24533 个技能
ใช้เมื่อผู้ใช้ร้องขอให้สร้าง จัดโครงร่าง หรือแก้ไข Jupyter notebooks (`.ipynb`) สำหรับการทดลอง การสำรวจ หรือบทแนะนำ; ควรใช้เทมเพลตที่มาพร้อมและรันสคริปต์ช่วยเหลือ `new_notebook.py` เพื่อสร้างโน้ตบุ๊กเริ่มต้นที่สะอาด
Kullanıcı deneyler, keşifler veya öğreticiler için Jupyter not defterleri (`.ipynb`) oluşturmasını, iskeletini hazırlamasını veya düzenlemesini istediğinde kullanın; birlikte gelen şablonları tercih edin ve temiz bir başlangıç not defteri oluşturmak için yardımcı betik `new_notebook.py`'yi çalıştırın.
استعمال کریں جب صارف تجربات، تحقیق، یا ٹیوٹوریلز کے لیے جیوپیٹر نوٹ بکس (`.ipynb`) بنانے، ڈھانچہ تیار کرنے، یا ایڈیٹ کرنے کو کہے؛ شامل ٹیمپلیٹس کو ترجیح دیں اور ایک صاف شروعاتی نوٹ بک تیار کرنے کے لیے مددگار اسکرپٹ `new_notebook.py` چلائیں۔
Використовуйте, коли користувач просить створити, підготувати або редагувати Jupyter-ноутбуки (`.ipynb`) для експериментів, досліджень або підручників; надавайте перевагу вбудованим шаблонам і запустіть допоміжний скрипт `new_notebook.py`, щоб згенерувати чистий початковий ноутбук.
在用户请求为实验、探索或教程创建、搭建或编辑 Jupyter 笔记本（`.ipynb`）时使用；优先使用捆绑的模板并运行辅助脚本 `new_notebook.py` 来生成一个干净的起始笔记本。
當使用者要求建立、搭建或編輯 Jupyter 筆記本（`.ipynb`）用於實驗、探索或教學時使用；優先使用隨附範本並執行輔助腳本 `new_notebook.py` 來產生一個乾淨的起始筆記本。
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.
Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.
Transform raw data into compelling narratives that drive decisions and inspire action.
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
Read and analyze Hugging Face paper pages or arXiv papers with markdown and papers API metadata.
Track ML experiments with Trackio using Python logging, alerts, and CLI metric retrieval.
Train or fine-tune vision models on Hugging Face Jobs for detection, classification, and SAM or SAM2 segmentation.