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
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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
Activate when building an influencer roster from scratch, expanding into a new platform or niche, replacing churned partners, finding micro and nano creators at scale, identifying which influencers a competitor partners with, or standing up an always-on discovery pipeline. The user names a niche, platform, follower band, or brand and wants a list of candidate creators to evaluate.
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.
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:
~~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.
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.
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.
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.
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.
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.
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.
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.
