release-health

Assess release health by traversing Jira Features, Initiatives, Epics, and Tasks to compute schedule, staffing, and refinement health signals. This produces an action-ready health report with prioritized remediation actions to de-risk your branch cut.

Sprint forecasting and risk metrics include schedule slippage, missing ownership or QA contact, missing acceptance criteria, missing sizing, blocked work indicators, and capacity risk evaluated against team roster and sprint policy inputs.

Generate a prioritized remediation action plan by identifying highest-severity risks across staffing, refinement, and capacity dimensions, then projecting sprint outcomes through branch cut to assign specific owners to each required action.

Release health analysis applies directly to OpenShift Edge planning scenarios, requiring inline Jira data fetching via MCP tools and deterministic transformation scripts to JSON before running the final risk and reporting analysis pass.

Avoiding raw Jira payloads in context during release risk assessment ensures the final analysis pass relies on deterministic JSON transformations, preventing context bloat and maintaining accurate schedule, staffing, and refinement health signal computation.

To analyze sprint outcomes for a branch cut, provide the specific release version and the target sprint range when prompted, allowing the skill to traverse the relevant Jira hierarchy and compute actionable health signals.