Claude Skills

Serverless platform selection expert for AWS Lambda, Azure Functions, GCP Cloud Functions, and Firebase. Use when choosing between serverless platforms, evaluating cold start requirements, or designing event-driven architectures. Considers project scale (pet project vs enterprise), workload patterns, and cost optimization.

Detects when user reports issues with recently completed work and suggests reopening relevant tasks or increments. Use when reporting bugs, regressions, or problems with recently finished features. Scans completed work from last 7 days and provides relevance-scored suggestions while checking WIP limits.

Master TDD orchestrator for strict red-green-refactor discipline, multi-agent test-driven workflows, and TDD intent detection. Use when implementing TDD across complex features, coordinating test and implementation agents, enforcing TDD cycle discipline, or wanting to write tests first. Covers modern TDD practices, test isolation, quality gates, and TDD education.

Technical lead bridging architecture and implementation for code quality and guidance. Use when reviewing code, refactoring for maintainability, or breaking features into implementation tasks. Covers design patterns, SOLID principles, code organization, and technical debt management.

Generate tasks.md with embedded test plans in BDD format, one user story at a time to prevent crashes. Use for test-first task planning where each task includes Given/When/Then scenarios. Produces implementation tasks with inline test specifications.

LLM-native translation skill that uses the current session for zero-cost SpecWeave content translation. Use when translating CLI messages, templates, documentation, or living docs to other languages. Supports multilingual output and internationalization workflows.

Detects multi-repo architecture patterns (frontend/backend/shared) from user prompts and guides umbrella setup. Use when working with multiple repositories, microservices architecture, or projects with separate FE/BE/Shared libraries. Helps configure SpecWeave for multi-repo coordination.

Smart merge for CLAUDE.md and AGENTS.md instruction files that preserves user customizations while updating SpecWeave sections. Use after plugin refresh, version upgrade, or when instruction files need sync. Parses SW-managed sections and preserves user content.

Mandatory format standard for ALL GitHub issues created by SpecWeave with checkable acceptance criteria and proper metadata. Use when creating GitHub issues, formatting issue content, or ensuring consistent issue structure. Covers user stories, epics, features, and increments.

Expert at organizing specs and splitting tasks across multiple GitHub repositories for monorepo, polyrepo, and parent repo architectures. Use when managing specs across multiple repos, coordinating cross-repo work, or allocating tasks to different teams/repositories.

Two-way synchronization between SpecWeave specs and GitHub Projects (push & pull by default). Use when asking about GitHub integration setup, troubleshooting sync issues, or configuring sync settings. For actual syncing, use /sw-github:sync-spec command.

Sync guidance for SpecWeave increments with JIRA epics/stories (content SpecWeave→JIRA, status JIRA→SpecWeave). Use when asking about JIRA integration setup or troubleshooting sync. For actual syncing, use /sw-jira:sync command instead.

Bidirectional conversion between SpecWeave increments and Azure DevOps work items. Use when exporting increments to ADO epics, importing ADO epics as increments, or resolving sync conflicts. Handles Epic/Feature/User Story/Task hierarchy mapping.

DevOps and IaC expert for Terraform, Kubernetes, Docker, CI/CD pipelines, and deployment platform decisions (Vercel vs Cloudflare vs Hetzner). Generates infrastructure ONE COMPONENT AT A TIME to prevent crashes.

Full-stack observability architect for Prometheus, Grafana, OpenTelemetry, distributed tracing (Jaeger/Tempo), SLIs/SLOs, error budgets, and alerting. Use for metrics, dashboards, traces, or reliability engineering.

Machine learning pipeline builder for end-to-end ML systems. Covers feature engineering, model training, evaluation, hyperparameter tuning, AutoML, and explainability (SHAP/LIME). Use for ML pipelines, model training, or building production ML systems.

Domain-specific ML expert for NLP, Computer Vision, and Time Series. Text classification, NER, sentiment (BERT, transformers), image classification, object detection (YOLO, ResNet), and forecasting (ARIMA, Prophet, LSTM). Use for specialized ML domains.

MLOps expert for ML infrastructure and production systems. ML pipelines (Kubeflow, Airflow, Prefect), model registry, deployment (Docker, K8s, serverless), monitoring, and CI/CD for ML. Use for automated training, model deployment, or ML infrastructure.

Multi-repo version alignment - lockstep/independent/umbrella strategies, semver constraints, version conflict detection.