Initialize a new git worktree and branch for feature development or bug fixes. Use when: (1) Starting work on a new feature, (2) Beginning a bug fix, (3) Creating an isolated workspace for any task, (4) You want to work in parallel on multiple branches. This skill handles branch naming with conventional branch conventions, worktree creation, and remote push setup.
Systematic refactoring with small-step discipline. Use when user says 'refactor', 'clean up', 'restructure', 'extract', 'rename', 'simplify', or mentions code smells. Enforces one change → test → commit cycle. For structural improvements, NOT style/formatting (use /lint). NOT for adding features or fixing bugs.
Use when work is complete and ready for integration, merge, or PR creation.
Creates pull requests with generated descriptions. Triggered when: PR creation, pull request, merge request, code review preparation.
This skill should be used when updating the project CHANGELOG, tracking requirement changes, recording design decisions, or documenting version history. Uses git-diff to detect and categorize changes to both code and requirements. Trigger on "changelog", "version history", "what changed", or after significant commits.
Assesses and responds to incoming code review feedback on PRs (reviewer comments, requested changes), especially when suggestions are unclear, technically questionable, or scope-expanding. Use before implementing review suggestions to align on intent and keep changes minimal.
Structured JSON logging with correlation IDs for multi-service systems. Use when implementing logging, debugging failures, or tracing errors across services. Triggers on: add logging, error handling, debug failures, trace errors.
Audit Axiom logs to identify and prioritize errors and warnings, research probable causes, and flag log smells. Use when user asks to check Axiom logs, analyze production errors, investigate log issues, or audit logging patterns.
Adds comprehensive error tracking with Sentry, Rollbar, or similar services including error boundaries, context, and breadcrumbs. Use when user requests error monitoring or mentions production debugging.
Observability patterns for Python backends. Use when adding logging, metrics, tracing, or debugging production issues.
Implement JSON-based structured logging for observability. Use when setting up logging, debugging production issues, or preparing for log aggregation (ELK, Datadog). Covers log levels, context, and best practices.
Use this when working with OpenTelemetry, telemetry, observability, traces, spans, metrics, logs, OTLP, semantic conventions, or instrumentation. Triggers on questions like "what attributes should I use", "how do I configure the collector", "what's the semconv for X".
Parse and analyze application logs to identify errors, patterns, and insights.
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
Monitor system resources (CPU, memory, disk, network) during development and production.
Configures Dapr pub/sub components for event-driven microservices with Kafka or Redis. Use when wiring agent-to-agent communication, setting up event subscriptions, or integrating Dapr sidecars. Covers component configuration, subscription patterns, publishing events, and Kubernetes deployment. NOT when using direct Kafka clients or non-Dapr messaging patterns.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Use when creating new skills, documenting workflows, or standardizing processes into reusable skill definitions.
