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LLM Product Engineering

sickn33/antigravity-awesome-skills

Guides AI product engineers through shipping reliable LLM features at scale, covering prompt-as-code practices, validation, safety layers, streaming experiences, and monitoring to cut costs and catch errors before they reach users.

Reliable Autonomous Agents

autonomous-agents

sickn33/antigravity-awesome-skills

Guides architects in building autonomous agents with guardrails, logging, and staged autonomy so each agent stays dependable over multi-step workflows instead of failing near production.

Azure ML SDK Python

sickn33/antigravity-awesome-skills

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Azure ML SDK Python

sickn33/antigravity-awesome-skills

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Context Optimization Techniques

context-optimization

sickn33/antigravity-awesome-skills

Guides how to stretch limited LLM context windows by compressing, masking, caching, and partitioning information so agents process longer conversations or documents without enlarging models; includes when to trigger optimizations and concrete patterns for summaries, masking, KV-cache reuse, and budget tracking.

Crypto BD Agent Patterns

crypto-bd-agent

sickn33/antigravity-awesome-skills

Provides production-tested patterns for building AI agents that autonomously discover, evaluate, and acquire token listings for cryptocurrency exchanges, covering multi-source intelligence, 100-point scoring, wallet forensics, outreach pipelines, micropayment orchestration, and ERC-8004 registration.

Deepgram Performance Tuning

deepgram-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

Guides teams through audio preprocessing, connection pooling, model selection, streaming workflows, concurrency controls, and caching to accelerate Deepgram transcription and cut latency across large-scale pipelines.

Automated Model Deployment

deploying-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Deploying Machine Learning Models automates serving trained models to production by analyzing requirements, generating API and infrastructure code, and orchestrating deployment workflows with validation, error handling, and monitoring baked in.

Feature Engineering Toolkit

engineering-features-for-machine-learning

jeremylongshore/claude-code-plugins-plus-skills

Feature Engineering Toolkit automates creating, selecting, and transforming dataset features to improve machine learning models; use it whenever you need new interactions, automatic selection, scaling, or encoding to boost accuracy and interpretability.

Exa Architecture Variants

exa-architecture-variants

jeremylongshore/claude-code-plugins-plus-skills

Guides teams through choosing and implementing validated Exa deployment architectures—from direct search to cached semantic layers and RAG pipelines—so integrations scale from simple apps to high-query, AI-enhanced solutions.

Exa Cost Optimization Guide

exa-cost-tuning

jeremylongshore/claude-code-plugins-plus-skills

Guides teams through reducing Exa AI search API bills by adding query caching, picking cost-effective search types, limiting result counts, deduplicating batch queries, and monitoring budget and usage alerts.

Exa Reliability Patterns

exa-reliability-patterns

jeremylongshore/claude-code-plugins-plus-skills

Establish circuit breakers, caching, retries, fallback chains, and quality monitoring for Exa neural search integrations so production services stay resilient under rate limits, empty results, or slow responses.

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