Data Science & ML Habilidades de Claude

Atomic Agents Project Structure

Scaffolds and organizes modular AI agent projects using standardized directory layouts and configuration patterns.

Atomic Agents Schema Designer

Defines robust, type-safe data contracts and Pydantic-based schemas for AI agents using the Atomic Agents framework.

Atomic Agents Context Providers

Injects dynamic, runtime data into AI agent system prompts to enable context-aware decision making and information sharing.

Atomic Agents Configuration

Orchestrates and configures modular, structured AI agents using the Atomic Agents framework for robust LLM applications.

Atomic Prompt Design

Generates structured, effective system prompts for AI agents using a modular architecture of background identity, processing steps, and output instructions.

Context Engineering Fundamentals

Master the core principles of AI context management to optimize agent performance and token efficiency.

Multi-Agent Architecture Patterns

Designs and implements robust multi-agent systems using supervisor, swarm, and hierarchical patterns to optimize context management and reasoning.

Context Optimization Expert

Optimizes LLM context windows through strategic compaction, observation masking, and partitioning to reduce token costs and improve agent performance.

Context Compression Strategies

Optimizes AI agent performance and token usage by implementing advanced context summarization and management strategies for long-running sessions.

Advanced LLM Evaluation

Implements production-grade LLM-as-a-judge patterns to evaluate model outputs using structured rubrics, bias mitigation, and pairwise comparison techniques.

LLM Project Methodology

Guides the end-to-end development of LLM-powered applications, from task evaluation and pipeline design to cost estimation and agent architecture.

Agent Memory Systems

Implements sophisticated multi-layer memory architectures for AI agents to persist state, track entities, and maintain temporal knowledge across sessions.

BDI Mental State Modeling

Transforms external RDF context into agent mental states to enable cognitive reasoning, explainability, and semantic interoperability in multi-agent systems.

Context Degradation Diagnostics

Diagnoses and mitigates performance failures in agentic systems caused by large context windows, attention loss, and information noise.

RAG Architect

Designs and implements production-grade Retrieval-Augmented Generation pipelines with advanced chunking, retrieval, and evaluation strategies.

Agent Designer

Architects and evaluates robust multi-agent systems using industry-standard patterns and sophisticated orchestration strategies.

Cortex-M Backend for ExecuTorch

Optimizes and deploys PyTorch models to Arm Cortex-M processors using the CMSIS-NN backend.

AudioCraft Audio Generation

Generates high-quality music and sound effects from text descriptions using Meta's AudioCraft library.

SimPO LLM Alignment

Implements Simple Preference Optimization to align Large Language Models without requiring a reference model.

Pyvene Causal Interventions

Facilitates causal interventions on PyTorch models using a declarative framework for mechanistic interpretability experiments.

AWQ LLM Quantization & Optimization

Optimizes Large Language Models using 4-bit activation-aware weight quantization to achieve 3x faster inference with minimal accuracy loss.

PEFT LLM Fine-Tuning

Optimizes large language model fine-tuning using LoRA, QLoRA, and other parameter-efficient methods to significantly reduce memory and hardware requirements.

GPTQ Model Quantization

Compresses large language models to 4-bit precision to enable high-speed inference and deployment on consumer-grade hardware.

SentencePiece Tokenization

Implements language-independent subword tokenization using BPE and Unigram algorithms for robust NLP model training and inference.

BigCode Evaluation Harness

Evaluates AI code generation models using industry-standard benchmarks and pass@k metrics.

Whisper Speech Recognition

Transcribes and translates audio across 99 languages using OpenAI's robust general-purpose speech recognition models.

Ray Data Scalable Processing

Facilitates high-performance distributed data processing and streaming for large-scale machine learning workloads.

Knowledge Distillation for LLMs

Compresses large language models into efficient student models while retaining performance through advanced teacher-student transfer techniques.

LangChain Framework Integration

Builds sophisticated LLM applications using agents, chains, and Retrieval-Augmented Generation (RAG) with a unified interface.

SGLang Inference Serving

Optimizes LLM serving and structured data generation with RadixAttention prefix caching for high-performance agentic workflows.