bundled/skills/manuscript-as-code/SKILL.md
Treat manuscripts as software: version control, reproducible builds, figure pipelines, CI, and structured repo layout. Helps teams avoid 'final_v7' chaos and ensures submission-ready artifacts.
npx skillsauth add foryourhealth111-pixel/vco-skills-codex manuscript-as-codeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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把“写论文”升级成“交付可复现的出版工程”:
适用:
见 templates/repo-structure.md。核心思想:
manuscript/:只放正文与引用(source-of-truth)figures/:每张图一个目录(source + out)build/:所有生成物(可删除,可再生)submission/ 与 revision/:投稿与返修阶段产物figures/fig-02/src/plot.pyfigures/fig-02/out/fig-02.pdf最少要求:
推荐维护:
submission/submission-manifest.yml(统一记录规格与完成度)manubot/rootstock(论文的 CI/协作写作范式)greenelab/deep-review(评审/返修视角)development
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
development
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
development
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
development
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.