name: fit-scorer
slug: fit-scorer
displayName: "Fit Scorer · 红人适配评分"
summary: "用 typed C3 ACE 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 typed C3 ACE creator results plus a separately labeled campaign-fit ranking without mixing brand fit into ACE. Not for finding new influencers — use influencer-discovery; not for sending outreach — use outreach-manager.'
version: "17.0.0"
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": "17.0.0", "discipline": "influencer", "phase": "discover", "family": "influencer-marketing", "hermes": {"tags": ["marketing", "influencer", "discover"], "category": "influencer"}, "openclaw": {"emoji": "📣", "homepage": "https://github.com/aaron-he-zhu/aaron-marketing-skills"}}

Fit Scorer

Score each shortlisted creator on the typed C3 ACE creator rubric, then keep campaign-specific commercial fit in a separate prioritization matrix. The ACE result is portable and brand-independent; the commercial matrix is not an ACE score and never enters CVI.

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 a shortlist of influencer handles (supplied by the user or carried over from influencer-discovery). Optional prior audience profiles from memory/influencer/audience-mapper/ and competitor partner benchmarks from memory/influencer/competitor-tracker/. For rostered creators, read partnership history and audience-stat provenance from memory/creators/<handle-slug>.md — the creator-registry roster record — as Partnership Potential inputs.
• Writes: only with explicit authorization, a report containing typed ACE results plus a separately labeled commercial-fit comparison at memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.md.
• Promotes: only with separate authorization, evidence-backed top picks and their exact ACE profile/version; never promote an unscored or provisional result.
• Done when:
  • Every creator has all 12 ACE items explicitly Pass/Partial/Fail/Unknown/N/A with dated evidence or a gap reason.
  • The exact ace-<goal> profile/context and deterministic scorer result are preserved; Unknown prevents an ACE total.
  • Any commercial-fit ranking is visibly separate from ACE and cannot override a veto or missing evidence.
• Primary next skill: competitor-tracker — benchmark your top-scored picks against the creators competitors already partner with.

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 — handles, 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/<handle-slug>.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 commercial comparison layouts live in references/scoring-templates.md. They are optional decision support, not the C3 rubric.

1. Lock typed context. Declare creator target/version, goal (awareness|engagement|conversion|brand-building), profile ace-<goal>, scope: ace, assessment_time: forecast|actual, shared campaign rollup_id, observation date, platform/tier/niche cohort, and evidence window. Profile scope/goal must match context.
2. Freeze evidence. Use creator analytics, public observations, roster history, and cohort benchmarks with source/date/type/confidence. Missing or refused private access is Unknown, never Fail or Partial.
3. Score ACE only. Evaluate A1-A4 Audience, C1-C4 Credibility, and E1-E4 Engagement from ace-creator-benchmark.md. Creator-brand fit, exclusivity conflict, cost, and campaign conversion belong to ROI.O/I, not ACE.
4. Verify critical failures. C3-ACE.A2 fails only on verified real-follower rate below 70%; C3-ACE.C1 on verified disqualifying conduct; C3-ACE.E2 on verified bought/pod engagement. One verified veto yields DONE_WITH_CONCERNS/FIX and final=min(raw,59); two or more yield DONE/BLOCK with no final score. Operationally hold outreach while a critical issue remains, but do not relabel the typed verdict.
5. Run the deterministic scorer. Follow runtime-invocation.md, resolve AARON_SKILLS_ROOT="${CLAUDE_PLUGIN_ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || true)}", verify the scorer and typed catalog, then execute python3 "$AARON_SKILLS_ROOT/scripts/rubric-score.py" score <run.json>. If the standalone install lacks them, return score_state: NOT_SCORED / score_confidence: not_scored; do not hand-calculate a total, verdict, or persistent artifact.
6. Build the separate commercial matrix when requested. Use audience-to-campaign fit, content style, campaign-specific brand/category fit, commercial terms, availability, and partnership potential. Label its 1-5 total commercial_fit_score; it is not ACE, cannot clear an ACE veto, and never enters CVI.
7. Rank transparently. Show ACE profile/result (or coverage/interval), critical controls, commercial fit separately, evidence confidence, and an outreach recommendation with owner/rerun condition. Do not rank an Unknown-heavy candidate as definitively superior.
8. Persist only with permission. Save the report only after authorization; request separate authorization before any hot-cache promotion or creator-registry proposal.

Compact Example

User: "Compare @ecofashionista, @greenwardrobe, @sustainablesarah for our sustainable fashion brand (goal: conversion)."

Output: Each creator receives a typed ace-conversion result using the same campaign rollup_id; the separate commercial matrix explains brand/category fit and terms. A verified 55% real-follower result fails A2 and caps one-veto ACE at 59, while refused access stays Unknown and prevents a total. Persistence is offered, not assumed.

Reference Materials

• references/scoring-templates.md — all per-dimension tables, final-score rollup, comparison report, custom-weighting matrix, worked example, and tips.
• skill-contract.md — shared contract and handoff summary format.
• state-model.md — memory tiers and save-path conventions.
• CONNECTORS.md — free/keyless data recipe per connector category.
• Scoring rubric: c3-benchmark.md (CVI rollup), c3/ace-creator-benchmark.md (the ACE Creator rubric this skill emits, incl. A2/C1/E2 veto items), c3/scoring-architecture.md (weighting and cap methodology).
• Sibling skills: influencer-discovery, competitor-tracker, audience-mapper, outreach-manager.

Next Best Skill

Primary: competitor-tracker — benchmark your top-scored picks against the creators competitors already work with before you commit budget.

Alternates (same discover phase):

• influencer-discovery — if the shortlist is too thin to rank, source more candidates.
• audience-mapper — if audience-match scores are uncertain, tighten the target-audience definition first.

Termination note: Track a visited-set of skills invoked this session. If the recommended next skill has already run, stop and report the chain complete rather than re-invoking it. Stop after at most 3 hops (max-depth 3) and hand back to the user with the saved report path.

Related Skills

• influencer-discovery - Find influencers to score
• competitor-tracker - Benchmark against competitor partners
• audience-mapper - Define target audience
• outreach-manager - Contact top-scored influencers