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Found 17 Skills
Systematic debugging and root cause analysis for identifying and fixing software issues. Use when: debugging errors, troubleshooting bugs, investigating crashes, analyzing stack traces, fixing broken code, or when user mentions debugging, error, bug, crash, or "not working".
Use when investigating errors, analyzing stack traces, or finding root causes of unexpected behavior. Invoke for error investigation, troubleshooting, log analysis, root cause analysis.
Diagnose and fix bugs using runtime execution traces. Use when debugging errors, analyzing failures, or finding root causes in Python, Node.js, or Java applications.
Implement distributed tracing with Jaeger and Zipkin for tracking requests across microservices. Use when debugging distributed systems, tracking request flows, or analyzing service performance.
Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes. Use PROACTIVELY when debugging issues, analyzing logs, or investigating production errors.
Analyze claude-trace JSONL files for session health, patterns, and actionable insights. Use when debugging session issues, understanding token usage, or identifying failure patterns.
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, and inspect datasets. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
Guidance for interpreting SPAA (Stack Profile for Agentic Analysis) files. Provides information on the file format, as well as tips on how to use it to identify performance bottlenecks, memory leaks, or opportunities for optimization. Use when the user is trying to read a .spaa file to understand the performance of an application.
Rust debugging skill for systems programming. Use when debugging Rust binaries with GDB or LLDB, enabling Rust pretty-printers, interpreting panics and backtraces, debugging async/await with tokio-console, stepping through no_std code, or using dbg! and tracing macros effectively. Activates on queries about rust-gdb, rust-lldb, RUST_BACKTRACE, Rust panics, debugging async Rust, tokio-console, or pretty-printers.
Debug errors, test failures, and unexpected behavior with log analysis and correlation. Use when encountering issues, error messages, analyzing logs, or investigating production errors.
Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".
Fetch, organize, and analyze LangSmith traces for debugging and evaluation. Use when you need to: query traces/runs by project, metadata, status, or time window; download traces to JSON; organize outcomes into passed/failed/error buckets; analyze token/message/tool-call patterns; compare passed vs failed behavior; or investigate benchmark and production failures.