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Found 31 Skills
AI agent workflow with interview-driven planning and team-based execution. Use /design to start planning, /work to execute.
Create a new feature/bug track with spec and implementation plan. Interactive interview generates requirements spec, then phased TDD plan. Use when starting work on a new feature, bug fix, or chore.
Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Generates and analyzes financial models, P&L forecasts, and cash flow projections. Transforms business assumptions into multi-year financial statements.
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Use when the user wants to create templates, extract reusable patterns, document solutions, or build a pattern library from working workflows.
Use when the workflow feels over-engineered, has premature optimizations, unnecessary abstraction layers, or complexity beyond actual requirements.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.
Use when the workflow lacks error handling, has been failing in production, or needs retry logic, fallback strategies, and circuit breakers.
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.