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Skills / Influencer / fit-scorer

fit-scorer

Weighted fit score — produces STAR Suitability (S).

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Discipline
Influencer
Framework
STAR
Gate
creator-content-auditor
Entrypoint
/aaron-marketing:influencer

From SKILL.md

Inlined from influencer/scout/fit-scorer/SKILL.md (primary sections; appendix omitted) — view full SKILL.md on GitHub

Sections: Fit Scorer · Quick Start · Skill Contract · Data Sources · Instructions · Related Skills

Fit Scorer

Score each shortlisted creator on the typed STAR Suitability (S) dimension, then keep deal-specific commercial fit in a separate prioritization matrix. Suitability includes the STAR-S8 brand/category and audience-brand evidence that is independent of any single deal; deal terms, availability, and campaign orchestration stay outside it. The commercial matrix is not a Suitability score and never enters the SQS.

Quick Start

Score one influencer:

Score @[handle] for [brand/campaign] and tell me if they're a good fit

Compare and rank a shortlist:

Compare and rank these influencers for [campaign]: @influencer1, @influencer2, @influencer3

Skill Contract

  • Reads: brand/campaign context, target audience definition, campaign goal, and shortlist entries carrying a stable opaque creator_ref plus either transient handles/profile URLs or resolvable opaque handle refs (supplied by the user or carried over from influencer-discovery). Optional prior audience profiles from memory/influencer/audience-mapper/, competitor partner benchmarks from memory/influencer/competitor-tracker/, and a WARM Campaign Retro Card's evidence_refs plus next_campaign_hypothesis when the user supplies or authorizes that handoff. For rostered creators, read partnership history and audience-stat provenance from memory/creators/<aggregate-id>.md — the creator-registry roster record — as Partnership Potential inputs.
  • Writes: return the typed Suitability (S) read and separately labeled commercial-fit comparison inline by default; when a Retro Card is supplied, preserve its hypothesis as a separately labeled next-cycle test constraint with no score or verdict effect. Save the report to memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.md only with exact WARM-save authorization. Saved reports and handoffs retain the stable opaque creator_ref and opaque evidence refs, never a raw handle, name, profile URL, email, provider ID, or deterministic hash in creator_ref.
  • Promotes: only with separate exact authorization, promote evidence-backed top picks and their exact Suitability (S) read and catalog version to memory/hot-cache.md; never promote an unscored/provisional result or the Retro Card's qualitative decision/hypothesis as scored truth.
  • Done when:

- Every creator has all 10 Suitability items S1S10 explicitly Pass/Partial/Fail/Unknown/N/A with dated evidence or a gap reason. - Every creator's stable opaque creator_ref is preserved from discovery/registry or generated once for this lineage; raw identity locators remain transient. - The typed goal/context and the Suitability item states are preserved for the gate; Unknown prevents a Suitability read. - Any commercial-fit ranking is visibly separate from the Suitability read and cannot override a veto or missing evidence. - If a Retro Card is supplied, its next_campaign_hypothesis is visible only as a falsifiable test constraint/commercial-matrix context; its evidence_refs are pointers for fresh investigation, not STAR item evidence or an automatic selection rule.

  • Primary next skill: campaign-planner — turn the ranked shortlist into an approved campaign plan. If that plan is already approved and outreach-ready, hand off to outreach-manager instead; competitor benchmarking is optional.

Handoff Summary

Emit the standard shape from skill-contract.md §Handoff Summary Format.

Data Sources

This family needs no live integrations (Tier 1). Fit Scorer works end to end by asking the user for the inputs it scores — transient handles or profile locators, audience targets, brand values, and any metrics they have. A connector sharpens the numbers but none is required.

  • ~~influencer database — follower counts, audience demographics, and partnership history.
  • ~~social platform analytics — engagement rate, comment quality samples, posting cadence, growth trend.
  • ~~audience intelligence — real-vs-bot follower estimates and audience overlap with your target.
  • Roster record (keyless Tier 1) — prior contact, response reputation, and delivery history come from memory/creators/<aggregate-id>.md when the creator is rostered (creator-registry curates it); ~~CRM is an optional Tier-2 sharpener for the same history when no roster record exists.

Measured YouTube inputs (free key): for YouTube candidates, python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" videos @handle --limit 10 supplies the engagement-authenticity inputs directly — per-video views/likes/comments against the displayed subscriber base (views-to-subs consistency, comment rate, cadence) — so those sub-scores come from Measured numbers instead of screenshots. Free YOUTUBE_API_KEY; shortlist vetting only (ToS refuses bulk-harvesting quota). See scripts/connectors/README.md.

With zero integrations, ask the user to supply each value the scoring tables request; the framework and weighting still produce a defensible ranking. See CONNECTORS.md for the free/keyless recipe per category.

Instructions

The contract-compatible copied layouts live in references/scoring-templates.md: use the creator_ref-only typed STAR-S1STAR-S10 evidence table for the Suitability read, then the optional commercial_fit_score tables for separate decision support. Never copy a raw locator into those outputs.

  1. Lock identity and typed context. Reuse the opaque creator_ref explicitly carried by discovery, or a creator-registry aggregate ID only when its handle link is verified. If the user supplies only a raw handle/profile URL and no verified aggregate exists, generate one random creator-<UUIDv4> and reuse it unchanged across this report, any authorized save, and downstream handoffs. Never set creator_ref to a raw handle, name, URL, email, provider ID, or deterministic hash of one; keep those locators transient for evidence acquisition. Resolve an opaque ref only through its accompanying authorized artifact or verified registry link. If neither is available, request the transient locator and preserve the identity as unresolved rather than guessing or merging. Then require the creator target and target version, named STAR profile/goal (awareness|engagement|conversion|brand-building), assessment_time: forecast|actual, shared campaign rollup_id, observation date, platform/tier/niche cohort, evidence window, material context object, and current STAR catalog_version — the exact typed identity the gate will reuse. If any field is absent, do not invent it: return NEEDS_INPUT, name the missing fields, and preserve the supplied identity unchanged for resume. When a Campaign Retro Card is supplied, record its evidence_refs and next_campaign_hypothesis in a separate non-scoring prior-cycle context block; neither becomes part of the STAR typed identity.
  2. Freeze evidence for the current window. Use current creator analytics, public observations, roster history, and cohort benchmarks with source/date/type/confidence. A Retro Card and its evidence_refs are discovery pointers only, never STAR item evidence. If a referenced primary source is independently reacquired and qualifies in the current evidence window, cite that fresh observation rather than the card. Missing or refused private access is Unknown, never Fail or Partial.
  3. Score Suitability only. Evaluate the Suitability items S1S10 (audience composition/realness, follower-growth integrity, reach reliability, engagement health and authenticity, credibility, and deal-independent brand/category fit) from star-benchmark.md. Deal-specific commercial terms, availability, and orchestration conflicts stay in the separate matrix; cost and measured campaign conversion belong to Return (R), scored later by the gate.
  4. Qualify critical-control evidence for handoff. STAR-S2 covers demonstrated follower fraud / real-follower rate below the matching tier × platform × niche benchmark; STAR-S6 covers demonstrated bought, coordinated, or pod-based engagement. Brand safety is the gate's Trust control STAR-T3, not a Suitability item. Mark an item Fail only from qualifying evidence, label it a potential gate finding, and operationally hold outreach while it stands. Do not call it a verified veto or apply the SQS cap/business verdict here; the auditor owns those decisions when it rolls up the full STAR run.
  5. Record the Suitability read for the gate. Capture every S1S10 state as exactly Pass/Partial/Fail/Unknown/N/A with source/date/window/type/confidence or an explicit gap/N/A reason under the locked brand/category/cohort context. This typed table—not any 1–5 aid—is the Suitability read. The creator-content-auditor gate folds it into the full STAR run and runs the deterministic scorer for the profile-weighted SQS; this skill does not run the scorer or emit the SQS. Unknown means applicable evidence is missing and prevents a complete Suitability read; never soften Unknown to Partial or hand-calculate a composite.
  6. Build the separate commercial matrix when requested. Use deal-specific audience/goal nuance, content concept, brand conflicts, commercial terms, availability, and partnership potential. A supplied next_campaign_hypothesis may appear beside the matrix as a falsifiable test constraint, but contributes zero points and no weight. Label every 1–5 component and its rollup commercial_fit_score; never call one “Fit Score” or “Final Score.” It is not a Suitability score, cannot clear a Suitability control finding, and never enters the SQS.
  7. Rank transparently. Show the typed Suitability read, critical controls, commercial_fit_score separately, evidence confidence, and an action tied to the declared rule with owner/rerun condition. Do not emit a generic Verdict or star rating. Route to campaign-planner by default; route to outreach-manager only when an approved campaign plan is already ready for execution. Offer competitor benchmarking as an optional check, not a mandatory detour. Do not rank an Unknown-heavy candidate as definitively superior, add/subtract points because a Retro hypothesis names a creator or tactic, or automatically select a candidate from a prior-cycle renew | retest | retire | unknown decision.
  8. Persist only with permission. Save the report only after exact WARM authorization; request separate authorization before any hot-cache promotion. Persist and hand off creator_ref plus opaque handle/evidence refs, not the transient raw identity locator. The Retro hypothesis remains WARM working context. This skill does not propose provisional commercial rankings or non-gate Suitability results to creator-registry.

Related Skills

Full source on GitHub (appendix / reference sections omitted for length).

FAQ

What does this skill do?
Weighted fit score — produces STAR Suitability (S).
Where is the authoritative source?
SKILL.md in the aaron-marketing-skills repo — https://github.com/aaron-he-zhu/aaron-marketing-skills/blob/main/influencer/scout/fit-scorer/SKILL.md
How do I install just this skill?
npx skills add aaron-he-zhu/aaron-marketing-skills -s fit-scorer.