Loading
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 evidence profiles, authenticity re
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add aaron-he-zhu/aaron-marketing-skills --skill influencer-discovery -a cursor -y
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 evidence profiles, authenticity red-flag screening, and a Fit-readiness queue without action ranking. Not for STAR scoring or ranking a known shortlist — use fit-scorer. 达人挖掘/找达人/创作者名单
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 evidence profiles, authenticity red-flag screening, and a Fit-readiness queue without action ranking. 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 evidence-backed creator candidates across platforms, screen them against declared discovery filters, and build a non-ranked readiness queue for typed Fit evaluation.
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]
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).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/.memory/hot-cache.md; discovery readiness or queue position is not a durable ranking fact.NEEDS_INPUT and name the missing criteria without fabricating candidates.NEEDS_INPUT, not a vetted shortlist. A separately authorized partial checkpoint is labeled PARTIAL, lists every gap, and contains no tier or rank.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.creator_ref across the report and handoff; raw identity locators remain transient and are never copied into creator_ref.current, stale, or unknown against the current STAR evidence_window with any refresh_required fields named.NEEDS_REFRESH, NOT_RANKED, and NEEDS_INPUT until refreshed.Emit the standard shape from skill-contract.md §Handoff Summary Format.
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.
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.
NEEDS_INPUT; offer audience-mapper only when the user wants help defining the audience. Step 1 template.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.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.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.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.
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.
Primary: fit-scorer — score and rank the discovered candidates with weighted criteria before outreach.
Alternates (same influencer family):
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.