| name | fit-scorer |
| slug | fit-scorer |
| displayName | Fit Scorer · 红人适配评分 |
| summary | 用 typed STAR 适配度(S) 维度评估创作者，并将活动商业适配度作为独立矩阵排序 |
| description | Use when the user asks to "score this influencer", "rank these creators for our campaign", or "tell me which influencer is the best fit"; produces the typed STAR Suitability (S) read plus a separately labeled campaign-fit ranking without mixing campaign-specific commercial fit into the Suitability read. Not for finding new influencers — use influencer-discovery; not for sending outreach — use outreach-manager. 达人适配度评分/创作者筛选排名 |
| version | 20.1.0 |
| license | Apache-2.0 |
| compatibility | Claude Code and compatible agent-skill hosts |
| homepage | https://github.com/aaron-he-zhu/aaron-marketing-skills |
| when_to_use | Use when a user has a shortlist of influencers and needs an objective, weighted score to prioritize outreach, choose between candidates, justify a selection to stakeholders, set consistent evaluation standards, compare creators across niches or platforms, or build long-term partner tiers. Activates on requests like score @handle for our brand, compare and rank these creators, or which of these is the best fit. |
| argument-hint | <brand or campaign> <influencer handle(s)> [campaign goal: awareness|engagement|conversion] |
| metadata | {"author":"aaron-he-zhu","version":"20.1.0","discipline":"influencer","phase":"scout","geo-relevance":"low","hermes":{"tags":["marketing","influencer","scout"],"category":"influencer"},"openclaw":{"emoji":"📣","homepage":"https://github.com/aaron-he-zhu/aaron-marketing-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.
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