Can Jarvis Write Sprint Retrospective and Planning Docs?

Yes. Jarvis (getjarvis.eu), the screen-aware desktop AI assistant for macOS 12+, Windows 10+, and Linux, drafts sprint retrospectives and planning docs by reading the sprint's real history from your tools. Press Cmd+/ (macOS) or Ctrl+/ (Windows) and Jarvis pulls completed and carried-over Linear or Jira issues, merged GitHub PRs, and relevant Slack threads to summarize what shipped, what slipped, and why. It routes the writing to frontier models from Anthropic, OpenAI, or Google and structures retros into What went well, What did not, and Action items. For planning, it drafts a candidate backlog with estimates you confirm. Jarvis will not invent velocity it cannot measure or auto-assign work. It saves docs to Notion or a Google Doc. Pro is $16/month with a free plan (40 requests/week); persistent memory keeps your ceremony formats consistent.

A retro built from memory misses things. Jarvis reconstructs the sprint from data: closed and reopened Linear or Jira tickets, merged GitHub pull requests, and Slack threads where blockers surfaced. It summarizes completed work, items carried over, and where scope changed mid-sprint. Because Jarvis is screen-aware, you can have the board open and ask it to factor in what is visible. It organizes findings into What went well, What could improve, and concrete Action items with owners. Jarvis surfaces patterns, like recurring estimation misses, but presents them as observations for the team to discuss, not verdicts.

For sprint planning, Jarvis drafts a candidate set of issues from the backlog, factoring in priorities, dependencies, and the team's recent throughput where that data exists. It proposes a sprint goal and groups work by theme. The drafting runs on frontier models from Anthropic, OpenAI, or Google. Jarvis will not commit the team to a velocity it cannot justify from history; it presents a proposal you adjust. It can carry forward unfinished items from the retro automatically. Estimates remain yours to set, and Jarvis flags where it lacks the data to suggest one rather than fabricating a number.

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