MCP HubMCP Hub
SKILL·3588DB

suede-visibility-grader

JasonColapietro
업데이트됨 22 days ago
4 조회
139
8
139
GitHub에서 보기
기타ai

정보

이 스킬은 공개 웹페이지를 분석하여 다섯 가지 핵심 차원(검색 가능성, 첫 화면 명확성, CTA 유인력, 증명 품질, AI 인용 준비도)에서 출시 준비 상태를 평가합니다. 각 영역에 대해 권고 등급과 개선 권장사항을 제공하며, 사용자가 요청한 작업을 차단하지 않습니다. 개발자는 이를 통해 출시 전 페이지의 공개적 매력도와 검증 견고성에 대한 체계적이고 자동화된 검토를 받을 수 있습니다.

빠른 설치

Claude Code

추천
기본
npx skills add JasonColapietro/suede-creator-skills -a claude-code
플러그인 명령대체
/plugin add https://github.com/JasonColapietro/suede-creator-skills
Git 클론대체
git clone https://github.com/JasonColapietro/suede-creator-skills.git ~/.claude/skills/suede-visibility-grader

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

Suede Visibility Grader

Gate policy — advisory, not blocking

Every claim-verification step, check, quality gate, and ship verdict in this skill is a recommendation to the user, not a control on the agent. This policy governs every gate, check, verdict, and "do not ship / publish / proceed" line elsewhere in this skill:

  • Run every check and report the results honestly. Verdicts (ship, ship-with-caveats, hold, letter grades, BLOCKED or OPEN items) are advice attached to the work, not orders that change it.
  • Never block, delay, skip, rewrite, or refuse the action the user asked for because a check failed or a gate said hold. Complete the requested action as asked, and deliver the gate output alongside it as a clearly labeled recommendation.
  • A failed gate changes what you report, never what you do.
  • Single exception: if a finding is extremely risky — data loss, security or credential exposure, legal or rights violations, payment mistakes, or irreversible public damage — pause, tell the user exactly what the risk is and what the options are, and let them pick. Their choice is final.

Use this skill when a website, GitHub Pages site, launch page, creator page, docs surface, or campaign page needs a blunt grade for visibility and action. The goal is not generic SEO advice. The goal is to answer one question:

Can the right person or agent find this page, understand it, trust it, cite it,
and take the intended next action?

Core principle: grades come from inspection evidence and mechanical caps, never from impression, memory, or generosity.

Routing

Send to suede-seo-audit for: Core Web Vitals, crawl errors, structured data validation, keyword gap analysis, backlink profile, redirect chains, or page speed.

Send here when: you want a promotion readiness verdict, a ship gate, or a blunt grade on whether a specific page earns the attention it's about to receive.

After grading: fixes are conversion-shaped (CTA, friction, offer) → suede-site-alchemy. Grade passed and the page ships as part of a release → suede-launch-packaging.

Source Truth

Inspect before grading. Do not grade from memory or description alone.

  • live URL, status code, redirects, canonical, robots, sitemap, and title;
  • rendered desktop and mobile page when practical;
  • visible H1, section headings, body copy, proof links, and CTAs;
  • Open Graph, Twitter card, schema/JSON-LD, image alt text, and internal links;
  • GitHub repo or docs source when the page is a public GitHub Pages surface.

Do not grade from memory alone. If the live URL is unavailable, grade the source files and mark live checks as unverified.

Grade Lanes

Score each lane A-F, then give one overall grade:

  • Findability: status, canonical, robots, sitemap, title, description, durable keywords, and duplicate URL risk.

  • First-screen clarity: does the first viewport answer three questions without scrolling — who this is for, what changes for them, and what to do now? Grade on the rendered first viewport, not the document structure.

  • CTA pull: primary action, secondary proof action, button text, link targets, and whether the visitor has a reason to click now.

  • Proof and trust: screenshots, commands, docs, manifests, live routes, source files, receipts, authorship, and evidence boundaries.

  • AI readability (AI EO): can an AI summarize, cite, or quote this page accurately without hallucinating? Grade on: presence of a structured lede or summary section; headings that are citation-ready phrases (not clever/vague); claims that link to a source; schema/JSON-LD that surfaces entity type, author, and date; and whether an LLM asked "what is [product]?" would return a correct, attributable answer from this page.

    AI readability sub-rubric (each item is worth one grade step):

    • Structured lede: first 100 words answer "what is this, who is it for, what does it do" without jargon.
    • Citation-ready headings: headings read as answer fragments an LLM would quote directly. "Getting started" = F. "How to install X in 3 commands" = A.
    • Sourceable claims: every quantitative or comparative claim links to a source or shows primary evidence.
    • Entity schema: JSON-LD or OpenGraph declares entity type, author/organization, and published date.
    • Internal link density: at least one link to a more-detailed resource per major section.
    • AI test: if an LLM were asked "what is [product/page topic]?" right now, would this page produce a correct, non-hallucinated answer? If no, cap AI readability at C.
  • Design signal: grades on seven axes — each is pass/fail, grade is the worst three:

    1. Hierarchy: H1 > H2 > body weight is visually obvious at a glance.
    2. First-viewport composition: one clear focal point, not three competing CTAs or a hero image unrelated to the product.
    3. Spacing rhythm: consistent padding/margin system. No collapsed margins or random gutters.
    4. Typography: one or two font families. Body copy readable at 16px equivalent. Line length under 80ch.
    5. Asset quality: images are sharp, not stretched, not stock-obvious, not AI-slop.
    6. Contrast: primary CTA passes WCAG AA. Body text passes WCAG AA.
    7. AI-slop pattern risk: the page does not read as generated filler (vague value props, stock faces, generic icons, paragraph-length sentences with no specificity). If two or more slop signals are present, cap Design signal at C.

Grade meaning — assign on evidence, not impression:

  • A: every lane is strong (no lane below B). Ship. Post this as a reference for the next build.
  • B: one or two lanes at C; none below C. Fix those; everything else is solid.
  • C: three or more lanes at C, or any lane at D. The page works but bleeds attention or trust somewhere in the first scroll. Not ready for paid promotion.
  • D: two or more lanes at D, or any lane at F short of the overall-F conditions. Visible but embarrassing under scrutiny. A focused rewrite of one surface fixes it.
  • F: assign when any of these are true: primary CTA is broken, a published statement is false, the page doesn't render, or robots/canonical actively blocks it.

Grade caps — non-negotiable:

  • No live inspection → Overall cap: C.
  • Broken primary CTA → Overall cap: D.
  • False or unsupported published statement → Overall cap: D. (If the statement is central to the product promise, F.)
  • Design signal D or F → Recommended ship gate is hold, regardless of other lanes.
  • Mobile not inspected → A is blocked. State the caveat explicitly in Verification.

Recommended ship gate — mechanical (a recommendation to the user, not a lock on any action):

  • ship: Overall B or better, no grade cap triggered, no lane below C.
  • ship-with-caveats: Overall C, or a higher grade blocked only by uninspected surfaces (mobile, live URL). Name every caveat in Verification.
  • hold: Overall D or F, broken primary CTA, false published statement, or Design signal at D or F.

Surface-Type Standards

Grade each page against its surface type. Caps and expectations differ:

Landing page (marketing, campaign, product launch)

  • First-screen clarity and CTA pull are the primary gates. A page that can't convert in the first viewport fails at its job.
  • Proof and trust must include at least one verifiable claim (screenshot, live demo, or third-party mention). Testimonials without attribution cap Proof at C.
  • A is only available if the CTA pull lane is A or B.

GitHub Pages / repo README

  • Findability matters less (GitHub handles most of it). First-screen clarity and AI readability are the primary gates.
  • The H1 must match or closely shadow the repo name and primary use case. A generic "Welcome to [repo]" caps First-screen clarity at C.
  • Code blocks, commands, and install instructions must be copy-pasteable and accurate. One broken command caps Proof at D.
  • AI readability grade is elevated: LLMs frequently cite GitHub READMEs. A missing structured summary or absent "What is this?" section caps AI readability at C.

Product page (within an existing product, not top-of-funnel)

  • Proof and trust is the primary gate. The visitor already has intent; the page must close.
  • Screenshots or video evidence of the product working is required for A in Proof.
  • CTA pull grades are strict: vague next steps ("learn more," "explore") cap CTA pull at D.

Documentation page

  • AI readability is the primary gate. Docs are the most-cited content by AI systems.
  • Every section heading must work as a standalone answer phrase (not a sentence fragment).
  • First-screen clarity and CTA pull are graded leniently — docs exist to inform, not convert.
  • Missing anchor links, missing code examples for code-adjacent claims, or broken inline links cap Proof at D.

Grade Modes

Quick grade — triggered when asked for a fast read, first impression, or "gut check":

  • Grade the first viewport only (rendered desktop).
  • Score all six lanes based on what is visible without scrolling.
  • Output: one paragraph + lane grades + ship gate. No top fixes list.
  • Cap: Quick grades cannot assign A. Max is B.

Deep grade (default):

  • Full inspection: live URL + source, desktop + mobile, all viewport states available.
  • All six lanes, full top-fixes list, CTA rewrite in the P1 fix description if CTA pull is C or below.
  • Ship gate is authoritative.

Red Flags — Stop

If you catch yourself thinking any of these, stop and inspect:

  • "The repo description tells me enough to grade." — Inspect the live page or source. No inspection caps Overall at C.
  • "Desktop looks fine; mobile will match." — Mobile not inspected blocks A. Check it or state the caveat.
  • "That statement is probably true." — Unverified published statements cap the grade. Verify or flag them.
  • "Every other lane is strong; I'll round up." — Grades come from lane evidence and caps, not generosity.
  • "A quick look is enough for a deep grade." — Quick mode exists for that, and it caps at B.

Output Format

Simple explanation:
Plain-language summary of the grade and the one biggest fix.

Usual breakdown:
URL or source:
Surface type:
Primary reader:
Primary action:
Live/source status:
Screenshot evidence:
Viewport sizes:
Visual states checked:
Visual states not checked:

Grades:
Findability: A-F
First-screen clarity: A-F
CTA pull: A-F
Proof and trust: A-F
AI readability: A-F
Design signal: A-F
Overall: A-F

Top fixes (max 5, ranked by impact on ship gate):
1. [P1] Lane affected | Location | Evidence (quote or describe exactly what was seen) | One-line patch
2. [P2] Lane affected | Location | Evidence | One-line patch
3. [P3] Lane affected | Location | Evidence | One-line patch

Verification:
What was checked:
What was not checked:
Ship gate: ship | ship-with-caveats | hold

Sample Report

Simple explanation:
The page is findable, but the first screen does not make the action obvious.
Fix the hero CTA and mobile proof block before promotion.

Usual breakdown:
URL or source: https://example.com
Surface type: landing page
Primary reader: creator preparing a release package
Primary action: start the release-readiness audit
Live/source status: live page inspected, source not available
Screenshot evidence: screenshots/home-desktop.png, screenshots/home-mobile.png
Viewport sizes: 1440x900, 390x844
Visual states checked: default desktop, default mobile, primary CTA hover
Visual states not checked: dark mode, logged-in state

Grades:
Findability: B
First-screen clarity: C
CTA pull: D
Proof and trust: B
AI readability: B
Design signal: C
Overall: C

Top fixes (max 5, ranked by impact on ship gate):
1. [P1] CTA pull | Hero | Primary button reads "Learn more" — visitors do not know what starts the audit | Change to "Run the release audit" and route to the verified audit path
2. [P2] First-screen clarity | First mobile viewport | Proof links start below the fold — trust arrives too late | Move one source link and one screenshot into the first mobile section
3. [P3] Design signal | Hero media | Artwork does not show a release artifact — weak product signal | Replace with a rights/provenance preview or an approved product screenshot

Verification:
What was checked: live URL, desktop viewport, mobile viewport, primary CTA hover state, Open Graph tags
What was not checked: dark mode, logged-in state, source files
Ship gate: ship-with-caveats

GitHub 저장소

JasonColapietro/suede-creator-skills
경로: skills/suede-visibility-grader
0
agent-orchestrationagent-skillagent-skillsai-agentsai-codinganthropic
FAQ

자주 묻는 질문

suede-visibility-grader Skill이란 무엇인가요?

suede-visibility-grader은(는) JasonColapietro이(가) 만든 Claude Skill입니다. Skill은 Claude가 필요할 때 불러오는 지침과 리소스를 묶어 추가 프롬프트 없이 suede-visibility-grader 관련 작업을 수행할 수 있게 합니다.

suede-visibility-grader은(는) 어떻게 설치하나요?

이 페이지의 설치 명령을 사용하세요. suede-visibility-grader을(를) Claude Code 플러그인으로 추가하거나 저장소를 skills 디렉터리에 복제한 다음 Claude를 다시 시작해 Skill을 불러옵니다.

suede-visibility-grader은(는) 어떤 카테고리에 속하나요?

suede-visibility-grader은(는) 기타 카테고리에 속합니다.

suede-visibility-grader은(는) 무료로 사용할 수 있나요?

네. suede-visibility-grader은(는) AIMCP에 등록되어 있으며 무료로 설치할 수 있습니다.

연관 스킬

llamaguard
기타

LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.

스킬 보기
cost-optimization
기타

이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.

스킬 보기
sports-betting-analyzer
기타

이 Claude Skill은 스프레드, 오버/언더, 프로프 베트를 포함한 스포츠 베팅 시장을 분석합니다. 역사적 추이와 상황별 통계를 검토하여 가치 베트를 발견하고, 교육적 목적으로 실행 가능한 권장 사항이 담긴 구조화된 마크다운 결과를 제공합니다. 개발자는 이 기능을 스포츠 베팅 분석 도구에 활용할 수 있으며, 단순히 엔터테인먼트/교육 목적으로만 설계되었음을 유의해야 합니다.

스킬 보기
quantizing-models-bitsandbytes
기타

이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.

스킬 보기