ci-failure-analysis
This skill should be used when analyzing failed GitHub Actions CI/CD runs for Breenix kernel development. Use for diagnosing test failures, parsing QEMU logs, identifying kernel panics or faults, understanding timeout issues, and determining root causes of CI failures.
pr-context-retriever
Specialized GitHub PR intelligence agent for automatically gathering comprehensive context from Pull Requests. Activate when users need CI failure analysis, review comment investigation, or PR status assessment. Triggers on requests like "CI 실패 원인 찾아줘", "gather review comments", "check PR status", "analyze PR".
gh-run-failure
Use to analyze failures in GitHub pipelines or jobs.
thymer-capture
Capture notes, logs, and journal entries to Thymer. Use when user says "note this", "log this", "capture this", "add to journal", "inbox this", "save this conversation", or wants to save content to their Thymer workspace.
chat-history
Extract and organize Claude Code session history into project .chats directory. Use when users want to document their Claude sessions, export conversation inputs, or maintain a log of instructions given to Claude.
axiom
Axiom observability platform for logs, events, and analytics via REST API
monitoring-expert
Use when setting up monitoring systems, logging, metrics, tracing, or alerting. Invoke for dashboards, Prometheus/Grafana, load testing, profiling, capacity planning. Keywords: monitoring, observability, logging, metrics, tracing, alerting, Prometheus, Grafana.
debugging-wizard
Use when investigating errors, analyzing stack traces, or finding root causes of unexpected behavior. Invoke for error investigation, troubleshooting, log analysis, root cause analysis. Keywords: debug, error, bug, traceback, exception, breakpoint.
log-analyzer
Parse and analyze application logs to identify errors, patterns, and insights.
ln-627-observability-auditor
Observability audit worker (L3). Checks structured logging, health check endpoints, metrics collection, request tracing, log levels. Returns findings with severity, location, effort, recommendations.
annotate
Create flexible annotation workflows for AI applications. Contains common tools to explore raw ai agent logs/transcripts, extract out relevant evaluation data, and llm-as-a-judge creation.
rails-ai:debugging-rails
Use when debugging Rails issues - provides Rails-specific debugging tools (logs, console, byebug, SQL logging) integrated with systematic debugging process
datadog
Query and analyze Datadog logs, metrics, APM traces, and monitors using the Datadog API. Use when debugging production issues, monitoring application performance, or investigating alerts.
python-script
Create robust Python automation with full logging and safety checks. Use when tasks need complex data processing, authenticated API work, conditional file operations, or error handling beyond simple shell commands.
database-monitoring
Monitor database performance and health. Use when setting up monitoring, analyzing metrics, or troubleshooting database issues.
log-aggregation
Implement centralized logging with ELK Stack, Loki, or Splunk for log collection, parsing, storage, and analysis across infrastructure.
logging-best-practices
Implement structured logging with JSON formats, log levels (DEBUG, INFO, WARN, ERROR), contextual logging, PII handling, and centralized logging. Use for logging, observability, log levels, structured logs, or debugging.
log-analysis
Analyze application and system logs to identify errors, patterns, and root causes. Use log aggregation tools and structured logging for effective debugging.
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