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Phoenix AI Observability

phoenix-observability

Orchestra-Research/AI-Research-SKILLs

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.

Prompt Engineering Patterns

prompt-engineering-patterns

sickn33/antigravity-awesome-skills

Guides engineers through advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability across few-shot learning, reasoning patterns, templates, and system prompt design.

Causal Intervention for PyTorch Models

pyvene-interventions

Orchestra-Research/AI-Research-SKILLs

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.

High-Performance Vector Similarity Search Engine

qdrant-vector-search

Orchestra-Research/AI-Research-SKILLs

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

LLM Quantization Toolkit

quantizing-models-bitsandbytes

Orchestra-Research/AI-Research-SKILLs

Quantizes HuggingFace LLMs to 8-bit or 4-bit with bitsandbytes, cutting memory by up to 75% while keeping accuracy, and supports QLoRA fine-tuning plus 8-bit optimizers for faster, memory-efficient training.

Retell AI Observability for Voice Agents

retellai-observability

jeremylongshore/claude-code-plugins-plus-skills

This skill provides implementation patterns for achieving observability in AI voice agents and telephony platforms built with Retell AI. It guides users on how to monitor the performance, status, and interactions of automated voice agents and phone calls. It also includes essential guidance on common API error handling (e.g., 401 Unauthorized, 429 Rate Limited), ensuring robust and reliable system operation.

Retell AI Voice Agent and Telephony Automation

retellai-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

Provides implementation patterns and reference architecture for building advanced AI voice agents and automating complex phone call workflows using the Retell AI SDK. This skill is designed for developers requiring robust voice interaction, real-time telephony integration, and comprehensive conversational AI capabilities in their applications.

Retell AI Voice Agent Reliability Patterns

retellai-reliability-patterns

jeremylongshore/claude-code-plugins-plus-skills

This skill provides implementation patterns for building robust and reliable AI voice agents and telephony automation systems using the Retell AI SDK. It covers best practices for handling common communication errors (like rate limiting or invalid API keys) and ensures stable interaction flow for voice-based applications and phone call automation scenarios.

Solana Vulnerability Audit

solana-vulnerability-scanner

trailofbits/skills

Scans Solana and Anchor programs for six critical security vulnerabilities—arbitrary CPI, improper PDA validation, missing signer/ownership checks, sysvar spoofing, and instruction introspection issues—to support audits and pre-launch reviews.

Sparse Autoencoders for Model Interpretability

sparse-autoencoder-training

Orchestra-Research/AI-Research-SKILLs

SAELens provides a framework for training and analyzing Sparse Autoencoders (SAEs). SAEs decompose the dense, often polysemantic activations of large language models into sparse, monosemantic features. Use this when you need to discover the discrete, interpretable concepts a model has learned, study feature superposition, or analyze specific safety-relevant behaviors (like bias or deception) within deep neural networks.

Speculative Decoding Acceleration

speculative-decoding

Orchestra-Research/AI-Research-SKILLs

Speeds up LLM inference by combining speculative decoding with Medusa multiple heads and lookahead techniques so chatbots, code assistants, and other real-time workloads can run with 1.5-3.6× lower latency while preserving target-model quality.

Superpowers Lab Environment Guide

superpowers-lab

sickn33/antigravity-awesome-skills

This skill provides a dedicated lab environment and pattern guidance for exploring and utilizing advanced, complex capabilities within the Claude AI framework. Use it when you need a structured space to test, develop, and understand advanced AI workflows and powerful features.

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