Autonomous Bug Hunter
Self-directed debugging skill that systematically reproduces, diagnoses, and fixes bugs with minimal supervision. Uses test-driven verification to ensure fixes actually resolve issues without introducing regressions.
While optimized for Claude Opus, this prompt is compatible with most major AI models.
Guides AI to articulate step-by-step reasoning before generating code fixes, significantly improving accuracy and reducing logical errors in debugging.
Chain-of-thought prompting has emerged as one of the most effective techniques for improving AI code quality. Research consistently shows that asking the AI to "think out loud" before writing code reduces errors by 30-50% on complex debugging tasks. This approach gained viral traction on Reddit when developers reported dramatic improvements in fix accuracy. The key insight: forcing explicit reasoning prevents the AI from pattern-matching to superficially similar but incorrect solutions.
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Self-directed debugging skill that systematically reproduces, diagnoses, and fixes bugs with minimal supervision. Uses test-driven verification to ensure fixes actually resolve issues without introducing regressions.
Implement robust error handling with proper exception hierarchies, error boundaries, retry logic, and user-friendly error messages across languages.
Implement structured logging, distributed tracing, metrics collection, and alerting for production systems with proper correlation and debugging capabilities.
Build high-quality Model Context Protocol (MCP) servers to integrate external APIs and services with AI assistants. Follow best practices for tool design, security, and cross-platform compatibility.
Goes beyond fixing code to explain what was wrong and what the developer likely misunderstood when writing it. Builds understanding, not just fixes.
Generates complete REST API specifications with endpoints, request/response schemas, authentication, and implementation code.
Guides learning through questions rather than answers, helping developers discover solutions themselves while building deeper understanding.
Simulates realistic technical interviews with coding problems, system design questions, and behavioral scenarios with real-time feedback.