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Found 311 Skills
Guides Solana-specific on-chain forensics—ATA resolution, SPL instruction parsing, transaction history via RPC and indexers (e.g. Helius-style APIs), fund-flow graphs, Solana clustering heuristics, and program authority review. Use when the user investigates Solana wallets, SPL tokens, DEX/Jito flows, rug or phishing patterns on Solana, or needs evidence-structured tracing reports with public data only.
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
Use when implementing distributed tracing, using Jaeger or Tempo, debugging microservices latency, or asking about "tracing", "Jaeger", "OpenTelemetry", "spans", "traces", "observability"
Add LangWatch tracing and observability to your code. Use for both onboarding (instrument an entire codebase) and targeted operations (add tracing to a specific function or module). Supports Python and TypeScript with all major frameworks.
Implement distributed tracing with Jaeger and Zipkin for tracking requests across microservices. Use when debugging distributed systems, tracking request flows, or analyzing service performance.
Instruments Python and TypeScript code with MLflow Tracing for observability. Triggers on questions about adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, or tracing specific frameworks (LangGraph, LangChain, OpenAI, DSPy, CrewAI, AutoGen). Examples - "How do I add tracing?", "How to instrument my agent?", "How to trace my LangChain app?", "Getting started with MLflow tracing", "Trace my TypeScript app"
Implement distributed tracing using logs, including trace context propagation, span logging, correlation IDs, and OpenTelemetry integration for observability
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
Comprehensive guide and toolkit for diagnosing Rspack build issues. Quickly identify where crashes/errors occur, or perform detailed performance profiling to resolve bottlenecks. Use when the user encounters build failures, slow builds, or wants to optimize Rspack performance.
Agent tracing CLI for inspecting agent execution snapshots. Use when user mentions 'agent-tracing', 'trace', 'snapshot', wants to debug agent execution, inspect LLM calls, view context engine data, or analyze agent steps. Triggers on agent debugging, trace inspection, or execution analysis tasks.
Distributed traces, spans, service dependencies, performance analysis, and failure detection. Query trace data, analyze request flows, and investigate span-level details.
Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation. Part of cursor-rules-java project