data-analysis
18
python-developer
Python best practices, FastAPI, Pandas ve veri bilimi kütüphaneleri kullanımı.
vuralserhat86
data-ai
sorting
stars
current ordering strategy
query
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data-analysis
18
python-data-stack
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
vuralserhat86
data-ai
data-engineering
18
data-transform
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
vuralserhat86
data-ai
data-engineering
18
data-structure-checker
This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.
aws-samples
data-ai
data-engineering
18
drizzle-orm
Type-safe ORM for Cloudflare D1 databases using Drizzle. Provides patterns for schema definition, migrations, and type-safe queries. Prevents transaction errors and schema mismatches. Includes templates for strict TypeScript usage.
vuralserhat86
data-ai
llm-ai
18
langchain-patterns
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.
vuralserhat86
data-ai
llm-ai
18
mcp-builder-skill
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
jjmartres
data-ai
llm-ai
18
prompt-engineering
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
vuralserhat86
data-ai
llm-ai
18
prompt-optimizer
Mevcut promptların token kullanımını ve başarı oranını optimize etme.
vuralserhat86
data-ai
machine-learning
18
huggingface-transformers
Hugging Face Transformers best practices including model loading, tokenization, fine-tuning workflows, and inference optimization. Use when working with transformer models, fine-tuning LLMs, implementing NLP tasks, or optimizing transformer inference.
vuralserhat86
data-ai
llm-ai
18
building-rag-systems
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
mjunaidca
data-ai
llm-ai
18
memory-systems
Design and implement memory architectures for agent systems. Use when building agents that need to persist state across sessions, maintain entity consistency, or reason over structured knowledge.
mjunaidca
data-ai
machine-learning
18
ai-engineer
Use when building production-grade GenAI, Agentic Systems, Advanced RAG, or setting up rigorous Evaluation pipelines.
kienhaminh
data-ai
llm-ai
18
scaffolding-openai-agents
Builds AI agents using OpenAI Agents SDK with async/await patterns and multi-agent orchestration. Use when creating tutoring agents, building agent handoffs, implementing tool-calling agents, or orchestrating multiple specialists. Covers Agent class, Runner patterns, function tools, guardrails, and streaming responses. NOT when using raw OpenAI API without SDK or other agent frameworks like LangChain.
mjunaidca
data-ai
llm-ai
18
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
basher83
data-ai
llm-ai
18
browserbase
Cloud browser infrastructure for AI agents - create sessions, persist contexts, and automate browsers
vm0-ai
data-ai
llm-ai
18
dspy-rag-pipeline
Build and optimize RAG pipelines with ColBERTv2 retrieval in DSPy
OmidZamani
data-ai
llm-ai
18
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
mjunaidca
data-ai
llm-ai
18
dspy-bootstrap-fewshot
Auto-generate high-quality few-shot examples using teacher models in DSPy
OmidZamani
data-ai
machine-learning
18
model-finetuning
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
vuralserhat86
data-ai
llm-ai
18
building-mcp-servers
Guides creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK). Covers tool design, authentication, Docker deployment, and evaluation creation. NOT when consuming existing MCP servers (use the server directly).
mjunaidca
data-ai