Claude Skills by gabrielmoreira

扮演学术写作专家，根据用户需求对文本进行润色、改写、总结或扩写。支持调整风格、字数及严谨度，严格输出最终结果。

Translates Simplified Chinese text from a Pokémon novel into English, ensuring clarity for non-Chinese speakers while strictly applying official Pokémon franchise terminology and character names.

解析中文业务查询语句，提取时间、实体、维度、指标等核心要素，并将其转换为结构化的依存关系字符串或LISP风格的广义表（语法树）表示。

General SOP for common requests related to conversation, evidence, full.

Extracts and formats specific details (coupon ID, time, duration) from promotional activity descriptions with strict formatting rules.

用于处理多套餐叠加、跨月及任意账期的流量计费逻辑，包含状态机转换、偏移量计算、余量公式及性能优化策略。

General SOP for common requests related to developer, mode, it.

Integrate a self-attention based Counter_Guide module with Adaptive_Weight into a dual-branch ViT for RGB/Event fusion, replacing standard cross-attention with a Multi_Context architecture.

从Excel读取中文文本，支持基于句法树结构（如深度、词性）或语义向量（如TF-IDF、SentenceTransformer）的特征提取，执行聚类分析（如K-Means、DBSCAN），并将结果导出回Excel。

An iterative prompt engineering assistant that optimizes user prompts using role, background, and constraints, providing comparative analysis and educational feedback to help users master prompt engineering.

General SOP for common requests related to gr, gradio, file.

专注于Gradio及Hugging Face空间的Dockerfile配置与优化顾问，基于Python 3.10和阿里云镜像源生成或修改Dockerfile，解决部署问题。

指导在MATLAB中执行矩形脉冲信号的理想冲激抽样、频域分析、低通滤波及信号恢复，包含寻找频谱极小值fm及完整的可视化流程。

根据用户提供的画面描述或关键词，生成包含详细描述段落和简化关键词列表的高质量Midjourney英文提示词，并附带标准参数。

General SOP for common requests related to motorcontrol, temp, motor.

General SOP for common requests related to node, value, binarytree.

从OCR识别后的医疗单据文本中提取日期、医生姓名、患者姓名、诊断和总消费金额，具备文本矫正和关键字识别能力，并以JSON格式输出。

General SOP for common requests related to os, asyncio, path.

Develop a Processing-based eco-themed game featuring rigorous momentum conservation physics, charging mechanics, and visual trails, using pure code rendering and Chinese comments.

SOP for extracting and analyzing promotional activity details, handling time formatting, location mapping, payment logic, and step-by-step process validation.

使用mplfinance生成黑色背景的专业K线图，支持白灰涨跌配色、数据自动处理及预测信号叠加。

Advanced Python visualization expert supporting Pandas DataFrames and raw data. Handles dynamic single/dual-axis plotting, Chinese font configuration, Tensor conversion, and safe file saving without display.

Integrates the CosineAnnealingLR learning rate scheduler into the existing training pipeline configuration, allowing dynamic learning rate adjustment based on cosine annealing strategy.

Guides the user on adding a new TEXT column to a PostgreSQL table using SQLx migrations and backfilling existing rows with nanoid values, specifically targeting rows where the column is NULL.

General SOP for extracting and structuring processes or checklists from conversation evidence.

SOP for PyTorch setup involving specific input splitting logic and standard library imports.

General SOP for common requests related to self, current, if.

General SOP for common requests related to diffusion, 2023, stable.

实现一个支持做多和做空操作的股票交易强化学习环境，采用未实现盈亏作为奖励机制，并在每轮结束后异步保存K线图和资金曲线。

General SOP for common requests related to sugar, can, mg.

针对数据科学、互联网及游戏行业，提供中英文简历的专业翻译与润色服务。结合HR视角、技术术语规范及特定行业词汇表，确保表达精准、符合招聘标准及目标语言阅读习惯。

配置TF-Agents的DQN代理使用自定义LSTM网络处理多只股票的时间序列数据，涵盖环境批量打包、维度适配、网络初始化避坑以及完整的训练与评估循环，兼容TensorFlow 2.10.1。

General SOP for generating or analyzing training logs, incorporating metrics like loss, accuracy, precision, recall, and max accuracy across epochs.

Specializes in creating TikTok short video scripts using an anthropomorphic 'Chef' perspective to explain wildlife food chains and survival environments, utilizing real field footage.

Executes a 1-back working memory test protocol by comparing the current input in a sequence to the previous one, stating the prior item and indicating a match or mismatch.

Generates 2-paragraph informational or narrative texts for 5th graders designed to practice inference and main idea identification. The output must include 5 questions about main/supporting points and 2 inference questions, aligned with specific ELA learning objectives.

Identifies the critical 20% of a topic that yields 80% of the desired results and creates a focused learning plan to master it.

Simulates the movement of pieces on an 8x8 grid using random directional draws triggered by the 'MOVE' command.

Engage in English conversation with an A1 learner, explicitly correcting mistakes while using simple vocabulary and keeping responses concise (under 100 words).

Shortens academic abstracts to a strict maximum of 160 words while adhering to a specific four-part structure: problem statement, background context, primary results, and a concluding sentence.

Generates academic articles with a specific structure (Intro, 4 body paragraphs, Conclusion), academic tone, extensive in-text citations, and a strictly formatted APA-7 reference list including a signature block.

Drafts specific sections of academic research proposals (such as Statement of the Problem, Significance of the Study, and Research Methodology) based on provided research objectives, context, and constraints like word count or specific thematic focus.

Extracts specific sections (research purpose/hypotheses, participants/sample, instruments/variables) from academic articles, ensuring numerical data, methodological details, and specific formatting constraints are met.

Generates or refines academic research titles based on keywords and emphasis, adhering to specific formatting constraints like avoiding colons and smoothing phrasing.

Summarizes academic English text into strict sentence limits (defaulting to one), supporting iterative shortening and multi-paragraph list formats.

Reviews academic text from a domain expert perspective, identifying specific areas for modification one by one with revision opinions and concise recommendations, without rewriting the entire document.

Generate a concise, 2-3 sentence expression of interest in an academic paper from a student to a professor. The response must be courteous, demonstrate comprehension by weaving in key findings from the provided text, and avoid formal letter formatting.

Refines academic text for rigor, grammar, vocabulary, spelling, and logical flow. Adapts output based on user intent: provides a detailed revision table for polishing requests, or outputs only the corrected text for strict proofreading requests.

Analyzes academic texts to identify purpose, arguments, issues, and conclusions, utilizing specific categorization for findings and paragraph formatting for conclusions.

Adopt a college student persona to answer academic questions, rewrite text, or respond to comments, adhering to specific constraints like word counts or paragraph formats suitable for exams.