Tirosman Demo Skillv0.2.0

Simulate a multi-project software development cycle with tasks moving from backlog to done. Use demo_start to create 150 tasks across 5 projects, then poll demo_status and use demo_qa_approve/reject to manage QA gates.

Demonstrate autonomous coordination between PM, dev, frontend, QA, and CEO agents. Use demo_board to visualize task assignments and progress, and demo_run to resume execution after manual interventions.

Before running the full demo, use demo_estimate to get projected token and cost numbers. This helps users budget for NVIDIA LLM calls and decide whether to proceed with auto_run=true or false.

Train end-users on a Jira-like QA approval process. Use demo_qa_approve and demo_qa_reject to move tasks between qa and in_progress, and observe the lifecycle via real-time webhook notifications.

Test the system's resilience by starting a demo run, then using demo_reset to wipe all tasks and start over. Useful for sandbox environments or repeated demonstrations without data buildup.

Offer the demo as a premium service where customers pay a monthly subscription to run multi-agent demos with real NVIDIA models. Revenue from recurring subscriptions based on tiered usage limits.

Charge customers based on the number of tokens consumed by the underlying LLM calls. Use demo_estimate to provide upfront cost projections and bill post-run.

Provide custom demo sessions and training for enterprises adopting MCP-based multi-agent workflows. Revenue from consulting fees and training workshops.