# Track Competitor Influencer Campaigns

Competitor influencer activity is difficult to compare across creators, campaigns, and platforms. This skill organizes evidence, estimates performance, and ranks strategic gaps.

## Install

```bash
npx skillstore add aaron-he-zhu/competitor-tracker
```

## Metadata

- Status: approved
- Slug: aaron-he-zhu-competitor-tracker
- Version: 17.0.0
- Author version: 17.0.0
- Skillstore revision: r1
- Version status: valid
- Tree hash: 1910a159b804d7f5c87af985338b2df7647e5b80bfbab339bab3f4d6d97800a5
- 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/competitor-tracker
- 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, env\_access
- Quality score: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/aaron-he-zhu-competitor-tracker
- Manifest: https://skillstore.pages.dev/api/skills/aaron-he-zhu-competitor-tracker/manifest

## Capabilities

- Defines the tracked brand, competitor set, platforms, date range, and research focus.
- Builds competitor creator rosters with handles, audience size, partnership type, and duration.
- Breaks down campaign timing, platform mix, content formats, calls to action, and estimated spend.
- Compares brands by creator tier, platform, engagement, content style, and relationship model.
- Estimates reach, engagement, earned media value, and spend while labeling figures as estimates.
- Ranks creator, platform, niche, and content-format opportunities from observed competitor gaps.

## Use Cases

- Benchmark a Brand Program: Compare your creator mix, platforms, campaign cadence, and estimated performance against selected competitors.
- Map Category Campaign Patterns: Review competitor launches, seasonal timing, creative formats, messages, and calls to action across a defined period.
- Find Strategic Openings: Identify underused creators, platforms, niches, and content formats before planning the next campaign.

## Prompt Templates

### Track One Competitor

```
Track [competitor] influencer activity on [platform] from [start date] to [end date]. List partners, campaigns, sources, and clearly labeled estimates.
```

### Compare Rival Programs

```
Compare [your brand] with [competitor 1] and [competitor 2]. Evaluate creator tiers, platforms, campaign cadence, content formats, and estimated engagement.
```

### Analyze a Campaign Shift

```
Analyze how [competitor] changed its influencer strategy between [period 1] and [period 2]. Cite evidence and separate observations from estimates.
```

### Build an Opportunity Brief

```
Assess [competitor list] in [category]. Rank creator, platform, niche, and format gaps by evidence strength, expected impact, and implementation difficulty.
```

## Limitations

- Results depend on public information, user-provided evidence, and available connectors.
- Reach, spend, earned media value, and performance figures are estimates unless verified data is supplied.
- Connector commands require a compatible host, bundled scripts, and optional service credentials.
- Partnership rosters can become outdated and require a defined monitoring cadence.

## Best Practices

- Define competitors, platforms, dates, and evidence standards before collecting data.
- Cite public sources and label every inferred metric or relationship as an estimate.
- Repeat the review on a consistent schedule to detect meaningful strategy changes.

## Anti Patterns

- Do not present estimated spend, reach, or earned media value as verified facts.
- Do not infer exclusivity or partnership status from a single ambiguous post.
- Do not compare brands across different periods or platforms without explaining the mismatch.

## Security Audit

- Audited at: 2026-07-12T11:21:27.411\+00:00
- Summary: Most static alerts are false positives caused by Markdown fences, inline code, emoji, and fixed relative documentation links. Two instructions intentionally run bundled Python connectors, and one accesses a public YouTube feed; these are expected features but create limited command and network exposure.

## Stats

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- Downloads: 7
- Favorites: 0
- Popularity score: 0