@@ -88,3 +88,54 @@ Regarding LR_reduction_workflow_config_parameters.xlsx, I have downloaded it to
Please read ~/Desktop/Hack-A-Thon-2026/2026-04-24-Files.md. Please use that information to summarise the Day 3 events and write them to tasking/Hack-A-Thon-2026-Day3-Summary.md for me to review. If there is any other information that you would like to have, please ask me.
### Prompt 1.4
I went ahead and made edits on commit 617c4a40. Thanks, alot! Erik attended when he could - I may not have captured his comments complete in the hand notes. There were also several discussions running concurrently at times. Anyway, what do you propose to do for '4. "Claude estiamte?". You have the git log from the quicknxs and mr_reduction repositories - that represents actual developer effort and progress. What is missing is when the developer actually started paying attention to the task; they may have taken a branch and unless they were dilligent it creating a development branch at the start of attention, the creation of the branch may represent a fairly noisy starting point. I have a spreadsheet, however, that extracts data from the work management system (the EWM system). You may inspect it at ~/Desktop/Hack-A-Thon-2026/Focus-REF-Work-2026-04-25.xlsx, in case it provides some clarify. I apologize for the circular reference warning - it has been troubling me for some time.
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Quick read of the EWM file: the Resolved-REF-Work-2026-04-25 tab has 1,076 completed work items with Estimate, Corrected Estimate,
Time Spent, Creation Date, and Resolution Date for each, and the Focus tab even has placeholder per-developer time-budget tasks
("Glass Time / Kevin Time / Marie Time") that read off available capacity per cycle. That's enough to do this empirically rather than
by gut.
Here's what I propose, in three phases — pick the depth you want:
Phase 1 — empirical baseline (≈30 min, no commitment).
Aggregate the 1,076 Resolved items by Type (Task / Story / Defect / Release) and by a coarse work-class derived from Summary keywords
(refactor, cleanup, test, new-feature, review, release), and produce three numbers per class: median Time Spent, p80 Time Spent, and
the median Time Spent / Estimate ratio (the "estimation honesty" factor — how much developers under-quote). Output: a one-page table