Budget Tracker

/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: 120%, immediate action: reduce bids, pause low-performers, set daily caps), overpace warning (110-120%, proactive adjustments this week), underspend warning (<70%, increase bids or expand targeting or reallocate), underspend info (70-85%, monitor).

1. Pull efficiency metrics: For each platform, retrieve CPA, ROAS, conversion volume, and cost per conversion

so reallocation decisions are performance-informed, not just pacing-based.

1. Recommend reallocations: Execute scripts/budget-optimizer.py with current spend

efficiency data to suggest specific dollar-amount shifts from underspending or low-efficiency platforms to high-performing ones with room to scale. Include rationale for each recommended move.

1. Save budget snapshot: Persist the current pacing snapshot via

scripts/performance-monitor.py --brand {slug} --action save-snapshot for historical tracking, trend analysis, and comparison in future budget-tracker runs.

Output

A structured budget dashboard containing:

• Budget summary: Total budget for the period, total spent to date, total remaining, overall pacing

percentage, days elapsed, days remaining, projected end-of-period total, and overall health status (on track, overpacing, underpacing)

• Per-platform spend table: Platform name, budget target, actual spend to date, pacing percentage,

daily burn rate (7-day avg), projected end-of-period spend, variance from target ($ and %), and status flag (green/yellow/red)

• Pacing visualization data: Daily spend trajectory vs ideal linear pacing for each platform —

highlights where spend is accelerating, decelerating, or tracking evenly across the period

• Overspend/underspend alerts: Priority-ordered list of pacing issues with severity, platform,

current pacing %, projected variance, and specific recommended corrective action

• Reallocation recommendations: Specific dollar-amount shifts between platforms with rationale — e.g.,

"Move $2,000 from LinkedIn (62% pacing, $85 CPA) to Google Ads (98% pacing, $22 CPA, room to scale)"

• Efficiency context: Per-platform CPA, ROAS, conversion volume, and cost trend alongside spend data

so budget decisions account for performance quality, not just pacing

• Daily burn rate breakdown: Current daily spend per platform vs target daily spend, with 7-day trend

direction and acceleration/deceleration indicator

• Projection scenarios: Best-case, expected, and worst-case end-of-period spend projections per platform

and in aggregate, with confidence ranges

• Executive summary: 2-3 sentence overview — total budget health, biggest risk or opportunity, and the

single most important action to take now

Agents Used

• performance-monitor-agent — Spend data aggregation from connected ad MCPs, pacing calculations, projection modeling, snapshot persistence, and historical spend trend analysis
• media-buyer — Budget optimization strategy, reallocation recommendations, platform-specific spend tactics (bid strategies, daily caps, audience expansion), and auction dynamics expertise

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: indranilbanerjee
• Source: indranilbanerjee/digital-marketing-pro
• License: MIT
• Homepage: https://indranil.in

Install and usage instructions live in the source repository linked above.