* fix: default cross-model workflows to frontier models * chore: bump version and changelog (v1.82.1.0) Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: repair frontier eval budgets and workflow instructions Preserve frontier models and quality thresholds while fixing truncated judge output, ordered section expansion, consent checks, QA scoring, and ship audit gates. Add regression coverage and refresh generated docs. Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: resolve workflow gaps exposed by frontier evals Clarify plan-review ordering and fallback modes, preserve deploy readiness gates, honor configured merge methods, correct benchmark and canary contracts, and restore vendored installs on setup failure. Cover recovery with real-shell regressions. Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: use agent capture budgets for deploy evals Multi-turn deploy and benchmark sessions were incorrectly limited to the single-call judge timeout. Use the existing capture tier and leave outer-test cleanup headroom, with a free policy regression test. Keep all behavioral assertions and frontier models unchanged. Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: clarify retro workflow and evaluate compare instructions Include compare mode in the frontier judge excerpt, define metric sources and snapshot ordering, and preserve the existing prompt-size budget. Co-authored-by: OpenAI Codex <noreply@openai.com> * fix: make documentation release review and publication consistent Review before commit, clarify changelog safeguards and unavailable reviewer modes, and preserve raw PR bodies across separate shell calls. Keep title sync in one shell and add regression coverage. Co-authored-by: OpenAI Codex <noreply@openai.com> --------- Co-authored-by: OpenAI Codex <noreply@openai.com>
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Structure the output as:
Tweetable summary (first line, before everything else):
Week of Mar 1: 47 commits (3 contributors), 3.2k LOC, 38% tests, 12 PRs, peak: 10pm | Streak: 47d
Engineering Retro: [date range]
Summary Table
(from Step 2)
Trends vs Last Retro
(from Step 12, loaded before save — skip if no matching history; in compare mode use Current vs Prior Period from the computed prior window even on the first run)
Time & Session Patterns
(from Steps 3-4)
Narrative interpreting what the team-wide patterns mean:
- When the most productive hours are and what drives them
- Whether sessions are getting longer or shorter over time
- Estimated hours per day of active coding (team aggregate)
- Notable patterns: do team members code at the same time or in shifts?
Shipping Velocity
(from Steps 5-7)
Narrative covering:
- Commit type mix and what it reveals
- PR size distribution and what it reveals about shipping cadence
- Fix-chain detection (sequences of fix commits on the same subsystem)
- Version bump discipline
Code Quality Signals
- Test LOC ratio trend
- Hotspot analysis (are the same files churning?)
- Greptile signal ratio and trend (if history exists): "Greptile: X% signal (Y valid catches, Z false positives)"
Test Health
- Total test files: N (
TEST_FILES_TOTAL) - Test files changed this period: M (
TEST_FILES_CHANGED; not newly added test cases) - Regression test commits: list the
REGRESSION_COMMITlines (test(qa):,test(design):, andtest: coveragecommits) - If prior retro exists and has
test_health: show delta "Test count: {last} → {now} (+{delta})" - If test ratio < 20%: flag as growth area — "100% test coverage is the goal. Tests make vibe coding safe."
Plan Completion
Check review JSONL logs for plan completion data from /ship runs this period:
setopt +o nomatch 2>/dev/null || true # zsh compat
eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null)"
cat ~/.gstack/projects/$SLUG/*-reviews.jsonl 2>/dev/null | grep '"skill":"ship"' | grep '"plan_items_total"' || echo "NO_PLAN_DATA"
If plan completion data exists within the retro time window:
- Count branches shipped with plans (entries that have
plan_items_total> 0) - Compute average completion: sum of
plan_items_done/ sum ofplan_items_total - Identify most-skipped item category if data supports it
Output:
Plan Completion This Period:
{N} branches shipped with plans
Average completion: {X}% ({done}/{total} items)
If no plan data exists, skip this section silently.
Focus & Highlights
(from Step 8)
- Focus score with interpretation
- Ship of the week callout
Shipping Streaks
(from Step 11: team and personal streaks, including broken-streak disclosure)
Shortcut Debt
(from Step 11.5: marker ledger and count, or the clean-ledger statement)
Your Week (personal deep-dive)
(from Step 9, for the current user only)
This is the section the user cares most about. Include:
- Their personal commit count, LOC, test ratio
- Their session patterns and peak hours
- Their focus areas
- Their biggest ship
- What you did well (2-3 specific things anchored in commits)
- Where to level up (1-2 specific, actionable suggestions)
Team Breakdown
(from Step 9, for each teammate — skip if solo repo)
For each teammate (sorted by commits descending), write a section:
[Name]
- What they shipped: 2-3 sentences on their contributions, areas of focus, and commit patterns
- Praise: 1-2 specific things they did well, anchored in actual commits. Be genuine — what would you actually say in a 1:1? Examples:
- "Cleaned up the entire auth module in 3 small, reviewable PRs — textbook decomposition"
- "Added integration tests for every new endpoint, not just happy paths"
- "Fixed the N+1 query that was causing 2s load times on the dashboard"
- Opportunity for growth: 1 specific, constructive suggestion. Frame as investment, not criticism. Examples:
- "Test coverage on the payment module is at 8% — worth investing in before the next feature lands on top of it"
- "Most commits land in a single burst — spacing work across the day could reduce context-switching fatigue"
- "All commits land between 1-4am — sustainable pace matters for code quality long-term"
AI collaboration note: If many commits have Co-Authored-By AI trailers (e.g., Claude, Copilot), note the AI-assisted commit percentage as a team metric. Frame it neutrally — "N% of commits were AI-assisted" — without judgment.
Top 3 Team Wins
Identify the 3 highest-impact things shipped in the window across the whole team. For each:
- What it was
- Who shipped it
- Why it matters (product/architecture impact)
3 Things to Improve
Specific, actionable, anchored in actual commits. Mix personal and team-level suggestions. Phrase as "to get even better, the team could..."
3 Habits for Next Week
Small, practical, realistic. Each must be something that takes <5 minutes to adopt. At least one should be team-oriented (e.g., "review each other's PRs same-day").
Week-over-Week Trends
(if applicable, from Step 10)