The 30-day pilot loop

We compare real stories before and after lightweight story-memory capture for lost decisions, unresolved blockers, missing evidence, unclear next steps, and repeated investigation.

  • Week 1: pick 3-5 active Jira stories where intent, handoff, review, or restart risk is visible.
  • Weeks 2-3: engineers capture lightweight story memory during real AI-assisted work.
  • Week 4: product owners, engineers, reviewers, and leads compare before-and-after story records.
  • Decision: continue, adjust, or stop with evidence from your team's stories.

What you review on Friday

Instead of asking the team to replay the week, inspect what changed, why it changed, and what decision is needed next.

Jira status

In Progress. Two PRs. One stale comment. Activity is visible, but product intent is not.

Story memory

Current bet, changed acceptance criteria, evidence, blocker, and next decision are attached to the story.

Product decision

Narrow the slice, approve the next step, reopen an assumption, or stop the wrong bet.

STORY-123 story memory

Ready for handoff
Product intent

Validate whether AI-assisted review can reduce repeated explanation loops.

Current plan

Run lightweight HDD on two active stories before expanding.

Decision change

Narrowed the first slice after acceptance evidence showed the original scope was too broad.

Evidence

Product owner and reviewer could inspect the decision path without replaying the AI chat.

Blocker

Security review needed for repo-local story notes.

Next decision

Decide whether to approve the next slice or reopen the assumption.

  1. Under the hood: imdone pull imdone hdd --light STORY-123 imdone push
  2. Optional: load the same story memory into a coding-agent session with imdone ai STORY-123

Who should apply

Best fit

  • Your team uses Jira and at least one AI coding agent on real work.
  • Product review or handoff often requires replaying tickets, chat, commits, or AI sessions.
  • One active team can test the habit on real stories for 30 days.

Not a fit

  • You want broad productivity benchmarking.
  • You cannot let story notes live near the repo.
  • You need a company-wide process rollout first.

What is included

  • Qualification call for team fit, Jira workflow, AI-tool use, security constraints, and timing.
  • Setup for one Jira project, up to two repositories, and lightweight HDD story memory.
  • Onboarding for product owners, engineers, reviewers, and leads.
  • Weekly evidence review and an end-of-pilot continue, adjust, or stop report.

Run the pilot on one active Jira team

Start with a qualification call. We will confirm Jira fit, AI-agent usage, security constraints, and whether your current stories can produce useful evidence in 30 days.

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