fit-scorer

GitHub

用于对达人进行适配度评分与排名。基于STAR模型评估适合性,并结合商业契合度生成独立排名报告,辅助品牌选择合适的创作者进行合作。

influencer/scout/fit-scorer/SKILL.md aaron-he-zhu/aaron-marketing-skills

Trigger Scenarios

用户要求评分特定达人 用户要求为营销活动筛选或排名创作者

Install

npx skills add aaron-he-zhu/aaron-marketing-skills --skill fit-scorer -g -y
More Options

Non-standard path

npx skills add https://github.com/aaron-he-zhu/aaron-marketing-skills/tree/main/influencer/scout/fit-scorer -g -y

Use without installing

npx skills use aaron-he-zhu/aaron-marketing-skills@fit-scorer

指定 Agent (Claude Code)

npx skills add aaron-he-zhu/aaron-marketing-skills --skill fit-scorer -a claude-code -g -y

安装 repo 全部 skill

npx skills add aaron-he-zhu/aaron-marketing-skills --all -g -y

预览 repo 内 skill

npx skills add aaron-he-zhu/aaron-marketing-skills --list

SKILL.md

Frontmatter
{
    "name": "fit-scorer",
    "slug": "fit-scorer",
    "license": "Apache-2.0",
    "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",
    "version": "19.2.0",
    "homepage": "https:\/\/github.com\/aaron-he-zhu\/aaron-marketing-skills",
    "metadata": {
        "phase": "scout",
        "author": "aaron-he-zhu",
        "hermes": {
            "tags": [
                "marketing",
                "influencer",
                "scout"
            ],
            "category": "influencer"
        },
        "version": "19.2.0",
        "openclaw": {
            "emoji": "📣",
            "homepage": "https:\/\/github.com\/aaron-he-zhu\/aaron-marketing-skills"
        },
        "discipline": "influencer",
        "geo-relevance": "low"
    },
    "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",
    "displayName": "Fit Scorer · 红人适配评分",
    "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]",
    "compatibility": "Claude Code and compatible agent-skill hosts"
}

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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Version History

  • 8ebe52f Current 2026-08-20 02:01
  • bc7d62d 2026-07-25 08:08

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