influencer-discovery Checked

DS_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 (../fit-scorer/SKILL.md) — score and rank the discovered candidates with weighted criteria.

### Handoff Summary

> Emit the standard shape from skill-contract.md §Handoff Summary Format (../../../references/skill-contract.md).

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 (../../../scripts/connectors/README.md).

See CONNECTORS.md (../../../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 (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 (../fit-scorer/SKILL.md) 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