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Systematic Debugging Methodology

systematic-debugging

jnMetaCode/superpowers-zh

This skill provides a structured approach to debugging software issues by prioritizing root cause analysis over quick fixes. It guides users through four phases: investigation, pattern analysis, hypothesis verification, and implementation. Essential for resolving test failures, production bugs, and performance issues efficiently, preventing recurring problems and architectural debt.

Dispatching Parallel Agents

dispatching-parallel-agents

jnMetaCode/superpowers-zh

Dispatch dedicated agents with isolated contexts to handle multiple independent failures or tasks in parallel. Use when you have several unrelated issues that can be investigated separately without shared state, while preserving your own context for coordination and review.

Request Code Review Before Merge

requesting-code-review

jnMetaCode/superpowers-zh

Dispatch a code review sub-agent to validate work quality before merging or completing tasks. This skill ensures issues are caught early, preventing bugs from spreading. It involves preparing git SHAs, using specific templates, and handling feedback from the review agent to maintain code quality and architectural integrity.

Structured Critical Thinking Coach

06-critical-thinking

24kchengYe/human-skill-tree

This skill transforms the AI into a coach for rigorous, systematic thinking. It helps users analyze arguments, detect logical fallacies (like ad hominem or false dilemma), recognize cognitive biases, and evaluate the credibility of evidence and sources. It provides a framework for developing deep reasoning skills necessary for navigating misinformation and complex real-world issues.

Histolab WSI Tile Extraction

K-Dense-AI/scientific-agent-skills

Python library for digital pathology whole slide images: tissue detection, tile extraction, filters, and stain normalization. Ideal for preparing H&E datasets for deep learning and quick tile-based analysis.

Debugging Qdrant Performance Issues

qdrant-monitoring-debugging

github/awesome-copilot

A comprehensive guide for diagnosing production performance issues in Qdrant vector databases. Learn how to systematically check the optimizer status, analyze high memory usage (OOM crashes), and troubleshoot slow query latencies using key metrics and dedicated API endpoints. Essential for maintaining optimal operational health.

Technical Design Validation Review

kiro-validate-design

Interactive technical design quality review tool that validates design documents against requirements, identifies critical issues, and provides GO/NO-GO decisions with actionable feedback for implementation readiness. Supports multi-agent parallel research and follows structured review process.

Qdrant Monitoring and Observability Setup

qdrant-monitoring

github/awesome-copilot

This guide provides comprehensive instructions for setting up monitoring and observability for Qdrant deployments. It helps users track critical performance metrics, check system health, and diagnose complex production issues such as memory growth or slow query requests. Use cases include setting up Prometheus, Grafana, and implementing robust health checks.

Collect Alchemy Debug Bundle

alchemy-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

This tool generates a comprehensive debug bundle for troubleshooting Alchemy SDK issues. It collects crucial diagnostic evidence, including connectivity tests for core, enhanced, and NFT APIs, network status across multiple chains (Ethereum, Polygon, Arbitrum), SDK version, and environment details. Use it when encountering complex blockchain query failures or preparing detailed support tickets for reliable issue resolution.

Diagnosing Anima Code Generation Errors

anima-common-errors

jeremylongshore/claude-code-plugins-plus-skills

This guide provides comprehensive troubleshooting for common issues encountered when converting Figma designs to code using the Anima SDK. It covers authentication failures (Figma/Anima tokens), specific node/file lookup errors, code rate limits, and output quality problems, helping developers ensure accurate and robust code generation.

Anima Debug Bundle Generator

anima-debug-bundle

jeremylongshore/claude-code-plugins-plus-skills

This utility generates a comprehensive JSON debug bundle for troubleshooting and support requests related to the Anima SDK. It collects vital information, including environment details, Figma API connectivity status, and verifies the Anima SDK generation capabilities. Use it when filing support tickets or debugging code generation issues.

Advanced Anthropic API Debugging Guide

anth-advanced-troubleshooting

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for developers encountering complex issues with the Claude API, including context window overflow management, debugging tool use failures, handling streaming corruption, and diagnosing unexpected stop reasons. Provides Python code snippets and diagnostic commands to ensure robust AI integration.

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