# Monitor campaign budget pacing

Live campaigns can overspend, underspend, or produce misleading signals during learning. This skill compares spend with a planned curve and gives a clear pacing verdict with a next action.

## Install

```bash
npx skillstore add aaron-he-zhu/budget-pacing-monitor
```

## Metadata

- Status: approved
- Slug: aaron-he-zhu-budget-pacing-monitor
- Version: 19.0.0
- Author version: 19.0.0
- Skillstore revision: r2
- Version status: valid
- Tree hash: 3d29f3ca3204c335a9475b200df0fdb810f872407db02231b0d9c451d205a53e
- Author: aaron-he-zhu
- GitHub username: aaron-he-zhu
- License: Apache-2.0
- Repository: https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/ad/scale/budget-pacing-monitor
- Ref: adcb3549b15782055d0beb6d29f113d20de08f92
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network, filesystem
- Quality score: 81
- Quality tier: silver
- Public page: https://skillstore.pages.dev/skills/aaron-he-zhu-budget-pacing-monitor
- Manifest: https://skillstore.pages.dev/api/skills/aaron-he-zhu-budget-pacing-monitor/manifest

## Capabilities

- Compares cumulative campaign spend with an even, front-loaded, or back-loaded target curve.
- Classifies pacing as On-track, Ahead, Behind, or Stalled.
- Calculates percent-to-pace and projects end-of-flight spend from the current rate.
- Checks whether learning-phase status makes a budget change unsafe.
- Names likely delivery drivers using user-provided campaign export data.
- Recommends a fire or hold reallocation trigger and a next check date.

## Use Cases

- Review a mid-flight campaign: Check whether a campaign is ahead of or behind its planned spend curve before the next budget review.
- Investigate a spend spike: Assess whether an early daily-cap hit is over-delivery and identify delivery signals to review.
- Prepare a stakeholder update: Create a measured pacing summary with a projection, trigger decision, and next check date.

## Prompt Templates

### Check a simple campaign

```
Check pacing for Campaign X. The daily budget is $200, the flight is 30 days, and day 9 has ended. Cumulative spend is $1,650. Use an even target curve.
```

### Review a lifetime budget

```
Review this campaign pacing. Lifetime budget is $12,000 from August 1 through August 31. Today is August 18 and spend is $7,900. The target is front-loaded. State the pace, projection, and next check date.
```

### Diagnose under-delivery

```
Assess under-delivery using this export summary: budget remaining $4,000, 70% of the flight elapsed, 42% spent, learning exited, low search volume, and no daily cap hit. Give the likely driver and reallocation trigger.
```

### Apply learning-phase protection

```
Check pacing for a campaign at 135% of its planned curve. It is still in learning phase after a recent budget edit. Separate measured facts from estimates and decide whether to hold or fire a reallocation trigger.
```

## Limitations

- Requires a user-provided campaign export with spend and flight information.
- Does not access advertising platforms or dashboards by itself.
- Does not calculate a new budget allocation or choose a bid strategy.
- Projections depend on the target curve and the accuracy of the supplied data.

## Best Practices

- Set the target curve before reviewing spend so the comparison reflects the original plan.
- Use exported daily spend data and label every figure as measured, user-provided, or estimated.
- Confirm learning-phase status before acting on an apparent pacing gap.

## Anti Patterns

- Do not judge pacing from a dashboard screenshot or a single total-spend value.
- Do not change budget during learning phase based only on a short-term pace signal.
- Do not present a projected end-of-flight result as measured performance.

## Security Audit

- Audited at: 2026-07-26T09:59:28.672\+00:00
- Summary: Most static alerts are false positives caused by Markdown code formatting, relative documentation links, and descriptive advertising-analysis text. One documented ledger command interpolates a campaign placeholder without quoting or validation, which can permit shell command injection if executed with adversarial input. No prompt-injection language or data-exfiltration intent was found.

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