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SkillsMP has collected 64 skills from cuba6112/skillfactory. Open a skill to review its source and details.
Showing 40 of 64 collected skills.
Build RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine. Use when implementing document Q&A, knowledge base search, or citation-backed responses. Covers VertexAiRagRetrieval tool, corpus setup, and citation formatting.
Build RAG systems with Ollama local + cloud models. Latest cloud models include DeepSeek-V3.2 (GPT-5 level), Qwen3-Coder-480B (1M context), MiniMax-M2. Use for document Q&A, knowledge bases, and agentic RAG. Covers LangChain, LlamaIndex, ChromaDB, and…
Design and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic,…
Multi-service orchestration with Docker Compose, focusing on network isolation, environment-specific profiles, and service discovery. Triggers: docker-compose, container-networking, docker-profiles, service-discovery, yaml-config.
Evaluation framework patterns for RAG and LLMs, including faithfulness metrics, synthetic dataset generation, and LLM-as-a-judge patterns. Triggers: ragas, deepeval, llm-eval, faithfulness, hallucination-check, synthetic-data.
Advanced FastAPI patterns including hierarchical dependency injection, background task management, and type-safe dependency annotation. Triggers: fastapi, dependency-injection, background-tasks, annotated-dependency, permission-chain.
Safe filesystem operations for agents, including path normalization vs resolution, temp file handling, atomic replacement, and spooled buffers. Use when reading/writing user-supplied paths, staging outputs, or managing temporary files; triggers: filesystem,…
Adherence to Conventional Commits and efficient Git history management using types, scopes, and advanced commit tools like fixup/amend. Triggers: git-commit, conventional-commits, breaking-change, fixup, git-amend, rebase.
Automation of GitHub tasks using the gh CLI and REST API. Includes pagination strategies, payload construction, and rate limit management. Triggers: github, gh-cli, github-api, rate-limit, pagination, pull-request.
Integration patterns for web search grounding, including query operator usage, API-based search orchestration, and citation metadata mapping. Triggers: google-search, grounding, search-api, citations, search-operators, web-search.
Building LLM agents with LangChain and LangGraph, covering tool-calling model initialization, state management, and observability with LangSmith. Triggers: langchain, langgraph, langsmith, agent-executor, chat-model-tools.
LlamaIndex Wolfram Alpha tool for computational knowledge queries, math solving, scientific calculations, and agent integration. Triggers: wolfram alpha, computational query, math solver, scientific calculation, WolframAlphaToolSpec.
Fundamental NumPy operations including ndarray creation, dtypes, shape manipulation, and basic operations with a focus on memory alignment and data views. Triggers: numpy, ndarray, dtype, reshape, memory alignment, array-creation.
Date and time handling with datetime64 and timedelta64, including business day offsets and naive time parsing. Triggers: datetime64, timedelta64, busday, time series, naive time.
Discrete Fourier Transform routines for spectral analysis, signal filtering, and frequency-domain operations. Triggers: fft, fourier transform, spectral analysis, rfft, fftshift, ifft.
Advanced indexing techniques including slicing, fancy indexing, and boolean masks, along with memory implications of views vs. copies. Triggers: indexing, slicing, fancy indexing, boolean mask, np.where, np.ix_.
Protocols for cross-library data exchange including DLPack, buffer interfaces, and __array_ufunc__ for overriding NumPy functions. Triggers: DLPack, interoperability, __array_interface__, __array_ufunc__, buffer protocol.
File I/O operations including binary formats (npy/npz), text processing (csv), and memory-mapping for huge datasets. Triggers: io, load, save, npz, genfromtxt, memmap, loadtxt.
Linear algebra operations in NumPy, including matrix multiplication, SVD, system solving, and least squares fitting. Triggers: linalg, matrix multiplication, SVD, eigenvalues, matrix decomposition, lstsq, multi_dot.
Masked arrays for robust handling of missing or invalid data, ensuring they are excluded from statistical and mathematical computations. Triggers: masked array, numpy.ma, missing data, invalid values, hard mask.
Deep dive into memory layout, including strides, C vs Fortran order, and zero-copy view generation via stride tricks. Triggers: strides, C-order, Fortran-order, memory locality, stride_tricks.
Modern polynomial API for fitting, root finding, and working with orthogonal series like Chebyshev and Legendre. Triggers: polynomial, polyfit, Chebyshev, Legendre, root finding.
Modern random number generation using the Generator API, focusing on statistical properties, parallel streams, and reproducibility. Triggers: random, rng, default_rng, SeedSequence, probability distributions, shuffle.
Set-theoretic operations for finding unique elements, membership testing, and array intersections. Triggers: unique, isin, intersect1d, setdiff1d, union1d.
Sorting and searching algorithms including O(n) partitioning, binary search, and hierarchical multi-key sorting. Triggers: sort, argsort, partition, searchsorted, lexsort, nan sort order.
Standard and NaN-robust statistical functions for data analysis, histograms, and correlation matrices. Triggers: statistics, mean, nanmean, histogram, corrcoef, percentile, std.
Vectorized string manipulation using the char module and modern string alternatives, including cleaning and search operations. Triggers: string operations, numpy.char, text cleaning, substring search.
Structured and record arrays for C-interoperability, binary blob interpretation, and multi-field tabular data handling. Triggers: structured array, record array, compound dtype, multi-field index.
Universal functions (ufuncs) for vectorization, including reductions, in-place operations, and custom Python-function wrapping. Triggers: ufunc, vectorize, reduce, accumulate, frompyfunc, in-place.
Advanced Python testing strategies with Pytest, covering fixtures, matrix testing with parametrization, and async test architecture. Triggers: pytest, fixtures, parametrize, pytest-asyncio, matrix-testing, yield-fixture.
Asyncio patterns in Python for high-concurrency IO-bound tasks. Includes coroutines, task management, and asynchronous resource handling. Triggers: asyncio, python-async, coroutine, await, async-gather, async-generator, event-loop.
Core PyTorch fundamentals including tensor operations, autograd, nn.Module architecture, and training loop orchestration. Covers optimizations like pin_memory and lazy module initialization. (pytorch, tensor, autograd, nn.Module, optimizer, training loop,…
PyTorch CUDA environment and performance guidance, with emphasis on CUDA 13 toolkit/driver requirements, PyTorch wheel compatibility, and runtime checks. Use when configuring PyTorch on NVIDIA GPUs, debugging CUDA setup, or migrating to CUDA 13; triggers:…
Distributed training strategies including DistributedDataParallel (DDP) and Fully Sharded Data Parallel (FSDP). Covers multi-node setup, checkpointing, and process management using torchrun. (ddp, fsdp, distributeddataparallel, torchrun, nccl, rank,…
Library for Graph Neural Networks (GNNs). Covers MessagePassing layers, modular aggregation schemes, and handling large graphs via mini-batching with disjoint graph representation. (pyg, messagepassing, gnn, gcn, gat, edge_index, knn_graph, global_mean_pool)
High-level training framework for PyTorch that abstracts boilerplate while maintaining flexibility. Includes the Trainer, LightningModule, and support for multi-GPU scaling and reproducibility. (lightning, pytorch-lightning, lightningmodule, trainer,…
Exporting PyTorch models to ONNX format for cross-platform deployment. Includes handling dynamic axes, graph optimization in ONNX Runtime, and INT8 model quantization. (onnx, onnxruntime, torch.onnx.export, dynamic_axes, constant-folding, edge-deployment)
Techniques for model size reduction and inference acceleration using INT8 quantization, including Post-Training Quantization (PTQ) and Quantization Aware Training (QAT). (quantization, int8, qat, fbgemm, qnnpack, ptq, dequantize)
Techniques for ensuring LLM responses adhere to strict JSON schemas, utilizing Pydantic models, JSON mode, and schema-based refusals. Triggers: structured-output, pydantic, json-schema, json-mode, llm-response-parsing.
Define and run tool-calling patterns for LLMs (schema design, call loops, validation, parallel calls). Use when building function/tool calling workflows or debugging tool selection and arguments; triggers: tool-calling, function-calling, tool schema, tool…