What problem does it solve? Teams lack objective data on how they actually deliver: velocity, estimation accuracy, sprint completion, AI-tool adoption, and documentation clarity are usually guessed rather than measured. This Skill turns Jira/Azure DevOps history, GitHub/Azure Repos activity, and Confluence/Notion pages into a calibration profile that grounds planning and coaching in real delivery data. ## Core Features & Use Cases - Delivery Analysis: Computes velocity with standard deviation, story-point calibration, estimation accuracy, and sprint completion per tracker (Jira and Azure DevOps kept separate, with a comparison table when both run). - AI Adoption & Docs Scanning: Detects AI-tool markers in commits/PRs as a lower-bound adoption footprint, and scores Confluence/Notion pages for clarity plus stylometric AI-likelihood. - Coaching Insights & Calibration: Produces start/stop/keep/try coaching insights and saves a profile that automatically calibrates future plan generation; supports member subsets and component-scoped runs (delivery/code/docs). - Use Case: A scrum master asks how the team is really performing before sprint planning. The Skill pages the tracker, analyzes the last 8 closed sprints, and returns velocity, estimation accuracy, AI-usage footprint, and coaching insights. ## Quick Start Ask the assistant to analyze the team's Jira history over the last eight sprints and show velocity, estimation accuracy, and AI adoption.
