# Optimize Influencer Marketing Budgets

Influencer campaign budgets are difficult to balance across creators, platforms, and content formats. This skill builds allocations, projections, scenarios, and reallocation recommendations from your inputs.

## Install

```bash
npx skillstore add aaron-he-zhu/budget-optimizer
```

## Metadata

- Status: approved
- Slug: aaron-he-zhu-budget-optimizer
- Version: 17.0.0
- Author version: 17.0.0
- Skillstore revision: r1
- Version status: valid
- Tree hash: 21d1be0e4536204e21bcd9415f3240e0856d316c324ce1c44cd97883385eb2ba
- 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/influencer/plan/budget-optimizer
- Ref: d71c7417a35d5c2624161bd2fe8de8a41a362128
- 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: filesystem, external\_commands, network
- Quality score: 70
- Public page: https://skillstore.pages.dev/skills/aaron-he-zhu-budget-optimizer
- Manifest: https://skillstore.pages.dev/api/skills/aaron-he-zhu-budget-optimizer/manifest

## Capabilities

- Allocates spend across influencer tiers, platforms, content formats, amplification, tools, gifting, and contingency.
- Applies built-in Instagram, TikTok, and YouTube cost benchmarks with industry multipliers.
- Projects reach, impressions, engagements, EMV, CPM, CPE, CPC, ROI, and ROAS when inputs support them.
- Compares conservative, recommended, and aggressive budget scenarios with a stated recommendation.
- Identifies concentration, contingency, cost-efficiency, and creator-selection budget risks.
- Recommends mid-campaign reallocations when spend and creator performance data are provided.

## Use Cases

- Plan a Product Launch: Build a platform and creator-tier allocation with projected reach, engagement, and contingency.
- Compare Client Budget Options: Present three spend scenarios with costs, expected outcomes, tradeoffs, and a recommended option.
- Reallocate Active Campaign Spend: Compare planned and actual results, then move remaining funds toward stronger creators or channels.

## Prompt Templates

### Create a Basic Allocation

```
Allocate [budget] across [platforms] for a [campaign goal] influencer campaign. Include creator tiers, content, amplification, and contingency.
```

### Compare Budget Scenarios

```
Compare [low], [target], and [high] budgets for [audience] on [platforms]. Recommend one scenario and explain the tradeoffs.
```

### Project Campaign Returns

```
Optimize [budget] using these rates and performance assumptions: [data]. Project CPM, CPE, conversions, ROI, and ROAS. Label every metric source.
```

### Reallocate Mid-Campaign Spend

```
Review this planned allocation, spend to date, and creator performance: [data]. Reallocate the remaining budget while preserving [constraints]. Quantify expected impact.
```

## Limitations

- Built-in costs and performance projections are estimates until replaced with current rate cards or measured data.
- The skill does not create the full campaign strategy or recruit influencers.
- It does not read live ad pacing, change bids, or execute budget changes.
- Optional connector data depends on connector availability and the quality of source records.

## Best Practices

- Provide current rate cards, target platforms, audience, timeline, goal, and fixed costs before requesting an allocation.
- Label all numbers as Measured, User-provided, or Estimated, and document every major assumption.
- Keep five to ten percent as contingency and review performance before reallocating active spend.

## Anti Patterns

- Do not treat benchmark ranges as guaranteed creator prices or campaign results.
- Do not place more than forty percent of the budget with one creator without documenting concentration risk.
- Do not execute reallocations from projections alone when current performance data is available.

## Security Audit

- Audited at: 2026-07-12T11:00:27.218\+00:00
- Summary: All 34 static findings are false positives caused by Markdown code fences, inline code, relative documentation links, marketing terminology, and repository metadata URLs. No shell execution, network request, system reconnaissance, or executable path traversal is present. One medium contextual risk remains because the output filename includes an unconstrained topic value.

## Stats

- Views: 1
- Downloads: 4
- Favorites: 0
- Popularity score: 0