Fit Scorer

Fit Scorer

Objectively evaluate how well an influencer matches your brand by scoring them across five weighted dimensions, turning gut feel into a defensible go/pass decision.

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: a fit-score report (per-dimension raw scores, weighted totals, verdict, ranked comparison) to memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.md.
• Promotes: top-ranked handles, final scores, and the go/pass verdict to memory/hot-cache.md so downstream skills pick the right targets.
• Done when:
  • Every shortlisted influencer has a weighted total score on the 1-5 scale with per-dimension justifications.
  • A ranked comparison and an explicit verdict (Highly Recommended / Recommended / Consider / Pass) exist for each candidate.
  • The report is saved to the family memory path and top picks are promoted to the hot cache.
• 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.

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

All fill-in tables and the comparison/report layouts live in references/scoring-templates.md — copy the matching block for each step.

1. Define the scoring framework. Set the five dimensions, weights (default below; tune per goal via the custom-weighting matrix), and the 1-5 scale. Use the Step 1 template.

   C³ ACE alignment & veto gate. This skill is the C³ Creator scorer (ACE). Map dimensions onto ACE: Audience Match → Audience; Engagement Quality → Engagement; the Brand Safety sub-check → Credibility (C1). Note: the value/aesthetic/messaging-fit part of Brand Alignment is creator × brand fit, which C³ scores in ROI.Orchestration (O1), not ACE — ACE is brand-independent, so keep brand-fit out of Credibility. Before ranking, screen every creator against the three ACE veto items; any failure is disqualifying → verdict PASS (do not partner) AND cap the Final Rating at the Poor / Below-Average band (≤ 2.9 / 5, i.e. ACE ≤ 59/100) so the score never contradicts the decline. State the veto ID + evidence:

   Veto	Item	Fail condition
   A2	Real-Follower Rate	< 70% real followers, or audit refused (follower fraud)
   C1	Brand Safety	disqualifying content / active scandal
   E2	Engagement Authenticity	pod / bought engagement

2. Score Audience Match — target-vs-actual demographics plus audience quality (real/active/bot %). Step 2 template.

3. Score Content Quality — production value, cadence, content mix, best examples, concerns. Step 3 template.

4. Score Brand Alignment — value/aesthetic/messaging fit and the Brand Safety check (feeds ACE C1). Step 4 template.

5. Score Engagement Quality — engagement rate vs industry avg, authenticity indicators, pod/buying signs (feeds ACE E2). Step 5 template.

6. Score Partnership Potential — partnership history, professionalism, exclusivity/availability, estimated value; pull prior-partnership and response-history facts from the memory/creators/ roster record when one exists. Step 6 template.

7. Calculate the final score — roll raw × weight into the weighted total, apply the interpretation band, write the verdict and expected performance. Step 7 template.

8. For multiple influencers, produce the ranking summary, dimension-by-dimension comparison, and prioritize/combine/pass recommendation. Step 8 template.

Save the report to memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.md and promote top picks + verdict to memory/hot-cache.md.

Compact Example

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

Output: Each scored across the five dimensions with conversion weighting (Audience 35%, Brand 20%). @sustainablesarah ranks #1 (4.4/5) on highest audience match and authentic engagement; @greenwardrobe flagged DONE_WITH_CONCERNS on a borderline real-follower rate (A2 watch); ranked comparison + go/pass verdicts saved, top pick promoted to hot cache.

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