Weekly Engineering Retrospective

Generates a comprehensive engineering retrospective analyzing commit history, work patterns, and code quality metrics. Team-aware: identifies the user running the command, then analyzes every contributor with per-person praise and growth opportunities.

Arguments

Default: last 7 days
24h: last 24 hours
14d: last 14 days
30d: last 30 days
compare: compare current window vs prior same-length window

Instructions

Parse the argument to determine the time window. Default to 7 days. All times should be reported in the user's local timezone.

Midnight-aligned windows: For day units, compute an absolute start date at local midnight. For example, if today is 2026-03-18 and the window is 7 days, the start date is 2026-03-11. Use --since="2026-03-11T00:00:00" for git log queries. For hour units, use --since="N hours ago".

Step 1: Gather Raw Data

First, fetch origin and identify the current user:

git fetch origin main --quiet
git config user.name
git config user.email

The name returned by git config user.name is "you" ... the person reading this retro. All other authors are teammates.

Run ALL of these git commands (they are independent):

# All commits with timestamps, subject, hash, author, files changed
git log origin/main --since="<window>" --format="%H|%aN|%ae|%ai|%s" --shortstat

# Per-commit test vs total LOC breakdown with author
git log origin/main --since="<window>" --format="COMMIT:%H|%aN" --numstat

# Commit timestamps for session detection and hourly distribution
git log origin/main --since="<window>" --format="%at|%aN|%ai|%s" | sort -n

# Files most frequently changed (hotspot analysis)
git log origin/main --since="<window>" --format="" --name-only | grep -v '^$' | sort | uniq -c | sort -rn

# PR numbers from commit messages
git log origin/main --since="<window>" --format="%s" | grep -oE '[#!][0-9]+' | sort -t'#' -k1 | uniq

# Per-author file hotspots
git log origin/main --since="<window>" --format="AUTHOR:%aN" --name-only

# Per-author commit counts
git shortlog origin/main --since="<window>" -sn --no-merges

# Test file count
find . -name '*.test.*' -o -name '*.spec.*' -o -name '*_test.*' -o -name '*_spec.*' 2>/dev/null | grep -v node_modules | wc -l

# Test files changed in window
git log origin/main --since="<window>" --format="" --name-only | grep -E '\.(test|spec)\.' | sort -u | wc -l

Step 2: Compute Metrics

Calculate and present these metrics in a summary:

Commits to main: N
Contributors: N
PRs merged: N
Total insertions: N
Total deletions: N
Net LOC added: N
Test LOC (insertions): N
Test LOC ratio: N%
Version range: vX.Y.Z → vX.Y.Z
Active days: N
Detected sessions: N
Avg LOC/session-hour: N

Then show a per-author leaderboard immediately below:

Contributor         Commits   +/-          Top area
You (garry)              32   +2400/-300   browse/
alice                    12   +800/-150    app/services/
bob                       3   +120/-40     tests/

Sort by commits descending. The current user always appears first, labeled "You (name)".

Step 3: Commit Time Distribution

Show hourly histogram in local time:

Hour  Commits  ████████████████
 00:    4      ████
 07:    5      █████
 ...

Identify:

Peak hours
Dead zones
Bimodal pattern (morning/evening) vs continuous
Late-night coding clusters (after 10pm)

Step 4: Work Session Detection

Detect sessions using 45-minute gap threshold between consecutive commits.

Classify sessions:

Deep sessions (50+ min)
Medium sessions (20-50 min)
Micro sessions (<20 min, single-commit)

Calculate:

Total active coding time
Average session length
LOC per hour of active time

Step 5: Commit Type Breakdown

Categorize by conventional commit prefix (feat/fix/refactor/test/chore/docs). Show as percentage bar:

feat:     20  (40%)  ████████████████████
fix:      27  (54%)  ███████████████████████████
refactor:  2  ( 4%)  ██

Flag if fix ratio exceeds 50% ... signals a "ship fast, fix fast" pattern that may indicate review gaps.

Step 6: Hotspot Analysis

Show top 10 most-changed files. Flag:

Files changed 5+ times (churn hotspots)
Test files vs production files in the hotspot list
VERSION/CHANGELOG frequency

Step 7: PR Size Distribution

Estimate PR sizes and bucket them:

Small (<100 LOC)
Medium (100-500 LOC)
Large (500-1500 LOC)
XL (1500+ LOC)

Step 8: Focus Score + Ship of the Week

Focus score: Percentage of commits touching the single most-changed top-level directory. Higher = deeper focused work. Lower = scattered context-switching.

Ship of the week: The single highest-LOC PR in the window. Highlight PR number, LOC changed, and why it matters.

Step 9: Team Member Analysis

For each contributor (including the current user), compute:

Commits and LOC ... total commits, insertions, deletions, net LOC
Areas of focus ... which directories/files they touched most (top 3)
Commit type mix ... their personal feat/fix/refactor/test breakdown
Session patterns ... when they code (peak hours), session count
Test discipline ... their personal test LOC ratio
Biggest ship ... their single highest-impact commit or PR

For the current user ("You"): Deepest treatment. Include all session analysis, time patterns, focus score. Frame in first person.

For each teammate: 2-3 sentences covering what they shipped and their pattern. Then:

Praise (1-2 specific things): Anchor in actual commits. Not "great work" ... say exactly what was good.
Opportunity for growth (1 specific thing): Frame as leveling-up, not criticism. Anchor in actual data.

If solo repo: Skip team breakdown.

AI collaboration: If commits have Co-Authored-By AI trailers, track "AI-assisted commits" as a separate metric.

Step 10: Week-over-Week Trends (if window >= 14d)

Split into weekly buckets and show trends:

Commits per week (total and per-author)
LOC per week
Test ratio per week
Fix ratio per week
Session count per week

Step 11: Streak Tracking

Count consecutive days with at least 1 commit, going back from today:

# Team streak
git log origin/main --format="%ad" --date=format:"%Y-%m-%d" | sort -u

# Personal streak
git log origin/main --author="<user_name>" --format="%ad" --date=format:"%Y-%m-%d" | sort -u

Display both:

"Team shipping streak: 47 consecutive days"
"Your shipping streak: 32 consecutive days"

Step 12: Load History & Compare

Check for prior retro history in memory/:

If prior retros exist, load the most recent one and calculate deltas:

                    Last        Now         Delta
Test ratio:         22%    →    41%         ↑19pp
Sessions:           10     →    14          ↑4
LOC/hour:           200    →    350         ↑75%
Fix ratio:          54%    →    30%         ↓24pp (improving)

If no prior retros exist, note "First retro recorded, run again next week to see trends."

Step 13: Save Retro History

Save a JSON snapshot to memory/retro-YYYY-MM-DD.json with metrics, authors, version range, streak, and tweetable summary.

Step 14: Write the Narrative

Format for Telegram (bullets, bold, no markdown tables in the final output).

Structure:

Tweetable summary (first line):

Week of Mar 1: 47 commits (3 contributors), 3.2k LOC, 38% tests, 12 PRs, peak: 10pm | Streak: 47d

Then sections:

Summary ... key metrics
Trends vs Last Retro ... deltas (skip if first retro)
Time & Session Patterns ... when the team codes, session lengths, deep vs micro
Shipping Velocity ... commit types, PR sizes, fix-chain detection
Code Quality Signals ... test ratio, hotspots, churn
Focus & Highlights ... focus score, ship of the week
Your Week ... personal deep-dive for the current user
Team Breakdown ... per-teammate analysis with praise + growth (skip if solo)
Top 3 Team Wins ... highest-impact things shipped
3 Things to Improve ... specific, actionable, anchored in commits
3 Habits for Next Week ... small, practical, realistic (<5 min to adopt)

Compare Mode

When the user says "compare":

Run the retro for the current window
Run the retro for the prior same-length window
Present side-by-side metrics with arrows showing improvement/regression
Brief narrative on biggest changes

Important Rules

All times in local timezone. Never set TZ.
Format for Telegram. Use bullets and bold. Avoid markdown tables in the final output.
Praise anchored in commits. Never say "great work" without naming what was good.
Growth areas anchored in data. Never criticize without evidence.
Save history. Every retro saves to memory/ for trend tracking.
Completion status:
- DONE ... retro generated, history saved
- DONE_WITH_CONCERNS ... generated but missing data (e.g., no prior retros for comparison)
- BLOCKED ... not in a git repo or no commits in window

gstack-openclaw-retro