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Autonomous Crypto BD Agent Toolkit

crypto-bd-agent

sickn33/antigravity-awesome-skills

A comprehensive toolkit for building production-grade AI agents focused on crypto business development. This skill guides users through the entire token listing lifecycle: autonomously discovering promising assets across multiple chains, applying a weighted 100-point scoring model, verifying safety via advanced wallet forensics, and managing outreach pipelines with necessary human oversight and LLM cascade patterns.

Manage AI Model API Keys in Cursor

cursor-api-key-management

jeremylongshore/claude-code-plugins-plus-skills

Comprehensive guide for configuring Bring Your Own Key (BYOK) for major AI providers, including OpenAI, Anthropic, Google Gemini, Azure, and custom endpoints. This allows users to bypass default quotas, manage costs directly with the source provider, and utilize a wide range of specialized models within the Cursor IDE. It covers setup steps, security best practices, and cost monitoring strategies.

Databricks ML Workflow: Training and Serving

databricks-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

Dataset Loader Creator

dataset-loader-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates dataset loader creation within ML Training workflows, offering best-practice guidance, validation checks, and production-ready code to accelerate data prep and model tuning.

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.

TorchTitan Distributed Pretraining

distributed-llm-pretraining-torchtitan

Orchestra-Research/AI-Research-SKILLs

Orchestrates PyTorch-native TorchTitan 4D parallelism (FSDP2, TP, PP, CP) for large-scale LLM pretraining, scaling from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing to boot Llama 3.1, DeepSeek V3, or custom models.

Declarative LM Systems

Orchestra-Research/AI-Research-SKILLs

DSPy lets you declaratively program complex language model systems, assemble modular RAG/agent pipelines, and optimize prompts and modules automatically for reliable LM-driven apps.

Selecting and Optimizing Embedding Models

embedding-strategies

sickn33/antigravity-awesome-skills

A comprehensive guide for selecting and optimizing embedding models for advanced vector search and retrieval-augmented generation (RAG) applications. This guide covers model comparison, chunking strategies, dimension reduction techniques, and provides runnable Python templates for both OpenAI and local Sentence Transformer implementations.

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.

Claude Eval Harness

affaan-m/everything-claude-code

Formal evaluation framework for Claude Code sessions that applies evaluation-driven development to define capability and regression checks, run deterministic and model graders, and track pass@k/pass^k reliability metrics before shipping.

Code Model Evaluation and Benchmarking

evaluating-code-models

Orchestra-Research/AI-Research-SKILLs

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.

LLM Evaluation Harness

evaluating-llms-harness

Orchestra-Research/AI-Research-SKILLs

Evaluates large language models across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) with standardized prompts and metrics, making it easy to benchmark HuggingFace or API models, compare releases, and track training progress.

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