| name | influencer-discovery |
| slug | influencer-discovery |
| displayName | Influencer Discovery · 红人发现 |
| summary | 多平台红人挖掘:候选池、画像与互动指标、真实性红旗筛查、分层短名单 |
| description | Use when the user asks to "find influencers", "build an influencer list", or "discover creators in [niche]"; produces a multi-platform candidate pool, per-influencer profiles with audience and engagement metrics, authenticity red-flag screening, and a tiered shortlist with fit scores. Not for scoring or ranking a known shortlist — use fit-scorer. |
| version | 17.0.0 |
| license | Apache-2.0 |
| compatibility | Claude Code and compatible agent-skill hosts |
| homepage | https://github.com/aaron-he-zhu/aaron-marketing-skills |
| when_to_use | Activate when building an influencer roster from scratch, expanding into a new platform or niche, replacing churned partners, finding micro and nano creators at scale, identifying which influencers a competitor partners with, or standing up an always-on discovery pipeline. The user names a niche, platform, follower band, or brand and wants a list of candidate creators to evaluate. |
| argument-hint | <brand or niche> [platform] [follower-range] |
| metadata | {"author":"aaron-he-zhu","version":"17.0.0","discipline":"influencer","phase":"discover","family":"influencer-marketing","hermes":{"tags":["marketing","influencer","discover"],"category":"influencer"},"openclaw":{"emoji":"📣","homepage":"https://github.com/aaron-he-zhu/aaron-marketing-skills"}} |
Influencer Discovery
Find the right influencers for your brand by searching across platforms, screening for audience fit and authenticity, and building a tiered candidate list ready for scoring.
Quick Start
Find 20 influencers in [niche] for [brand/product]
Find influencers in [niche] with 50K-200K followers on TikTok and Instagram,
based in [location], engagement above 4%, who have worked with brands like [brand]
Skill Contract
- Reads: brand/product, niche or category, target platforms, follower range, engagement floor, location/language, audience demographics, exclusions; prior
entity-optimizer brand profile and any audience-mapper output if present in memory; existing roster records under memory/creators/ (dedupe the candidate pool against creators already rostered by creator-registry).
- Writes: discovery results to
memory/influencer/influencer-discovery/YYYY-MM-DD-<topic>.md — search criteria, candidate pool stats, per-influencer profiles, tiered shortlist with fit scores. Roster-worthy shortlisted creators (verified handles, contact path, audience stats) go as one-line updates to memory/events/creators.ndjson via an authorized operation: propose request to registry-events.py — only creator-registry writes canonical records under memory/creators/.
- Promotes: durable facts (top-tier handles, confirmed niche/platform mix, competitor-saturated creators) to
memory/hot-cache.md.
- Done when:
- A candidate pool exists with at least the requested count screened past follower, engagement, and brand-safety filters.
- Each shortlisted influencer has a profile with metrics, audience read, and a preliminary fit score.
- A tiered shortlist (must-reach / strong / consider) is compiled with next-step pointers.
- Primary next skill: fit-scorer — score and rank the discovered candidates with weighted criteria.
Handoff Summary
Emit the standard shape from skill-contract.md §Handoff Summary Format.
Data Sources
This family has no live integrations required (Tier 1): the skill works with only the inputs the user provides. Ask the user for niche, platforms, follower band, engagement floor, location, and exclusions, then reason over what they supply plus any public handles they share.
