# Discover and Vet Influencers Across Platforms

Finding relevant creators across platforms takes time and often produces inconsistent evidence. This skill structures discovery, screening, profiles, and tiered shortlists for campaign planning.

## Install

```bash
npx skillstore add aaron-he-zhu/influencer-discovery
```

## Metadata

- Status: approved
- Slug: aaron-he-zhu-influencer-discovery
- Version: 17.0.0
- Author version: 17.0.0
- Skillstore revision: r1
- Version status: valid
- Tree hash: ddee6810b73c81cc2aa2e95166c1fd1774c685db7a7607850dcc550e1718615e
- 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/discover/influencer-discovery
- 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: env\_access, filesystem, external\_commands, network
- Quality score: 69
- Public page: https://skillstore.pages.dev/skills/aaron-he-zhu-influencer-discovery
- Manifest: https://skillstore.pages.dev/api/skills/aaron-he-zhu-influencer-discovery/manifest

## Capabilities

- Captures campaign goals, platforms, follower ranges, engagement thresholds, locations, and exclusions.
- Organizes multi-platform search methods using hashtags, similar accounts, competitor mentions, and native discovery.
- Screens candidates for relevance, recency, engagement, authenticity, competitor conflicts, and brand safety.
- Builds creator profiles covering visible metrics, audience signals, content style, partnership history, and public contact paths.
- Compiles candidates into must-reach, strong, and consider tiers with preliminary fit signals.
- Produces a discovery report with summary statistics, mix recommendations, insights, and next steps.

## Use Cases

- Launch a New Creator Program: Build an initial creator pool from campaign goals, audience criteria, platform requirements, and brand-safety exclusions.
- Expand Into a New Niche: Research creators in an unfamiliar category and compare visible audience, content, partnership, and authenticity signals.
- Refresh an Agency Shortlist: Screen replacement candidates, document evidence, and prepare a tiered list for client review and deeper scoring.

## Prompt Templates

### Find Creators in a Niche

```
Find [count] influencers in [niche] for [brand or product]. Use public evidence and mark every unverified field as unconfirmed.
```

### Apply Platform Filters

```
Find [count] creators on [platforms] with [follower range], at least [engagement rate], and audiences in [location]. Exclude [criteria].
```

### Build Evidence-Based Profiles

```
Research these creator handles: [handles]. Build profiles with sources, recent content signals, public metrics, partnership history, brand-safety notes, and public contact paths.
```

### Create a Tiered Campaign Shortlist

```
Discover creators for [campaign]. Screen at least [pool size], document rejection reasons, compare platform mix, and produce three shortlist tiers with confidence notes and next steps.
```

## Limitations

- It relies on user inputs and public data unless optional connectors are available.
- It cannot verify private audience demographics, hidden analytics, pricing, or contract terms.
- Its preliminary fit scores require separate weighted review before final ranking.
- Public follower and engagement metrics can change after the report is created.

## Best Practices

- Define required filters and exclusions before searching so every candidate receives consistent screening.
- Cite public sources, separate measured facts from estimates, and mark missing information as unconfirmed.
- Review recent content across platforms before making brand-safety or authenticity judgments.

## Anti Patterns

- Do not invent follower counts, audience demographics, contact details, or partnership history.
- Do not treat preliminary fit signals as a final weighted ranking.
- Do not collect private contacts, bypass login controls, or guess email addresses.

## Security Audit

- Audited at: 2026-07-12T12:33:59.37\+00:00
- Summary: Most static alerts are false positives caused by Markdown code fences, inline code, and relative documentation links. Two findings remain because line 59 executes an unreviewed connector and requires an API key. The workflow also needs safeguards for untrusted public web content and stored creator contact data.

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