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Taylor Eernisse 4185abe05d docs: add feature ideas catalog, time-decay scoring plan, and timeline issue doc

Ideas catalog (docs/ideas/): 25 feature concept documents covering future
lore capabilities including bottleneck detection, churn analysis, expert
scoring, collaboration patterns, milestone risk, knowledge silos, and more.
Each doc includes motivation, implementation sketch, data requirements, and
dependencies on existing infrastructure. README.md provides an overview and
SYSTEM-PROPOSAL.md presents the unified analytics vision.

Plans (plans/): Time-decay expert scoring design with four rounds of review
feedback exploring decay functions, scoring algebra, and integration points
with the existing who-expert pipeline.

Issue doc (docs/issues/001): Documents the timeline pipeline bug where
EntityRef was missing project context, causing ambiguous cross-project
references during the EXPAND stage.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-02-09 10:16:48 -05:00

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MR Pipeline Efficiency

Command: lore mr-pipeline [--since <date>]
Confidence: 78%
Tier: 3
Status: proposed
Effort: medium — builds on bottleneck detector with more stages

What

Track the full MR lifecycle: creation, first review, all reviews complete (threads resolved), approval, merge. Compute time spent in each stage across all MRs. Identify which stage is the bottleneck.

Why

"Our merge process is slow" is vague. This breaks it into stages so teams can target the actual bottleneck. Maybe creation-to-review is fast but review-to-merge is slow (merge queue issues). Maybe first review is fast but resolution takes forever (contentious code).

Data Required

All exists today:

merge_requests (created_at, merged_at)
notes (note_type='DiffNote', created_at, author_username)
discussions (resolved, resolvable, merge_request_id)
resource_state_events (state changes with timestamps)

Implementation Sketch

For each merged MR, compute:

Created → First Review: MIN(DiffNote.created_at) - mr.created_at
First Review → All Resolved: MAX(discussion.resolved_at) - MIN(DiffNote.created_at)
All Resolved → Merged: mr.merged_at - MAX(discussion.resolved_at)

Note: "resolved_at" isn't directly stored but can be approximated from the last note in resolved discussions, or from state events.

Human Output

MR Pipeline (last 30 days, 24 merged MRs)

  Stage                    Median    P75      P90
  Created → First Review   4.2h     12.1h    28.3h
  First Review → Resolved  8.1h     24.5h    72.0h    <-- BOTTLENECK
  Resolved → Merged        0.5h      1.2h     3.1h

  Total (Created → Merged) 18.4h    48.2h    96.1h

  Biggest bottleneck: Review resolution (median 8.1h)
  Suggestion: Consider breaking large MRs into smaller reviewable chunks

Downsides

"Resolved" timestamp approximation may be inaccurate
Pipeline assumes linear flow; real MRs have back-and-forth cycles
Draft MRs skew metrics (created early, reviewed late intentionally)

Extensions

lore mr-pipeline --exclude-drafts — cleaner metrics
Per-project comparison: which project has the fastest pipeline?
Trend line: weekly pipeline speed over time
Break down by MR size (files changed) to normalize

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