Influencer Discovery

Influencer Discovery

Find evidence-backed creator candidates across platforms, screen them against declared discovery filters, and build a non-ranked readiness queue for typed Fit evaluation.

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, decision-relevant geography/language, audience demographics, exclusions; dated candidate records from a user export, public source, roster, or live connector; the current campaign's STAR evidence_window when supplied; prior entity-registry brand profile and any audience-mapper output if present in memory; existing roster records under memory/creators/ (dedupe only through verified identity links against creators already rostered by creator-registry).
• Writes: return discovery results inline by default; only with separate exact authorization, save them to memory/influencer/influencer-discovery/YYYY-MM-DD-<topic>.md. A saved artifact uses a stable opaque creator_ref plus pseudonymous recipient_ref, contact_source_ref, and agency_ref, keeps raw handles, profile URLs, and contact coordinates transient-only, and retains geography only at the granularity required by the declared filter. Save an opaque handle_ref/source_ref identity resolver only when the authorized source artifact or verified creator-registry link can resolve it. Without one, keep identity_status: unresolved, save no hidden raw-locator mapping, and set cross_session_locator_required: true. Reuse a verified creator-registry aggregate ID when one exists; otherwise generate creator-<UUIDv4> once for the candidate lineage. Never set creator_ref to a raw handle, name, URL, email, provider ID, or a deterministic hash of any of them. Each roster-worthy creator update requires another exact authorization for an operation: propose request through registry-events.py to memory/events/creators.ndjson; only creator-registry writes canonical records under memory/creators/.
• Promotes: only with separate exact authorization, durable facts (verified creator/handle refs, confirmed niche/platform coverage, competitor-saturated creators) to memory/hot-cache.md; discovery readiness or queue position is not a durable ranking fact.
• Done when:
  • The required search criteria are present; otherwise stop with NEEDS_INPUT and name the missing criteria without fabricating candidates.
  • Exactly two raw locators without complete criteria/evidence remain NEEDS_INPUT, not a vetted shortlist. A separately authorized partial checkpoint is labeled PARTIAL, lists every gap, and contains no tier or rank.
  • A candidate pool exists with at least the requested count screened past follower, engagement, and brand-safety filters.
  • Each candidate has a field-level evidence trail (provider/tool, source_ref, observed_at, window, evidence label), an audience read, and an evidence-completeness triage state (READY_FOR_FIT | NEEDS_REFRESH | INELIGIBLE) that is neither a score nor a STAR Suitability verdict.
  • Every candidate keeps one stable opaque creator_ref across the report and handoff; raw identity locators remain transient and are never copied into creator_ref.
  • Conflicting observations remain separate, identity merges have a verified cross-link, and the Fit handoff marks each volatile field current, stale, or unknown against the current STAR evidence_window with any refresh_required fields named.
  • A non-ranked Fit-readiness queue is compiled with next-step pointers; every stale/unknown required field produces NEEDS_REFRESH, NOT_RANKED, and NEEDS_INPUT until refreshed.
• 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

Planning and screening need no live integration (Tier 1), but a real creator list still needs candidate records: public handles/links or an export supplied by the user, existing roster records, or a live search connector. Search criteria alone are not evidence that any specific creator or metric exists. If no candidate source is available, return a query/collection plan and NEEDS_INPUT; never invent handles, profiles, counts, or audience data.

Normalize evidence only in the report template, not through a new ingestion layer. For every factual field retain provider/tool, source_ref, observed_at, the measurement window (or not-supplied), and one label: Measured, Calculated, Estimated, User-provided, or Proxy. Keep conflicting values for the same field as parallel observations; do not average them, prefer the newest automatically, or merge identities from names/handles alone. A cross-provider identity becomes one creator only after a verified cross-link or explicit user confirmation.

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 — surface possible existing-partner matches for verified identity-link review; never auto-merge records.
• ~~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 resolve a candidate transiently and retain an opaque verified-handle evidence ref instead of hand-copying identity data. A handle ref remains separate from creator_ref: only an explicitly carried upstream creator_ref or a verified creator-registry identity link may resolve the aggregate; otherwise create a fresh random opaque ref and preserve the identity gap. 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 per-influencer score, STAR Suitability verdict, outreach priority, or action rank. It records evidence completeness and declared-filter results; fit-scorer owns typed comparison and ranking 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. Raw handles/profile URLs may appear only in Step 2's transient lookup block and must be removed before any save or handoff. Log the saved-safe batch with creator_ref, identity status, opaque handle_ref/source_ref when resolvable, provider/tool, query purpose, observed_at, window, and evidence label. If no public handles/links, user export, roster records, or live search connector can supply candidate records, produce the exact query pack and collection template, return NEEDS_INPUT, and stop before naming creators. 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, first reuse an explicitly carried opaque creator_ref or a creator-registry aggregate ID whose handle link is verified. If neither exists, generate one random creator-<UUIDv4> and reuse it unchanged throughout this report lineage. Never derive it from a handle or other identity data. Save an opaque handle/evidence ref only when an authorized artifact or verified registry link resolves it; otherwise keep identity_status: unresolved, create no hidden locator map, and require the raw locator again in a later session. Then fill the profile (pseudonymous identity refs, field-level metrics and audience evidence, content, partnership history, contact-path refs, and evidence-completeness triage state). Preserve conflicts as parallel rows and merge provider identities only after a verified cross-link. Compare each volatile observation with the current campaign's STAR evidence_window: within it is current; outside it is stale; a missing window/date or absent STAR window is unknown. A stale or unknown required field stays visible, becomes refresh_required, and forces triage_state: NEEDS_REFRESH, ranking_status: NOT_RANKED, and NEEDS_INPUT; never invent a global TTL. Do not emit a score, recommendation tier, or STAR Suitability verdict. 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, descriptive platform/follower-band breakdowns, and three non-ranked evidence queues: READY_FOR_FIT, NEEDS_REFRESH, and INELIGIBLE under the declared filters. Do not recommend a creator mix, label anyone Priority/Highly Recommended, or action-rank candidates before typed Fit. If the input is only two raw locators and criteria/evidence are incomplete, return NEEDS_INPUT and do not save a vetted pool. A partial checkpoint requires separate exact save authorization, must say PARTIAL/NOT_VETTED, list criteria/evidence gaps, and contain no rank, score, “top” label, or fit-scorer handoff. 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 through registry-events.py 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 fit-scorer the field-level evidence plus the STAR evidence_window, freshness_status, and refresh_required list; if no current STAR window exists, mark freshness unknown rather than inventing one. 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."

Illustrative output when a dated export or live connector returned candidate records: create one field-level evidence profile per opaque creator_ref, then place each row in READY_FOR_FIT, NEEDS_REFRESH, or INELIGIBLE under the declared filters. All rows remain NOT_RANKED; stale/unknown required fields are NEEDS_INPUT, and only the current complete rows hand off to fit-scorer. Without candidate records, return only the query/collection plan and NEEDS_INPUT. 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