weekly-review

name	weekly-review
description	Use when the user asks for a weekly review, engineering review, team review, or summary of recent commits. Also use for bi-weekly, monthly, or sprint reviews ("review the last N days"). Produces a concise, stakeholder-friendly engineering summary organized by category with contributor stats.
allowed-tools	Bash, Read, Write
argument-hint	[days] [categories]

Weekly Review

What this skill does

Produces a structured engineering review of recent commits, organized by category with 2-5 bullets each plus contributor statistics. Output is plain text with Unicode emojis suitable for copy-paste into email, Slack, Google Docs, or any communication channel. Works for ANY codebase and tech stack — the framework is purely git-based.

The framework is analysis-focused (not prescriptive): it gathers commit data, helps you organize it into meaningful categories, and writes a concise summary in plain language accessible to non-technical stakeholders while still meaningful to engineers.

Defaults

• days: 7 (use 14 for bi-weekly, 30 for monthly)
• categories: 5 (use 3 for shorter reviews, 7 for monthly)

If the user provides args, parse them as <days> [categories]. Otherwise use defaults.

Phase 1: Gather data

Run these git commands in parallel from the repo root to collect commit history:

# Verify you're on the correct branch
git pull && git branch --show-current

# Get all commits
git log --since="<DAYS> days ago" --oneline --no-merges

# Count commits by author
git log --since="<DAYS> days ago" --format="%an" --no-merges | sort | uniq -c | sort -rn

# Get detailed stats (first 500 lines)
git log --since="<DAYS> days ago" --stat --no-merges | head -500

Phase 2: Analyze internally (do not output this)

Think through the commits and organize them. Do NOT produce a long verbose breakdown — this is internal work.

1. Review the commits — read through and understand what changed
2. Identify patterns — group related commits together
3. Choose N categories — pick categories that naturally emerge from the actual work
4. Note key stats — total commits, files changed, contributors, percentages
5. Identify highlights — the most impactful changes

Category examples (pick what fits this period's actual work — do not force-fit):

• 🔐 Credential & Authentication Infrastructure
• 🔌 Third-party Integrations
• 💬 Frontend Performance & UX
• 📧 User Lifecycle Automation
• 🧪 Testing & Code Quality
• 🐛 Bug Fixes & Stability
• ⚙️ DevOps & Infrastructure
• 🏗️ API Development
• 💰 Billing & Payments
• 📊 Reporting & Analytics
• 💾 Database & Data Models
• 🚀 Feature Development

Use descriptive, specific names based on actual work — not generic labels. Prefer "Credential & Authentication Infrastructure" over just "Auth".

Phase 3: Write the final summary

Output format (plain text, Unicode emojis, NO numbered sections):

# Engineering Review - <Date Range>

🔐 **Category Name**
• **Feature/improvement name** - What it does and why it matters for users or the business
• **Another improvement** - The benefit or problem it solves, in plain language
• **Third item** - Focus on impact, not implementation details
[Continue with 2-5 bullets per category]

🔌 **Category Name**
• **Feature name** - Business value and user impact
[2-5 bullets]

[... N categories total ...]

📊 **Team Statistics & Recognition**
• [X] total commits over [N] days ([Z] commits/day average)
• [Additional high-level metrics: features completed, improvements made]
• **[Name]**: [X] commits ([%]%) - [Their focus areas in plain language]
• **[Name]**: [X] commits ([%]%) - [Their focus areas in plain language]

Title date range: ALWAYS show the full requested period (e.g., "Oct 6-13, 2025" for a 7-day review), regardless of when commits actually occurred. The title shows the review period, not the activity period.

Writing guidelines

Each bullet:

• Start with **bold title** then a dash and description
• Focus on WHAT was done and WHY it matters (business impact, user benefit, problem solved)
• Plain language — avoid jargon, code paths, method names, file references
• 1-2 sentences max if needed for clarity
• Test: "Would a product manager, designer, or executive understand this?"

Category selection:

• Choose the N most relevant categories to this period's work
• Order by importance/impact
• NO NUMBERS — just emoji + bold title (e.g., 🔐 **Authentication** not 1. 🔐 Authentication)

Team Statistics section:

• Calculate commit percentages for each contributor
• List in order of commit count (highest first)
• Include 1-2 bullets describing each person's focus areas in plain language
• Add relevant aggregate metrics (files changed, tests added, etc.)

Output expectations

• ✅ Plain text with full Unicode emoji support
• ✅ N categories with emoji + bold title (NO NUMBERS)
• ✅ 2-5 bullets per category in plain language (no code paths, no method names, no file references)
• ✅ Team Statistics section with contributor breakdown
• ✅ Focus on business impact and user benefits, not technical implementation
• ❌ NOT multiple pages of verbose analysis
• ❌ NOT numbered sections
• ❌ NOT technical jargon or code references (apps/path/file.py, function names, etc.)

Final step: save the document

Save to plain text in /tmp/:

• Weekly: /tmp/eng_review_<mon><day>-<day>.txt (e.g., /tmp/eng_review_oct6-13.txt)
• Monthly: /tmp/eng_review_<mon><day>-<mon><day>.txt (e.g., /tmp/eng_review_sep7-oct7.txt)

After saving, offer: open -a TextEdit <path>

Version history

• v1.0.0 (2026-04-29): Imported from cuttlefish apps/ai/mcp/cto_tools/server.py::weekly_review as a self-contained skill (no MCP dependency)