Agent skill · marketing growth · aiskillstore
influencer-discovery
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.
Why this skill is useful
Provides domain-specific knowledge for influencer discovery, including audience metrics and authenticity screening that the AI wouldn't reliably generate on its own.
What it needs
About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/influencer-discovery