What problem does it solve?
It turns messy Jira sprint data into a clear, evidence-based sprint health report so teams can quickly understand delivery performance, flow efficiency, risks, and next-sprint improvements.
Core Features & Use Cases
- Sprint health reporting: Generates a comprehensive report that covers delivery, scope stability, flow efficiency, story sizing, work distribution, blocker analysis, backlog health, and delivery predictability.
- Anti-pattern detection: Flags issues like zombie items, perpetual carryover, work concentration, and missing acceptance criteria based on the available data.
- Jira or CSV inputs: Ingests sprint data from Jira (via MCP) or from CSV, then outputs styled HTML and/or Markdown artifacts for stakeholders.
- Quality and guardrails: Measures data quality coverage, uses safe fallbacks when fields are missing, and avoids speculative conclusions.
- Optional changelog enrichment: Optionally analyzes targeted changelogs to support findings like repurposing or reassignment churn.
Quick Start
Ask for a sprint health report by telling the AI which Jira project key or sprint name you mean, for example: "Generate the sprint health report for project PROJ and sprint 'Sprint 12' as HTML."