Influencer Discovery

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-registry

  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: only with separate exact authorization, discovery results to

  memory/influencer/influencer-discovery/YYYY-MM-DD-<topic>.md

  — search criteria, candidate pool stats, per-influencer profiles, tiered shortlist with preliminary triage signals. Roster-worthy shortlisted creators (verified handles, contact path, audience stats) go as one-line updates to

  memory/events/creators.ndjson

  only via a separately authorized

  operation: propose

  request to

  registry-events.py

  — only

  creator-registry

  writes canonical records under

  memory/creators/

  .
• Promotes: only with separate exact authorization, durable facts (top-tier handles, confirmed niche/platform mix, competitor-saturated creators) to

  memory/hot-cache.md

  .
• Done when:
  • The required search criteria are present; otherwise stop with

    NEEDS_INPUT

    and name the missing criteria without fabricating candidates.
  • 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 discovery-triage signal that is not a STAR Suitability 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

~~

• ~~influencer database

  — bulk discovery, follower/engagement metrics, audience demographics.

• ~~social platform analytics

  — native creator-marketplace data, trending sounds, related accounts.

• ~~CRM

  — import the shortlist and dedupe against existing partners.

• ~~audience overlap

  — estimate creator-audience vs. brand-audience match.

https://www.youtube.com/oembed?url=<video-url>&format=json

https://www.tiktok.com/oembed?url=<post-url>

https://publish.twitter.com/oembed?url=<post-url>

~~influencer database

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" channel @handle

youtube.py videos @handle --limit 10

YOUTUBE_API_KEY

Instructions

1. Define search criteria. Capture brand, goal, audience definition, budget/follower tier, platforms, engagement floor, location/language, exclusions, and the required/preferred parameter table. If any required criterion is missing, stop with

   NEEDS_INPUT

   ; offer audience-mapper only when the user wants help defining the audience. Step 1 template.
2. Conduct the search. Work hashtags, similar-accounts, competitor mentions, and platform-native discovery; log any tool queries used. Step 2 template.
3. Initial screening. Filter the pool on follower range, engagement, recency, relevance, and brand safety; tally red flags (suspected fake followers, controversy, competitor exclusivity, inactivity). These are discovery signals, not verified STAR failures or vetoes; unsupported applicable evidence remains Unknown for downstream scoring. Per-platform reading cues: references/platform-vetting.md. Step 3 template.
4. Build influencer profiles. For each qualified creator, fill the profile (basics, metrics, audience, content, partnership history, contact, preliminary discovery-triage signal). Do not emit a STAR Suitability score from partial coverage. For a deep single-creator read with a contact waterfall, use references/creator-dossier.md. Step 4 template.
5. Compile the discovery report. Roll profiles into summary stats, by-platform and by-tier breakdowns, the three-tier shortlist, mix recommendation, and next steps. Step 5 template.
6. Add insights. Note niche content trends, the competitive picture, and recommendations for future searches. Step 6 template.

operation: propose

fit-scorer

Compact Example

Reference Materials

• references/templates.md — all step fill-in blocks (criteria, search, screening, profile, report, insights), the worked example, tips, and the "what/when" overview.
• references/platform-vetting.md — per-platform creator playbooks (X/LinkedIn/TikTok/YouTube/Reddit) feeding screening and profiling in steps 3-4.
• references/creator-dossier.md — structured per-creator dossier from public data, with a contact-discovery waterfall.
• skill-contract.md — shared contract and Handoff Summary format.
• state-model.md — memory tiers and save-path conventions.
• CONNECTORS.md — free/keyless data recipes and opt-in MCP layer.
• STAR benchmark at references/star-benchmark.md — scoring framework that fit-scorer applies downstream.
• Siblings in the scout phase: fit-scorer, audience-mapper, trend-spotter.

Next Best Skill

• competitor-tracker — when discovery surfaced competitor-saturated creators and you want to map the competitive field first.
• audience-mapper — when the target audience is still fuzzy and criteria need sharpening before a re-search.

Related Skills

• audience-mapper - Define who to reach
• fit-scorer - Score and rank discovered influencers
• competitor-tracker - Find competitor influencers
• outreach-manager - Contact discovered influencers