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Fit Scorer

Free

Evaluate influencer suitability for your campaigns.

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Free · Opens the source repo

What Fit Scorer does

Fit Scorer is a specialized tool designed to assess and rank influencers based on their suitability for specific marketing campaigns. By leveraging the STAR Suitability (S) framework, it provides a detailed evaluation of each influencer's fit, considering factors such as audience composition, engagement authenticity, and credibility. This ensures that marketers can make informed decisions about which influencers align best with their brand values and campaign goals.

The skill operates by requiring the user to input relevant context, including the campaign goals, target audience, and a shortlist of influencer handles. It generates a Suitability score for each influencer, which is independent of any commercial fit considerations. This separation allows marketers to focus on the qualitative aspects of influencer suitability before delving into the commercial implications, which are handled separately in a prioritization matrix.

Fit Scorer is particularly useful for brands looking to optimize their influencer marketing strategies by providing a defensible ranking of influencers based on evidence-backed criteria. It is not intended for discovering new influencers or managing outreach, making it a focused tool for evaluating existing candidates. The skill also integrates with existing data sources, such as audience profiles and competitor benchmarks, to enhance the scoring process.

This tool is ideal for marketing teams and campaign managers who need to evaluate influencer partnerships rigorously and systematically. By using Fit Scorer, teams can ensure they are selecting influencers who not only resonate with their target audience but also uphold the brand's integrity and values throughout the campaign.

When to use it

Use this skill when you have a shortlist of influencers and need to assess their fit for a specific campaign.

When not to use it

Do not use this skill for discovering new influencers or for outreach management; those tasks require different tools.

What you can build with it

Evaluating Influencer Suitability

Use Fit Scorer to assess how well an influencer aligns with your brand's values and campaign goals.

Ranking Influencers for a Campaign

When you have multiple influencers in mind, Fit Scorer helps rank them based on their suitability for your specific campaign.

Separating Suitability from Commercial Fit

Utilize Fit Scorer to get a clear view of influencer suitability without mixing in commercial terms, ensuring informed decision-making.

How to install Fit Scorer

View source

1. Install with the skills CLI

npx skills add aaron-he-zhu/aaron-marketing-skills/fit-scorer --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by aaron-he-zhu

Fit Scorer

Score each shortlisted creator on the typed STAR Suitability (S) dimension, then keep campaign-specific commercial fit in a separate prioritization matrix. The Suitability read is portable and brand-independent; 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 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 the typed Suitability (S) read 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 Suitability (S) read and catalog version; never promote an unscored or provisional result.
  • Done when:
    • Every creator has all 10 Suitability items S1S10 explicitly Pass/Partial/Fail/Unknown/N/A with dated evidence or a gap reason.
    • 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.
  • 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 STAR Suitability rubric.

  1. Lock typed context. 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.
  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 Suitability only. Evaluate the Suitability items S1S10 (audience composition/realness, follower-growth integrity, reach reliability, engagement health and authenticity, credibility, and portable brand/category fit) from star-benchmark.md. Campaign-specific commercial terms and availability 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 the S1S10 states with source/date/type/confidence as the portable Suitability (S) read. The creator-content-auditor gate folds this read 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 Suitability read; never soften Unknown to Partial or hand-calculate a composite.
  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 a Suitability score, cannot clear a Suitability veto, and never enters the SQS.
  7. Rank transparently. Show the Suitability (S) read (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 S1S10 item states under the same campaign rollup_id; a Suitability (S) read exists only at complete applicable coverage, while the separate commercial matrix explains campaign-specific terms and availability. A verified below-benchmark real-follower rate marks STAR-S2 Fail and holds outreach; refused access stays Unknown and prevents the read. Only creator-content-auditor may apply the later STAR business verdict/cap. Persistence is offered, not assumed.

Reference Materials

Next Best Skill

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

Alternates (same scout phase):

  • creator-content-auditor — when a complete Suitability read or potential STAR-S2/STAR-S6/STAR-T3 control evidence is ready, stop and hand it to this sole STAR gate as a separate invocation; do not auto-run or simulate its verdict.
  • 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.

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