What makes Fit Scorer notable?

1.1K downloads. Trust evidence available

How should Fit Scorer be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Fit Scorer

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... Skill: Fit Scorer Owner: aaron-he-zhu Summary: 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... Tags: latest:19.0.0 Version history: v19.0.0 | 2026-07-24T14:37:28.907Z | auto Fit Scorer 19.0.0 - Improved instructions for handling typed context: now strictly requires all STAR target fields; if missing, return

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

19.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 19.0.0release · observed Jul 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17e1tg8pjra8dn1dvtq21sahx83hrxj:fit-scorer

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-aaron-he-zhu-fit-scorer/snapshot"

Documentation

CLAWHUB

145,593 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
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: "19.0.0"
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": "19.0.0", "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 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](../../../protocol/creator-registry/SKILL.md) 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 authoriz

_meta.json

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  "ownerId": "kn73qjxwmbna25qq8q051epqt980sys5",
  "slug": "fit-scorer",
  "version": "19.0.0",
  "publishedAt": 1784903848907
}

references/scoring-templates.md

# Fit Scorer — Scoring Templates

Per-dimension scoring tables, the final-score rollup, the multi-influencer comparison report, the custom-weighting matrix, and a worked example. The numbered steps in [SKILL.md](../SKILL.md) reference these blocks. Fill in the bracketed cells per candidate.

These 1-5 tables are a campaign-specific prioritization aid, not the typed STAR Suitability rubric. The Suitability (S) read comes from `references/star-benchmark.md`; label every score from this file `commercial_fit_score`, and never feed it into the SQS or use it to clear a Suitability veto.

---

## Step 1 — Scoring Framework

```markdown
### Scoring Framework

**Brand/Campaign**: [name]
**Campaign Goal**: [awareness/consideration/conversion]
**Target Audience**: [description]

### Scoring Dimensions

| Dimension | Weight | Description |
|-----------|--------|-------------|
| Audience Match | [%] | How well their audience matches target |
| Content Quality | [%] | Production value and consistency |
| Brand Alignment | [%] | Values, aesthetic, messaging fit |
| Engagement Quality | [%] | Authenticity and depth of engagement |
| Partnership Potential | [%] | Professionalism, history, availability |
| **Total** | **100%** | |

**Scoring Scale**: 1-5 (1=Poor, 2=Below Average, 3=Average, 4=Good, 5=Excellent)
```

---

## Step 2 — Audience Match

```markdown
## Audience Match Score

**Influencer**: @[handle]

### Target vs. Actual Comparison

| Attribute | Target | Influencer's Audience | Match |
|-----------|--------|----------------------|-------|
| Age | [target] | [actual] | ✅/⚠️/❌ |
| Gender | [target] | [actual] | ✅/⚠️/❌ |
| Location | [target] | [actual] | ✅/⚠️/❌ |
| Interests | [target] | [actual] | ✅/⚠️/❌ |
| Income/Purchasing | [target] | [actual] | ✅/⚠️/❌ |

### Audience Quality Assessment

| Metric | Value | Assessment |
|--------|-------|------------|
| Real follower % | [%] | [Good/Concerning] |
| Active follower % | [%] | [Good/Concerning] |
| Bot/spam % | [%] | [Good/Concerning] |
| Audience growth | [trend] | [Organic/Suspicious] |

### Audience Match Score: [X/5]

**Justification**: [explanation]

**Weighted Score**: [X] × [weight%] = [weighted points]
```

---

## Step 3 — Content Quality

```markdown
## Content Quality Score

**Influencer**: @[handle]

### Production Quality

| Factor | Rating | Notes |
|--------|--------|-------|
| Visual quality | [1-5] | [notes] |
| Audio quality (if video) | [1-5] | [notes] |
| Editing skill | [1-5] | [notes] |
| Creativity | [1-5] | [notes] |
| Consistency | [1-5] | [notes] |

### Content Analysis

**Posting Frequency**: [X posts/week]
**Content Mix**: [types and %]
**Caption Quality**: [assessment]
**Hashtag Strategy**: [assessment]

### Best Content Examples

1. **[Content 1]**: [why it's good]
2. **[Content 2]**: [why it's good]

### Content Concerns

- [Concern 1 if any]
- [Concern 2 if any]

### Content Quality Score: [X/5]

**Justification**: [explanation]

**Weighted Score**: [X] × [weight%] = [we

skill-card.md

## Description:

Scores shortlisted creators on the typed STAR Suitability dimension and separately ranks campaign-specific commercial fit for influencer marketing decisions.

This skill is ready for commercial/non-commercial use.

## Publisher:

[aaron-he-zhu](https://clawhub.ai/user/aaron-he-zhu)

### License/Terms of Use:

MIT-0

## Use Case:

Marketing and influencer-operations teams use this skill to evaluate a known shortlist of creators, produce evidence-backed Suitability item states, and keep campaign-specific commercial-fit rankings separate from the STAR Suitability read.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Campaign context, creator metrics, local memory records, or YouTube measurements may include sensitive business or creator data.

Mitigation: Review approved data sources before use, provide only the campaign and creator evidence needed for scoring, and use YouTube API-based measurements only for approved shortlist vetting.

Risk: Incomplete or refused evidence can make a creator ranking appear more certain than the underlying data supports.

Mitigation: Require the typed STAR context fields, preserve Unknown states for missing evidence, and avoid definitive Suitability reads when applicable coverage is incomplete.

Risk: Persisted reports or promoted picks could expose draft evaluations or stale recommendations.

Mitigation: Save reports only after explicit authorization, request separate authorization before promotion, and include rerun conditions and evidence dates in recommendations.

## Reference(s):

- [Fit Scorer on ClawHub](https://clawhub.ai/aaron-he-zhu/skills/fit-scorer)
- [Publisher profile](https://clawhub.ai/user/aaron-he-zhu)
- [Source homepage](https://github.com/aaron-he-zhu/aaron-marketing-skills)
- [Scoring templates](references/scoring-templates.md)

## Skill Output:

**Output Type(s):** [text, markdown, guidance, shell commands]

**Output Format:** [Markdown reports with typed Suitability item states, evidence notes, comparison tables, recommendations, and optional shell commands for measured YouTube inputs.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May write reports only after explicit user authorization; may recommend separate promotion only after separate authorization.]

## Skill Version(s):

19.0.0 (source: server evidence and frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

distribution-manifest.json

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