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Found 24 Skills
Specializes in analyzing Lynx trace data to diagnose performance issues and provide actionable optimization strategies. Key Scenarios: - Loading Performance: Diagnosing slow startup metrics (FCP, FMP, TTI) and white screen issues. - Smoothness Analysis: Investigating root causes for scroll jank, frame drops, and interaction lag. - Regression Detection: Comparing traces to identify performance degradation or verify optimization gains between versions. - Pipeline Deep Dive: Pinpointing bottlenecks in specific rendering stages like Layout, Paint, JS execution, and background threads. - Native Module Analysis: Investigating performance issues related to native module calls.
Use when investigating errors, analyzing stack traces, or finding root causes of unexpected behavior. Invoke for error investigation, troubleshooting, log analysis, root cause analysis.
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).
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.
Write ClickHouse queries for SigNoz dashboards over OpenTelemetry logs and traces. Use this skill whenever the user asks for SigNoz ClickHouse queries for logs or traces, SigNoz dashboard queries, log analysis, span counts, latency, or trace breakdowns.
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.
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.
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".
View Langfuse session details with all traces. Use when analyzing conversation flows, checking session costs, or debugging multi-turn interactions.
Query and analyze distributed traces and spans using DataPrime syntax. Use this skill whenever the user wants to investigate request latency, find slow operations, debug service-to-service calls, look up a trace ID, analyze span durations, check error spans, examine distributed traces, investigate OpenTelemetry/Jaeger tracing data, or query Coralogix spans in any way - even if they don't explicitly mention "DataPrime" or "cx spans".
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.