gstack-openclaw-retro

name gstack-openclaw-retro description Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware with per-person contributions, praise, and growth areas. Use when asked for weekly retro, what shipped this week, or engineering retrospective. 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 git ls-files 2>/dev/null | grep -E '(\.test\.|\.spec\.|_test\.|_spec\.)' | 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