Marketing Team

---
name: marketing-brand-voice
description: Review and improve marketing copy so it matches our brand voice, audience, and campaign positioning.
---

# Marketing Brand Voice

Use this skill when the user wants help drafting, reviewing, or refining marketing copy.

## When to use

Use this skill for:

- email campaigns
- landing page copy
- launch messaging
- ad copy
- product announcements

Do not use this skill for legal review, pricing approval, or final publishing decisions.

## Brand voice rules

- Write in a clear, confident, direct tone.
- Avoid buzzwords and vague claims.
- Prefer concrete value over hype.
- Keep language simple and easy to scan.
- Focus on customer outcomes, not internal features alone.

## Review checklist

When reviewing copy:

1. Identify the intended audience.
2. Check whether the main message is clear in the first few lines.
3. Remove fluff, repetition, and generic phrases.
4. Rewrite lines that sound off-brand or too technical.
5. Make sure the call to action is specific.

## Output format

When the user asks for review:

1. Summarize what is working.
2. List the biggest issues.
3. Provide an improved version.
4. Explain major edits briefly.

## Constraints

- Do not invent customer results or metrics.
- Do not make legal, compliance, or pricing claims unless the user explicitly provides them.
- If brand guidance conflicts with the user's request, point out the conflict clearly.

import argparse
import csv
from datetime import datetime, timedelta
from pathlib import Path


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--csv_path", required=True, help="Path to campaign metrics CSV file")
    parser.add_argument("--days", type=int, default=7, help="How many recent days to include")
    return parser.parse_args()


def load_rows(csv_path: Path):
    with csv_path.open("r", encoding="utf-8") as handle:
        reader = csv.DictReader(handle)
        return list(reader)


def main():
    args = parse_args()
    csv_path = Path(args.csv_path)

    if not csv_path.exists():
        raise SystemExit(f"Metrics file not found: {csv_path}")

    rows = load_rows(csv_path)
    cutoff = datetime.utcnow().date() - timedelta(days=args.days)

    filtered = []
    for row in rows:
        row_date = datetime.strptime(row["date"], "%Y-%m-%d").date()
        if row_date >= cutoff:
            filtered.append(row)

    if not filtered:
        print("No campaign data found for the selected date range.")
        return

    total_impressions = sum(int(row.get("impressions", 0) or 0) for row in filtered)
    total_clicks = sum(int(row.get("clicks", 0) or 0) for row in filtered)
    total_signups = sum(int(row.get("signups", 0) or 0) for row in filtered)
    total_spend = sum(float(row.get("spend", 0) or 0) for row in filtered)

    ctr = (total_clicks / total_impressions * 100) if total_impressions else 0.0
    signup_rate = (total_signups / total_clicks * 100) if total_clicks else 0.0
    cost_per_signup = (total_spend / total_signups) if total_signups else 0.0

    print("Weekly Marketing Report")
    print("=======================")
    print(f"Date range: last {args.days} days")
    print(f"Campaign rows analyzed: {len(filtered)}")
    print(f"Impressions: {total_impressions}")
    print(f"Clicks: {total_clicks}")
    print(f"CTR: {ctr:.2f}%")
    print(f"Signups: {total_signups}")
    print(f"Signup rate: {signup_rate:.2f}%")
    print(f"Spend: ${total_spend:.2f}")
    print(f"Cost per signup: ${cost_per_signup:.2f}")


if __name__ == "__main__":
    main()