Agent skill · data analytics · sickn33
debugging-toolkit-smart-debug
Diagnose failures from reproduction steps, traces, logs and code; test hypotheses and verify a scoped fix without inventing production evidence.
Why this skill is useful
Adds a comprehensive set of AI-assisted debugging workflows and techniques that enhance the agent's ability to analyze and resolve complex issues.
What it needs
Requires datadog, elk, fullstory, honeycomb, jaeger, logrocket, loki, newrelic, sentry, splunk, zipkin installed locally. Requires datadog, elk, fullstory, honeycomb, jaeger, logrocket, loki, newrelic, sentry, splunk, zipkin account access. About 4k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Use this skill when A reproducible failure or intermittent incident needs evidence-driven debugging. Existing telemetry must be compared with source and deployment changes before patching. Do not use this skill when The task is unrelated to debugging toolkit smart debug You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. You are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Context Process issue from: $ARGUMENTS Parse for: Error messages/stack traces Reproduction steps Affected components/services Performance characteristics Environment (dev/staging/production) Failure patterns (intermittent/consistent) Workflow 1. Initial Triage Analyze directly; use a debugger subagent only when the user authorizes delegation and the host exposes that capability: Error pattern recognition Stack trace analysis with probable causes Component dependency analysis Severity assessment Generate 3-5 ranked hypotheses Recommend debugging strategy 2. Observability Data Collection For production/staging issues, gather: Error tracking (Sentry, Rollbar, Bugsnag) APM metrics (DataDog, New Relic, Dynatrace) Distributed traces (Jaeger, Zipkin, Honeycomb) Log aggregation (ELK, Splunk, Loki) Session replays (LogRocket, FullStory) Query for: Error frequency/trends Affected user cohorts Environment-specific patterns Related errors/warnings Performance degradation correlation Deployment timeline correlation 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/debugging-toolkit-smart-debug--ad46bf