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Found 1,089 Skills
This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.
Container debugging and troubleshooting techniques for production issues
This skill should be used when encountering bugs, errors, failing tests, or unexpected behavior. Provides systematic debugging with evidence-based root cause investigation using a four-stage framework.
Create effective debugging prompts—include error messages, stack traces, expected vs actual behavior, logs, and attempted solutions
Systematic debugging playbook for application errors and incidents: crashes, regressions, intermittent failures, production-only bugs, performance issues, stack traces, log/trace analysis, profiling, and distributed systems root cause analysis.
Debugs Laravel applications with Xdebug integration. Activates when setting breakpoints, stepping through code, inspecting variables, analyzing dd() output, debugging routes, controllers, queues, or Eloquent queries; or when user mentions debug, breakpoint, step into, inspect variables, Xdebug, or troubleshooting errors.
Comprehensive bash script debugging and troubleshooting techniques for 2025
Load PROACTIVELY when task involves investigating errors, diagnosing failures, or tracing unexpected behavior. Use when user says "debug this", "fix this error", "why is this failing", "trace this issue", or "it's not working". Covers error message and stack trace analysis, runtime debugging, network request inspection, state debugging, performance profiling, type error diagnosis, build failure resolution, and root cause analysis with memory-informed pattern matching against past failures.
Comprehensive protocol for validating root causes of software issues. Use when you need to systematically debug a complex bug, flaky test, or unknown system behavior by forming hypotheses and validating them with specific tasks.
Four-phase debugging framework with root cause tracing - understand the source before proposing fixes. Use when investigating bugs, errors, unexpected behavior, or failed tests.
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.
Docker container debugging and management. Use when investigating container issues, checking logs, resource usage, or Docker Compose services.