A skill your agent uses 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)…

Apache-2.0Auto-check passedMarketing & SEO

Install Fit Scorer

skills CLI
$ npx skills add aaron-he-zhu/aaron-marketing-skills --skill fit-scorer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aaron-he-zhu/aaron-marketing-skills fit-scorer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/influencer/scout/fit-scorer .claude/skills/fit-scorer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
fit-scorer
GitHub stars
2.9k
Token cost
~4.2k tokens
SKILL.md length
1,760 words
Files
2 (incl. references)
Skills in repo
119
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses 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)…

  • Works in 8 steps: Lock identity and typed context. Reuse… → Freeze evidence for the current window.… → Score Suitability only. Evaluate the… → …
  • The user asks to score this influencer
  • SKILL.md covers Quick Start, Skill Contract, Data Sources and Instructions, plus 4 more sections
  • Calls python3; needs YOUTUBE_API_KEY

What it does

Fit Scorer is an agent skill from aaron-he-zhu/aaron-marketing-skills. 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

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/scoring-templates.md`). Compatibility notes: Claude Code and compatible agent-skill hosts

It sits in Marketing & SEO, covering Influencer and creator marketing. The repository describes itself as: 120 marketing skills as an AI marketing staff — plugin, portable skills, or an 8-bot team across 7 disciplines (narrative, SEO/GEO, social, email, paid, influencer, launch) on… The licence is Apache-2.0.

When your agent uses it

  • The user asks to score this influencer
  • Rank these creators for our campaign
  • Tell me which influencer is the best fit

Example prompts

  • “score this influencer”
  • “rank these creators for our campaign”
  • “tell me which influencer is the best fit”
  • “/fit-scorer”

Requirements

  • Python 3
  • A credential in YOUTUBE_API_KEY
  • Compatibility (from SKILL.md): Claude Code and compatible agent-skill hosts

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Lock identity and typed context. Reuse the opaque creator_ref explicitly carried by discovery, or a creator-registry aggregate ID only…
  2. Freeze evidence for the current window. Use current creator analytics, public observations, roster history, and cohort benchmarks with…
  3. Score Suitability only. Evaluate the Suitability items S1–S10 (audience composition/realness, follower-growth integrity, reach…
  4. Qualify critical-control evidence for handoff. STAR-S2 covers demonstrated follower fraud / real-follower rate below the matching tier ×…
  5. Record the Suitability read for the gate. Capture every S1–S10 state as exactly Pass/Partial/Fail/Unknown/N/A with…
  6. Build the separate commercial matrix when requested. Use deal-specific audience/goal nuance, content concept, brand conflicts, commercial…
  7. Rank transparently. Show the typed Suitability read, critical controls, commercial_fit_score separately, evidence confidence, and an…
  8. Persist only with permission. Save the report only after exact WARM authorization; request separate authorization before any hot-cache…

What it can do on your machine

Read from SKILL.md and the folder at commit d5529cb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • YOUTUBE_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code and compatible agent-skill hosts

    From compatibility in the SKILL.md frontmatter.

Context cost

Fit Scorer loads about 4.2k tokens when it runs, and up to ~9.1k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 1,760 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~110
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aaron-he-zhu/aaron-marketing-skills at commit d5529cb, republished under its Apache-2.0 licence (© aaron-he-zhu). 1,760 words, ~4,162 tokens.

Download SKILL.mdSave it as .claude/skills/fit-scorer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
fit-scorer
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
compatibility
Claude Code and compatible agent-skill hosts
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
version
20.1.0
license
Apache-2.0
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…
argument-hint
<brand or campaign> <influencer handle(s)> [campaign goal: awareness|engagement|conversion]
metadata.author
aaron-he-zhu
metadata.version
20.1.0

Fit Scorer

Score each shortlisted creator on the typed STAR Suitability (S) dimension, then keep deal-specific commercial fit in a separate prioritization matrix. Suitability includes the STAR-S8 brand/category and audience-brand evidence that is independent of any single deal; deal terms, availability, and campaign orchestration stay outside it. 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 shortlist entries carrying a stable opaque creator_ref plus either transient handles/profile URLs or resolvable opaque handle refs (supplied by the user or carried over from influencer-discovery). Optional prior audience profiles from memory/influencer/audience-mapper/, competitor partner benchmarks from memory/influencer/competitor-tracker/, and a WARM Campaign Retro Card's evidence_refs plus next_campaign_hypothesis when the user supplies or authorizes that handoff. For rostered creators, read partnership history and audience-stat provenance from memory/creators/<aggregate-id>.md — the creator-registry roster record — as Partnership Potential inputs.
  • Writes: return the typed Suitability (S) read and separately labeled commercial-fit comparison inline by default; when a Retro Card is supplied, preserve its hypothesis as a separately labeled next-cycle test constraint with no score or verdict effect. Save the report to memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.md only with exact WARM-save authorization. Saved reports and handoffs retain the stable opaque creator_ref and opaque evidence refs, never a raw handle, name, profile URL, email, provider ID, or deterministic hash in creator_ref.
  • Promotes: only with separate exact authorization, promote evidence-backed top picks and their exact Suitability (S) read and catalog version to memory/hot-cache.md; never promote an unscored/provisional result or the Retro Card's qualitative decision/hypothesis as scored truth.
  • Done when:
    • Every creator has all 10 Suitability items S1–S10 explicitly Pass/Partial/Fail/Unknown/N/A with dated evidence or a gap reason.
    • Every creator's stable opaque creator_ref is preserved from discovery/registry or generated once for this lineage; raw identity locators remain transient.
    • 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.
    • If a Retro Card is supplied, its next_campaign_hypothesis is visible only as a falsifiable test constraint/commercial-matrix context; its evidence_refs are pointers for fresh investigation, not STAR item evidence or an automatic selection rule.
  • Primary next skill: campaign-planner — turn the ranked shortlist into an approved campaign plan. If that plan is already approved and outreach-ready, hand off to outreach-manager instead; competitor benchmarking is optional.
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 — transient handles or profile locators, 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/<aggregate-id>.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 contract-compatible copied layouts live in references/scoring-templates.md: use the creator_ref-only typed STAR-S1–STAR-S10 evidence table for the Suitability read, then the optional commercial_fit_score tables for separate decision support. Never copy a raw locator into those outputs.

  1. Lock identity and typed context. Reuse the opaque creator_ref explicitly carried by discovery, or a creator-registry aggregate ID only when its handle link is verified. If the user supplies only a raw handle/profile URL and no verified aggregate exists, generate one random creator-<UUIDv4> and reuse it unchanged across this report, any authorized save, and downstream handoffs. Never set creator_ref to a raw handle, name, URL, email, provider ID, or deterministic hash of one; keep those locators transient for evidence acquisition. Resolve an opaque ref only through its accompanying authorized artifact or verified registry link. If neither is available, request the transient locator and preserve the identity as unresolved rather than guessing or merging. Then 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. When a Campaign Retro Card is supplied, record its evidence_refs and next_campaign_hypothesis in a separate non-scoring prior-cycle context block; neither becomes part of the STAR typed identity.
  2. Freeze evidence for the current window. Use current creator analytics, public observations, roster history, and cohort benchmarks with source/date/type/confidence. A Retro Card and its evidence_refs are discovery pointers only, never STAR item evidence. If a referenced primary source is independently reacquired and qualifies in the current evidence window, cite that fresh observation rather than the card. Missing or refused private access is Unknown, never Fail or Partial.
  3. Score Suitability only. Evaluate the Suitability items S1–S10 (audience composition/realness, follower-growth integrity, reach reliability, engagement health and authenticity, credibility, and deal-independent brand/category fit) from star-benchmark.md. Deal-specific commercial terms, availability, and orchestration conflicts 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 every S1–S10 state as exactly Pass/Partial/Fail/Unknown/N/A with source/date/window/type/confidence or an explicit gap/N/A reason under the locked brand/category/cohort context. This typed table—not any 1–5 aid—is the Suitability read. The creator-content-auditor gate folds it 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 complete Suitability read; never soften Unknown to Partial or hand-calculate a composite.
  6. Build the separate commercial matrix when requested. Use deal-specific audience/goal nuance, content concept, brand conflicts, commercial terms, availability, and partnership potential. A supplied next_campaign_hypothesis may appear beside the matrix as a falsifiable test constraint, but contributes zero points and no weight. Label every 1–5 component and its rollup commercial_fit_score; never call one “Fit Score” or “Final Score.” It is not a Suitability score, cannot clear a Suitability control finding, and never enters the SQS.
  7. Rank transparently. Show the typed Suitability read, critical controls, commercial_fit_score separately, evidence confidence, and an action tied to the declared rule with owner/rerun condition. Do not emit a generic Verdict or star rating. Route to campaign-planner by default; route to outreach-manager only when an approved campaign plan is already ready for execution. Offer competitor benchmarking as an optional check, not a mandatory detour. Do not rank an Unknown-heavy candidate as definitively superior, add/subtract points because a Retro hypothesis names a creator or tactic, or automatically select a candidate from a prior-cycle renew | retest | retire | unknown decision.
  8. Persist only with permission. Save the report only after exact WARM authorization; request separate authorization before any hot-cache promotion. Persist and hand off creator_ref plus opaque handle/evidence refs, not the transient raw identity locator. The Retro hypothesis remains WARM working context. This skill does not propose provisional commercial rankings or non-gate Suitability results to creator-registry.
Show full SKILL.md (385 more words)Show less

Compact Example

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

Output: Each creator reuses an upstream opaque creator_ref or receives one random creator-<UUIDv4> before scoring; the raw handles remain transient lookup inputs. Each creator then receives S1–S10 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. If a prior Retro Card is supplied, its hypothesis appears only as a zero-weight next-cycle test constraint and its sources are re-observed in the current window. 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: campaign-planner — turn the ranked shortlist into the campaign plan, budget, timeline, and approval path.

Conditional next step:

  • outreach-manager — only when an approved campaign plan already defines the offer, budget, target creator, channel, and outreach approval path; this handoff does not authorize a send.

Optional checks and alternates:

  • competitor-tracker — optionally benchmark top picks against competitor partnerships when that evidence would change the selection.
  • 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 the inline result plus any separately authorized save path.

© aaron-he-zhu, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in influencer/scout/fit-scorer of aaron-he-zhu/aaron-marketing-skills.

  • SKILL.md
  • references/scoring-templates.md

Open the folder on GitHubat commit d5529cb

Compare with similar skills

Fit Scorer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fit Scorer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Fit Scorer this skillaaron-he-zhu/aaron-marketing-skills2.9k—~4.2kAutomated safety check: PassApache-2.0
Audience ResearchScrapeCreators/social-media-research-skills3.4k—~635Automated safety check: NotesMIT
Influencer Discoverytigerless-labs/influencer-discovery212—~2.5kAutomated safety check: NotesNone
Opencloneteam-attention/openclone130—~2.6kAutomated safety check: NotesMIT
Reelclaw Adsdansugc/reelclaw145—~3.9kAutomated safety check: NotesMIT
Affiliate CheckAffitor/affiliate-skills701—~808Automated safety check: NotesMIT

Similar skills

  • Audience Research

    ScrapeCreators/social-media-research-skills

    A skill your agent uses when the user wants to evaluate a creator, influencer, or brand audience using public profile signals, TikTok audience demographics, follower/following data, comments…

    3.4k GitHub stars~635 tokensUpdated 1 mo ago
    Marketing & SEOAuto-check: notes
  • Influencer Discovery

    tigerless-labs/influencer-discovery

    Find the bloggers/creators who can help promote your work, capture their contact info, and append them to the target sheet in Google Sheets.

    212 GitHub stars~2.5k tokensUpdated 19 days ago
    Marketing & SEOAuto-check: notes
  • Openclone

    team-attention/openclone

    Create, manage, or talk to an openclone "clone" — a named AI persona with one or more categories (vc, tech, founder, expert, influencer, politician, celebrity) and attached knowledge.

    130 GitHub stars~2.6k tokensUpdated 1 mo ago
    Marketing & SEOAuto-check: notes
  • Reelclaw Ads

    dansugc/reelclaw

    Make short-form UGC video ads (TikTok, Reels, Shorts) for the product in the current repo with DansUGC ReelClaw and real human creator reactions from the DansUGC library.

    145 GitHub stars~3.9k tokensUpdated 12 days ago
    Marketing & SEOAuto-check: notes
  • Affiliate Check

    Affitor/affiliate-skills

    Live affiliate program data from openaffiliate.dev. An agent skill from Affitor/affiliate-skills.

    701 GitHub stars~808 tokensUpdated 26 days ago
    Marketing & SEOAuto-check: notes
  • Effect Monitoring

    vivy-yi/xiaohongshu-skills

    A skill your agent uses when monitoring Xiaohongshu marketing campaign performance, tracking promotion effectiveness, analyzing advertising ROI, measuring influencer collaboration results…

    481 GitHub starsUsed in 1 repo~4.5k tokens
    Marketing & SEOAuto-check passed

More from aaron-he-zhu/aaron-marketing-skills

All 119 skills in this repo
  • Ad Account Auditor

    aaron-he-zhu/aaron-marketing-skills

    A skill your agent uses when auditing a paid ad account for incremental contribution, wasted spend, or measurement integrity before scaling; runs a typed 20-item ROAS profile with verified vetoes…

    2.9k GitHub starsUsed in 2 repos~2.2k tokens
    Auto-check passed
  • Ad Creative Builder

    aaron-he-zhu/aaron-marketing-skills

    A skill your agent uses when the user asks to "write ad copy", "generate RSA headlines", or "build ad creative at volume"; produces ad units — RSA headlines/descriptions, hooks, and an angle matrix…

    2.9k GitHub starsUsed in 2 repos~2.2k tokens
    Auto-check passed
  • Ad Test Designer

    aaron-he-zhu/aaron-marketing-skills

    A skill your agent uses when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"…

    2.9k GitHub starsUsed in 2 repos~2.8k tokens
    Auto-check passed
  • Bid Strategy Planner

    aaron-he-zhu/aaron-marketing-skills

    A skill your agent uses when the user asks to "pick a bid strategy", "set a tCPA/tROAS target", or "plan the learning-phase entry"; produces a bid-strategy choice (tCPA / tROAS / max-conversions /…

    2.9k GitHub starsUsed in 2 repos~2.6k tokens
    Auto-check passed
  • Conversion Signal QA

    aaron-he-zhu/aaron-marketing-skills

    A skill your agent uses when the user asks to "QA my conversion tracking before launch", "check my UTMs / pixel / event firing", "set up a tracking pre-flight", or "set the dedup rule so Meta and…

    2.9k GitHub starsUsed in 2 repos~2.4k tokens
    Auto-check passed
  • Creator Registry

    aaron-he-zhu/aaron-marketing-skills

    A skill your agent uses when the user asks "what did we pay this creator last time" or to "update the creator roster"; curates creator identity, rate, rights, exclusivity, compliance-event, and…

    2.9k GitHub starsUsed in 2 repos~1.6k tokens
    Auto-check passed

Categories

Questions about Fit Scorer

What does Fit Scorer do?

A skill your agent uses 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)…. Fit Scorer is an agent skill from aaron-he-zhu/aaron-marketing-skills. 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.

When should I use Fit Scorer?

Fit Scorer fits situations like: the user asks to score this influencer; rank these creators for our campaign; tell me which influencer is the best fit.

How do I install Fit Scorer in Claude Code?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill fit-scorer -a claude-code`. Or copy the skill folder (influencer/scout/fit-scorer in aaron-he-zhu/aaron-marketing-skills) into .claude/skills/fit-scorer in your project. Claude Code loads it when a task matches its description.

How do I install Fit Scorer in Codex?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill fit-scorer -a codex`. Or copy the skill folder (influencer/scout/fit-scorer in aaron-he-zhu/aaron-marketing-skills) into .agents/skills/fit-scorer in your project. Codex loads it when a task matches its description.

Can I use Fit Scorer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill fit-scorer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fit-scorer, .gemini/skills/fit-scorer, .github/skills/fit-scorer and .opencode/skills/fit-scorer in your project.

What does Fit Scorer need to run?

Going by SKILL.md and its folder, Fit Scorer needs the command-line tools its instructions call (python3) and credentials named YOUTUBE_API_KEY. Our summary lists: Python 3; A credential in YOUTUBE_API_KEY. Compatibility (from SKILL.md): Claude Code and compatible agent-skill hosts.

Does Fit Scorer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fit Scorer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Fit Scorer use?

Fit Scorer is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fit Scorer use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.

What are the alternatives to Fit Scorer?

Skills that share tags, products or a category with Fit Scorer: Audience Research (ScrapeCreators/social-media-research-skills, 3.4k stars), Influencer Discovery (tigerless-labs/influencer-discovery, 212 stars), Openclone (team-attention/openclone, 130 stars) and Reelclaw Ads (dansugc/reelclaw, 145 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fit Scorer?

aaron-he-zhu (a GitHub user) maintains it in aaron-he-zhu/aaron-marketing-skills, which has 2,898 GitHub stars. The repository holds 119 skills in this directory. The repository was last updated on October 11, 2026.

Source: aaron-he-zhu/aaron-marketing-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.