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, authenticity red-flag screening, and a tiered shortlist with preliminary triage signals. Not for STAR scoring or ranking a known shortlist — use fit-scorer. 达人挖掘/找达人/创作者名单

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

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Find 20 influencers in [niche] for [brand/product]

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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

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 a STAR Suitability score; any per-influencer score in step 4 is only a discovery-triage signal that fit-scorer replaces with a typed evidence read downstream.

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.

Return the discovery report inline. Saving the report, caching the shortlist, and submitting each roster-worthy creator as operation: propose are three separate operations and each requires exact authorization; without it, offer the eligible path and write nothing. After a vetted shortlist exists, hand it with dated evidence to fit-scorer. fit-scorer records the S1-S10 evidence read; creator-content-auditor alone determines verified STAR vetoes and renders the gate verdict.

Compact Example

User: "Find 15 micro-influencers (10K-100K followers) in sustainable fashion for a new eco clothing brand."

Output: 43 candidates surfaced, 15 pass the declared discovery filters with preliminary triage signals above 18/25. Top candidate @sustainablestyle_sarah (47K IG + 23K TikTok, 5.2% ER, prior eco-brand partners) has a 24/25 discovery signal; shortlist tiered into 5 high-engagement leads, 7 mid-tier, 3 rising stars. The report is returned inline, then save, promotion, and registry-proposal permissions are offered separately. Full walkthrough in references/templates.md.

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

Primary: fit-scorer — score and rank the discovered candidates with weighted criteria before outreach.

Alternates (same influencer family):

• 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.

Termination: Maintain a visited-set. If a skill has already been invoked this session, stop and report chain-complete rather than re-invoking it. Max chain depth is 3 hops from the originating request; stop and summarize when reached.

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