Influencer Discovery

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.

Where a tool could sharpen results, use ~~ connector placeholders:

• ~~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.

Keyless candidate-card metadata (oEmbed): YouTube (https://www.youtube.com/oembed?url=<video-url>&format=json), TikTok (https://www.tiktok.com/oembed?url=<post-url>), and X (https://publish.twitter.com/oembed?url=<post-url>) return a post's title, author name/handle, and thumbnail with no key — enough to auto-fill a candidate's profile row from pasted links instead of hand-copying. Metadata only: no follower or engagement metrics, so those stay ~~influencer database or manual export — except YouTube, below.

Measured YouTube metrics (free key): python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" channel @handle returns the real displayed subscriber count, total views, and video count, and youtube.py videos @handle --limit 10 adds per-video views/likes/comments — upgrading a YouTube candidate's profile row from Estimated to Measured. Free YOUTUBE_API_KEY (10,000 units/day; one channel check ≈ 1–3 units). ToS boundary: vet a named shortlist, don't build a bulk creator database — quota extensions are refused for competitive harvesting. See scripts/connectors/README.md.

See CONNECTORS.md for the free/keyless recipe per category and the opt-in MCP layer. None are required — every step degrades to user-supplied inputs.

Instructions

Each step has a fill-in block in references/templates.md — copy the matching block. This skill does not compute final fit scores; the per-influencer score in step 4 is a triage signal that fit-scorer refines downstream.

1. Define search criteria. Capture brand, goal, budget tier, and the required/preferred parameter table plus nice-to-haves and exclusions. 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 (fake followers, controversy, competitor exclusivity, inactivity). 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 fit score). 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.

Save the report to memory/influencer/influencer-discovery/YYYY-MM-DD-<topic>.md and, with permission, cache the active shortlist in working memory. Submit each roster-worthy creator as an authorized operation: propose request through registry-events.py to memory/events/creators.ndjson, then recommend creator-registry for resolution; proposal count never changes authority.