Read sprint plan and story files to compute progress and burndown status.
npx skills add https://github.com/dnkrow/skill --skill sprint-status-dnkrow
Please help me install this Agent Skill. Skill: sprint-status Source: https://github.com/dnkrow/skill/tree/main/claude-global/sprint-status Command: npx skills add https://github.com/dnkrow/skill --skill sprint-status-dnkrow
Sprint teams often need an immediate answer to how the sprint is going, but manually reviewing plans and story files is slow and error-prone.
production/sprint-status.yaml as the authoritative source, then falls back to lightweight markdown scanning when it’s missing.Ask for a sprint update by running /sprint-status with no argument to check the current sprint.
High-intent search queries and answers about installing and using this skill.
To get a sprint health snapshot, this tool reads your sprint plan and story status sources from disk. It uses production/sprint-status.yaml as the authoritative status source, falling back to lightweight markdown scanning when that file is missing.
Burndown calculation works by computing story completion percentages against time consumed in the sprint. Risk escalation triggers when in-progress stories appear stale, labeling the sprint as On Track, At Risk, or Behind.
The best way to check sprint progress is to request a sprint update, which scans your production/sprints/ directory to locate files and summarizes progress concisely in a snapshot kept to thirty lines.
You can check a specific sprint number by providing it as an argument when requesting a sprint update. Running without an argument defaults to checking the current ongoing sprint's health and progress.
You do not need a sprint-status.yaml file to generate a sprint update. The tool prefers it as the authoritative source but performs status, staleness, and burndown calculations by falling back to lightweight markdown scanning.
A stale story risk escalation appears because the status assessment detected in-progress stories that have not been updated recently. This mechanism flags potential delays by computing completion versus time consumed.
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