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Claude Code Development Best Practices

claude-code-guide

sickn33/antigravity-awesome-skills

A comprehensive guide to configuring and maximizing the potential of Claude Code, an advanced agentic coding tool. This resource details best practices, setup templates (CLAUDE.md), advanced prompting techniques (e.g., thinking keywords), and debugging strategies for building reliable, complex software autonomously.

Session Learning Patterns

continuous-learning

affaan-m/everything-claude-code

Automatically evaluates Claude Code sessions at the Stop hook, identifies reusable patterns, and stores them as learned skills for future reuse, helping you capture troubleshooting steps, debugging techniques, and project conventions without manual effort.

Multi-Agent Code Review Orchestration Tool

error-debugging-multi-agent-review

sickn33/antigravity-awesome-skills

A sophisticated AI system that coordinates multiple specialized agents (e.g., Security Auditors, Performance Analysts, Architecture Specialists) to perform comprehensive, multi-perspective code assessments. It analyzes code context, manages state, and synthesizes conflicting insights to provide deep, holistic reviews for complex software projects.

LangChain Observability And Monitoring Dashboard

langchain-observability

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

Lindy AI Agent Debugging Bundle

lindy-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.

Retell AI Voice Agent Debugging Bundle

retellai-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

This bundle provides implementation patterns and debugging utilities for AI voice agents and telephony functions using Retell AI. It is ideal for developers integrating voice-based conversational AI into phone call systems, customer service platforms, or any telephony workflow, covering SDK usage and common error handling.

Subagent Creation Guide

subagent-creator

tech-leads-club/agent-skills

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

Rapid Unsloth Fine-Tuning

Orchestra-Research/AI-Research-SKILLs

Provides expert guidance on Unsloth to accelerate fine-tuning via LoRA/QLoRA, reduce memory usage, and streamline API usage, debugging, and best practices.

Claude Code Expert CLI for Development

claude-code-expert

sickn33/antigravity-awesome-skills

A deep expert system for Anthropic's Claude Code CLI. This skill maximizes productivity by managing advanced features like hooks, persistent context via CLAUDE.md, memory management, sub-agent orchestration, and integrating with local development ecosystems. Ideal for complex coding tasks, debugging, and advanced workflow automation.

Structured AI Agent Introspection And Debugging

agent-introspection-debugging

affaan-m/everything-claude-code

This skill provides a systematic, four-phase workflow for diagnosing and recovering from AI agent failures. It guides the agent to first capture the failure state, diagnose the root cause (e.g., loop, context overflow, state drift), apply minimal containment recovery actions, and finally generate a detailed introspection report, minimizing the need for human intervention.

Optimize LLM Prompts with Trace Data

arize-prompt-optimization

github/awesome-copilot

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

Export And Inspect LLM Traces And Spans

github/awesome-copilot

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.

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