Best use case
weekly-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Weekly project review report
Teams using weekly-review should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/weekly-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How weekly-review Compares
| Feature / Agent | weekly-review | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Weekly project review report
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# PM Weekly Review
> Weekly work review -- hours, tokens, progress
## When to use
- "weekly report"
- "what was done this week"
- At the end of the week
- Before planning the next week
## Paths
| What | Path |
|------|------|
| Tasks | `$PM_PATH/pm_tasks_master.csv` |
| Execution Log | `$PM_PATH/pm_execution_log.csv` |
| Learnings | `$PM_PATH/pm_learnings.csv` |
| Script | `$PROJECT_ROOT/projects/scripts/weekly_report.py` |
## Quick start
```bash
cd $PROJECT_ROOT
python3 projects/scripts/weekly_report.py
```
## Manual analysis
```python
import pandas as pd
from datetime import date, timedelta
# Last 7 days
week_ago = str(date.today() - timedelta(days=7))
today = str(date.today())
tasks = pd.read_csv('$PM_PATH/pm_tasks_master.csv')
log = pd.read_csv('$PM_PATH/pm_execution_log.csv')
learnings = pd.read_csv('$PM_PATH/pm_learnings.csv')
# Filter by date
log_week = log[(log['date'] >= week_ago) & (log['date'] <= today)]
```
## Metrics
```python
# Total hours
total_hours = log_week['duration_minutes'].sum() / 60
print(f"Total hours: {total_hours:.1f}")
# Total tokens
total_tokens = log_week['tokens_total'].sum()
print(f"Total tokens: {total_tokens:,}")
# Completed tasks
completed = tasks[(tasks['status'] == 'done') & (tasks['last_updated'] >= week_ago)]
print(f"Tasks completed: {len(completed)}")
# By activity type
by_type = log_week.groupby('activity_type')['duration_minutes'].sum() / 60
print("\nBy type:")
print(by_type)
# By project
by_project = log_week.groupby('project_id')['duration_minutes'].sum() / 60
print("\nBy project:")
print(by_project)
```
## Report format
```
=== WEEKLY REPORT ===
Period: 2025-01-27 — 2025-02-03
METRICS
* Hours: 12.5
* Tokens: 45,230
* Tasks completed: 8
BY PROJECT
* proj-001 (Client D): 6.5 hrs
* proj-002 (CRM): 4.0 hrs
* proj-003 (Infra): 2.0 hrs
BY TYPE
* coding: 5.5 hrs
* research: 3.0 hrs
* planning: 2.0 hrs
* discussion: 2.0 hrs
COMPLETED TASKS
* [proj-001] Send outreach
* [proj-001] Check responses
* [proj-002] Update CRM schema
...
LEARNINGS
* Telegram has a 60 sec limit between messages
* WhatsApp does not sync history
...
BLOCKED
* [proj-003] task-004: Waiting for Tailscale account
NEXT WEEK
* Finish proj-001
* Start proj-004
```
## Saving the report
```python
report_path = f'$PROJECT_ROOT/reports/weekly_{today}.md'
with open(report_path, 'w') as f:
f.write(report_content)
```
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