camera-ready rule, model-weight and license decisions, and making a vision artifact runnable by a skeptical stranger.
Use when packaging the code, benchmarks, and flows behind an ACM/IEEE Design Automation Conference (DAC) Research Manuscript into a credible, reusable artifact — given that DAC has historically run no formal artifact-evaluation or badge-issuing track (verify per cycle), so the goal is reviewer credibility and community reuse via open EDA flows and DOI-archived releases, not an ACM badge.
Use when assembling the data and code replication package for a The Economic Journal (EJ) manuscript to the RES / EJ Data Editor standard (DCAS-endorsed, Zenodo deposit, reproducibility check before final acceptance). Builds the package and README; it does not run the analysis itself.