Skills influencer-discovery

📦

influencer-discovery

Name: influencer-discovery
Author: aaron-he-zhu

v17.0.0 Content revision r1 Medium Risk 🔑 Env variables📁 Filesystem access⚙️ External commands🌐 Network access

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.

Supports: Claude Codex Code(CC)

📊 69 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "influencer-discovery" from https://skillstore.io/skills/aaron-he-zhu-influencer-discovery.md and its manifest at https://skillstore.io/api/skills/aaron-he-zhu-influencer-discovery/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/aaron-he-zhu-influencer-discovery.md Signed manifest GET /api/skills/aaron-he-zhu-influencer-discovery/manifest Signed lockfile GET /api/skills/aaron-he-zhu-influencer-discovery/lockfile

Test it

Using "influencer-discovery". Find 15 sustainable fashion micro-influencers for an eco clothing launch.

Expected outcome:

Search criteria covering niche, platforms, follower range, engagement, location, and exclusions.
A screened candidate pool with evidence, red flags, and reasons for removal.
A three-tier shortlist with creator profiles, preliminary fit signals, and recommended next steps.

Using "influencer-discovery". Research five named YouTube creators for a productivity software campaign.

Expected outcome:

Profiles with displayed subscribers, recent video engagement, content themes, and sponsorship saturation.
Audience and brand-fit observations labeled by evidence quality.
Public business contact paths and unconfirmed fields clearly identified.

Using "influencer-discovery". Find competitor-partnered fitness creators and identify less saturated alternatives.

Expected outcome:

A comparison of visible competitor partnerships, saturation concerns, adjacent creator options, and a prioritized list for deeper scoring.

Security Audit

Medium Risk

v7 • 7/12/2026 Open versioned report

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.

Files scanned

600

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (2)

Medium

SKILL.md:68-70 references/creator-dossier.md:22-28

Untrusted Web Content Has No Prompt-Injection Boundary

The workflow tells the agent to search and read public profiles, posts, and websites without directing it to treat embedded instructions as untrusted data. A malicious creator page could influence the agent's research or output.

The cited instructions explicitly require consuming open-web content, and no content-isolation guidance appears in the reviewed files. Exploitation depends on encountering a malicious page.

Low

SKILL.md:34-35 references/creator-dossier.md:67-75

Persistent Storage of Creator Contact Data

The workflow collects public business contact paths and stores roster-worthy creator records in durable memory. It does not define data minimization, retention, deletion, or access controls for this contact data.

The files clearly specify contact discovery and durable record storage. The data is limited to public business channels, which reduces severity but does not remove privacy obligations.

Capability review items (2)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

High

SKILL.md:59

Ruby/shell backtick execution

**Measured YouTube metrics (free key)**: `python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.

Line 59 directs execution of a Python connector outside the scanned skill and substitutes a creator handle into a shell-style command. An untrusted handle or connector could cause command execution beyond the documented lookup.

High

SKILL.md:59

Generic API/secret keys

**Measured YouTube metrics (free key)**: `python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.

Line 59 requires YOUTUBE_API_KEY for a connector whose implementation was not included in the scanned files. The use is plausible, but secret handling and destination controls cannot be verified, so this remains fail-closed.

Risk Factors

🔑 Env variables (3)

references/creator-dossier.md:28 references/creator-dossier.md:74 SKILL.md:59

📁 Filesystem access (29)

⚙️ External commands (15)

SKILL.md:22-24 SKILL.md:24-26 SKILL.md:26-29 SKILL.md:29-33 SKILL.md:33 SKILL.md:34 SKILL.md:35-50 SKILL.md:50-52 SKILL.md:52-53 SKILL.md:53-54 SKILL.md:54-55 SKILL.md:55-57 SKILL.md:57 SKILL.md:59 SKILL.md:74

🌐 Network access (3)

SKILL.md:10 SKILL.md:13 SKILL.md:57

Audited by: codex View Audit History →

Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report

Copy report link

https://skillstore.io/skills/aaron-he-zhu-influencer-discovery/audits/7?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown badge

[![Skillstore security assessment](https://skillstore.io/badges/skills/aaron-he-zhu-influencer-discovery/security.svg)](https://skillstore.io/skills/aaron-he-zhu-influencer-discovery?utm_source=security_passport_badge)

HTML badge

<a href="https://skillstore.io/skills/aaron-he-zhu-influencer-discovery?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/aaron-he-zhu-influencer-discovery/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Embed card

<iframe src="https://skillstore.io/embed/skills/aaron-he-zhu-influencer-discovery.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Academic citations (APA · BibTeX · CFF)

APA citation

aaron-he-zhu. (2026). influencer-discovery security audit report (audit version 7) [Author version 17.0.0]. Skillstore. https://skillstore.io/skills/aaron-he-zhu-influencer-discovery/audits/7

BibTeX citation

@techreport{aaron-he-zhu-aaron-he-zhu-influencer-discovery-2026,
  author = {aaron-he-zhu},
  title = {influencer-discovery security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/aaron-he-zhu-influencer-discovery/audits/7},
  note = {Author version 17.0.0}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "influencer-discovery security audit report (audit version 7)"
version: "17.0.0"
type: report
authors:
  - name: "aaron-he-zhu"
date-released: "2026-07-12"
url: "https://skillstore.io/skills/aaron-he-zhu-influencer-discovery/audits/7"
identifiers:
  - type: other
    value: "skillstore:aaron-he-zhu-influencer-discovery:audit:7"
    description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: Medium

Architecture

100

Maintainability

Content

Community

Spec Compliance

What You Can Build

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.

Try These Prompts

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.

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.

Avoid

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.