claude_code_setup

Refactor Python code to match this repo's coding standards and style. Use when the user asks to "refactor", "clean up", "fix coding standards", or "apply best practices" to a module, file, or directory in this repo. Applies python-best-practices, pydantic v2, pytorch-lightning, and python-docs conventions as relevant to the target code.

Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial contexts.

English LaTeX academic paper assistant for existing `.tex` projects. Use this skill whenever the user wants to compile, lint, audit, or improve an English LaTeX conference or journal paper such as IEEE, ACM, Springer, NeurIPS, or ICML submissions. Trigger even when the user only mentions one paper issue, such as bibliography errors, grammar cleanup, sentence splitting, logic review, expression polishing, translation, title optimization, figure checks, pseudocode review, algorithm block cleanup, de-AI editing, or experiment-section review. Also trigger for "proofread my paper", "fix my LaTeX", "prepare for submission", "check my manuscript", "improve my writing", `algorithm2e`, `algorithmicx`, `algpseudocodex`, `Require/Ensure`, or "Algorithm 1" when the user has a .tex file.

Chinese LaTeX thesis assistant for existing .tex degree thesis projects (XeLaTeX/LuaLaTeX/latexmk). Use this skill whenever a user works on a Chinese master's or doctoral thesis needing compilation, GB/T 7714 bibliography checks, chapter structure mapping, template detection (thuthesis, pkuthss), terminology consistency, logic coherence review, heading lead-in checks, title optimization, de-AI editing, or experiment chapter review. Trigger even for single issues like "帮我编译论文", "检查国标格式", "看看绪论逻辑", "毕业论文", "学位论文", or "硕士/博士论文".

Unified paper audit for Chinese and English academic papers across LaTeX, Typst, and PDF formats. Use when reviewing paper quality, running pre-submission checks, simulating peer review, making pass/fail gate decisions, polishing writing, re-auditing revisions, or scoring with ScholarEval dimensions. Trigger when the user says "check my paper", "is this ready to submit", "score my paper", "review like a reviewer", "find missing references", "literature search", "re-audit", or wants a CI/CD quality gate. Do not use for direct source editing or compiling — use the format-specific writing skills instead.

Typst academic paper assistant for existing `.typ` paper projects in English or Chinese. Use this skill whenever the user wants to compile, audit, or improve a Typst paper, including format checks, bibliography validation for BibTeX or Hayagriva, grammar/sentence/logic review, expression polishing, translation, title optimization, pseudocode review, algorithm block cleanup, de-AI editing, or experiment-section review. Trigger even when the user only mentions one Typst file, one bibliography issue, one pseudocode block, or one section rewrite. Also trigger when the user mentions ".typ files", "typst compile error", "typst export", "typst bibliography", `algorithm-figure`, `lovelace`, or `algorithmic` even without saying the word "Typst" explicitly.

Common git operation patterns and commands. Use when working with git branches, commits, staging, rebasing, stashing, or any other git operations. Triggers include branching, committing, merging, rebasing, and syncing with remote repositories.

GitLab operations via glab CLI. Use when working with GitLab merge requests, issues, CI/CD pipelines, or repository operations. Triggers include creating/viewing/merging MRs, checking pipeline status, managing issues, and any GitLab-specific tasks.

Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation in FastAPI, Django, and configuration management. Use when working with API request/response validation, settings management, ORM model validation, data parsing and serialization, or type-safe data classes.

Create, update, and sync Python project documentation from source code. Use when asked to document a module, generate API references, create architecture diagrams with Mermaid, update docs after code changes, or keep documentation in sync with source. Triggers include "document", "write docs", "update docs", "sync docs", "architecture diagram", "module docs", "API reference", "document this module", "docs are outdated", or when code changes require corresponding documentation updates.

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

Best practices for Remotion - Video creation in React