chore: regenerate Codex/agents SKILL.md for retro shareable card

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Garry Tan
2026-03-22 13:48:16 -07:00
parent e260bf7521
commit dc743917f4
+69 -6
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@@ -830,26 +830,89 @@ From the discovery JSON, analyze tool usage patterns:
### Global Step 7: Aggregate and generate narrative
Structure the output as:
Structure the output with the **shareable personal card first**, then the full
team/project breakdown below. The personal card is designed to be screenshot-friendly
— everything someone would want to share on X/Twitter in one clean block.
---
**Tweetable summary** (first line, before everything else):
```
Week of Mar 14: 5 projects, 182 commits, 15.3k LOC | CC: 48, Codex: 8, Gemini: 3 | Focus: gstack (58%) | Streak: 52d
Week of Mar 14: 5 projects, 138 commits, 250k LOC across 5 repos | 48 AI sessions | Streak: 52d 🔥
```
## 🚀 Your Week: [user name] — [date range]
This section is the **shareable personal card**. It contains ONLY the current user's
stats — no team data, no project breakdowns. Designed to screenshot and post.
Use the user identity from `git config user.name` to filter all per-repo git data.
Aggregate across all repos to compute personal totals.
Render as a single visually clean block:
```
╔══════════════════════════════════════════════════════════════╗
║ [USER NAME] — Week of [date] ║
╠══════════════════════════════════════════════════════════════╣
║ ║
║ [N] commits across [M] projects ║
║ +[X]k LOC added · [Y]k LOC deleted · [Z]k net ║
║ [N] AI coding sessions (CC: X, Codex: Y, Gemini: Z) ║
║ [N]-day shipping streak 🔥 ║
║ ║
║ PROJECTS ║
║ ──────────────────────────────────────────────────────────── ║
║ [repo1] [N] commits +[X]k LOC [role: solo/team] ║
║ [repo2] [N] commits +[X]k LOC [role: solo/team] ║
║ [repo3] [N] commits +[X]k LOC [role: solo/team] ║
║ ... ║
║ ║
║ SHIP OF THE WEEK ║
║ [PR title] — [LOC] lines across [N] files ║
║ ║
║ TOP WORK ║
║ • [1-line description of biggest theme] ║
║ • [1-line description of second theme] ║
║ • [1-line description of third theme] ║
║ ║
║ Powered by gstack /retro global ║
╚══════════════════════════════════════════════════════════════╝
```
**Rules for the personal card:**
- Only show repos where the user has commits. Skip repos with 0 commits.
- Sort repos by user's commit count descending.
- For LOC, use "k" formatting for thousands (e.g., "+64.0k" not "+64010").
- Role: "solo" if user is the only contributor, "team" if others contributed.
- Ship of the Week: the user's single highest-LOC PR across ALL repos.
- Top Work: 3 bullet points summarizing the user's major themes, inferred from
commit messages. Not individual commits — synthesize into themes.
E.g., "Built /retro global — cross-project retrospective with AI session discovery"
not "feat: gstack-global-discover" + "feat: /retro global template".
- The card must be self-contained. Someone seeing ONLY this block should understand
the user's week without any surrounding context.
- Do NOT include team members, project totals, or context switching data here.
**Personal streak:** Use the user's own commits across all repos (filtered by
`--author`) to compute a personal streak, separate from the team streak.
---
## Global Engineering Retro: [date range]
### Overview
Everything below is the full analysis — team data, project breakdowns, patterns.
This is the "deep dive" that follows the shareable card.
### All Projects Overview
| Metric | Value |
|--------|-------|
| Projects active | N |
| Total commits (all repos) | N |
| Total commits (all repos, all contributors) | N |
| Total LOC | +N / -N |
| AI coding sessions | N (CC: X, Codex: Y, Gemini: Z) |
| Active days | N |
| Global shipping streak | N consecutive days |
| Global shipping streak (any contributor, any repo) | N consecutive days |
| Context switches/day | N avg (max: M) |
### Per-Project Breakdown
@@ -882,7 +945,7 @@ Format:
```
### Cross-Project Patterns
- Time allocation across projects (% breakdown)
- Time allocation across projects (% breakdown, use YOUR commits not total)
- Peak productivity hours aggregated across all repos
- Focused vs. fragmented days
- Context switching trends