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Found 240 Skills
Java logging best practices with SLF4J, structured logging (JSON), and MDC for request tracing. Includes AI-friendly log formats for Claude Code debugging. Use when user asks about logging, debugging application flow, or analyzing logs.
Comprehensive codebase reading engine. Systematically reads actual source code line by line through a 6-phase protocol — scoping, structural mapping, execution tracing, deep reading, pattern synthesis, and structured reporting. Source code is the source of truth. Use when needing to truly understand how code works, not just what documentation claims.
Production-grade logging and observability patterns for ASP.NET Core Razor Pages. Covers structured logging with Serilog, correlation IDs, health checks, request logging, OpenTelemetry integration, and diagnostic best practices. Use when setting up structured logging in ASP.NET Core applications, implementing distributed tracing with OpenTelemetry, or configuring health checks and observability.
Comprehensive Pal MCP toolkit for code analysis, debugging, planning, refactoring, code review, and execution tracing. Provides systematic workflows with expert validation for complex development tasks.
AST-based semantic code search skill for AI agents. Teaches agents to use sqry's 34 MCP tools for finding symbols by structure (functions, classes, types), tracing relationships (callers, callees, imports, inheritance), analyzing dependencies, and detecting code quality issues. Unlike embedding-based search, sqry parses code like a compiler. Supports 37 languages. Uses tiered discovery: start with Quick Tool Selection below, load reference files only when you need parameter details or advanced workflows.
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
Generate Chi HTTP handlers following GO modular architechture conventions (request/response DTOs, use case orchestration, error handling, swagger annotations, Fx DI). Use when creating HTTP endpoint handlers in internal/modules/<module>/http/chi/handler/ for REST operations (List, Create, Update, Delete, Get) that need to decode requests, call use cases, map responses, and handle errors with proper logging and tracing.
World-class application logging - structured logs, correlation IDs, log aggregation, and the battle scars from debugging production without proper logsUse when "log, logging, logger, debug, trace, audit, structured log, correlation id, request id, log level, winston, pino, bunyan, log4j, logging, observability, debugging, monitoring, tracing, structured-logs, correlation, aggregation" mentioned.
Multi-Model Collaboration — Invoke gemini-agent and codex-agent for auxiliary analysis **Trigger Scenarios** (Proactive Use): - In-depth code analysis: algorithm understanding, performance bottleneck identification, architecture sorting - Large-scale exploration: 5+ files, module dependency tracking, call chain tracing - Complex reasoning: solution evaluation, logic verification, concurrent security analysis - Multi-perspective decision-making: requiring analysis from different angles before comprehensive judgment **Non-Trigger Scenarios**: - Simple modifications (clear changes in 1-2 files) - File searching (use Explore or Glob/Grep) - Read/write operations on known paths **Core Principle**: You are the decision-maker and executor, while external models are consultants.
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.
Vercel observability for Web Analytics, Speed Insights, logs, tracing, alerts, and observability tooling. Use when monitoring performance or debugging production behavior on Vercel.
Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).