budget-tracker

Solid

Track budget pacing in real time. Use when: cross-platform spend tracking, overspend alerts, reallocation recommendations.

AI & Automation 136 stars 37 forks Updated 3 days ago MIT

Install

View on GitHub

Quality Score: 87/100

Stars 20%
71
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# /digital-marketing-pro:budget-tracker ## Purpose Track advertising budget in real-time across all connected ad platforms. Analyze spend pacing against targets, project end-of-period totals, flag overspend risks and underspend inefficiencies, calculate daily burn rates, and recommend budget reallocations to maximize ROI within the remaining budget window. Designed for media buyers and marketing managers who need a single view of where money is going and whether it is being spent effectively. ## Input Required The user must provide (or will be prompted for): - **Budget period**: This month, this quarter, or a custom date range (e.g., "Feb 1 - Mar 31"). Determines the pacing denominator and projection horizon - **Ad platforms to include**: All connected platforms or specific ones (e.g., "Google Ads and Meta only"). Defaults to all connected ad MCPs - **Budget targets per platform** (optional): Specific spend targets per platform for the period. If omitted, targets are pulled from `profile.json` budget_range and any saved platform allocations - **Total budget** (optional): Overall budget cap for the period. If omitted, pulled from `profile.json` budget_range - **Alert thresholds** (optional): Custom thresholds for overpace (default: >110% of expected pacing) and underspend (default: <70% of expected pacing) flags - **Include efficiency metrics** (optional): Whether to pull CPA, ROAS, and conversion data alongside spend. Defaults to yes ## Process 1. **Load br...

Details

Author
indranilbanerjee
Repository
indranilbanerjee/digital-marketing-pro
Created
4 months ago
Last Updated
3 days ago
Language
Python
License
MIT

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category