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How to present the deterministic AI-SEO audit returned by audit_page. Use when reporting a page's readability and citation-readiness for AI answer engines (ChatGPT, Claude, Perplexity).
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/shadcn-labs/agentcn --skill seo-audit명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Migrate Mastra code-based agents (agents built with new Agent(...) and registered in a Mastra({ agents }) map) to the file-based convention: one directory per agent under src/mastra/agents. Use when the user wants to convert or refactor Mastra agents to the per-directory convention, mentions Mastra "file-based agents" or agentConfig, or wants to split a big src/mastra/index.ts or agents.ts into one folder per agent — even if they do not name this skill. Requires @mastra/core 1.48+.
How to present the deterministic AI-SEO audit returned by audit_page. Use when reporting a page's readability and citation-readiness for AI answer engines (ChatGPT, Claude, Perplexity).
Use when building, debugging, reviewing, or documenting Flue agents, workflows, channels, skills, tools, sandboxes, targets, routing, persistence, observability, or CLI usage; routes coding agents to version-matched Flue documentation through the CLI.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | seo-audit |
| description | How to present the deterministic AI-SEO audit returned by audit_page. Use when reporting a page's readability and citation-readiness for AI answer engines (ChatGPT, Claude, Perplexity). |
How well a page can be crawled, chunked, understood, and cited by AI answer
engines — not classic blue-link SEO. The audit_page tool computes this
deterministically: page data flows through context.dev, then a fixed rubric of
~30 checks scores six categories into a 0–100 total. Your job is to present
that result, not to re-score it.
audit_page returns JSON with:
score (0–100) and band ({ label, interpretation }).stats — words, headings, links, externalLinks, jsonLdBlocks, char counts.categories[] — each { id, name, score, maxScore, naExcluded, items[] }.categories[].items[] — each check: { id, label, status, evidence, recommendation, score, maxScore }. status is pass | partial | fail | na.topPriorities[] — the highest-impact failing/partial checks, pre-sorted.agentPrompts — { full, topIssues[] }, ready-to-run fix prompts.diagnostics — context.dev fetch status and JSON-LD parse failures.| ID | Category | Max |
|---|---|---|
| A | Technical AI Crawlability | 28.4 |
| B | Content Structure & Chunking | 25.4 |
| C | Structured Data / Schema | 13.4 |
| D | E-E-A-T & Entity Authority | 21.4 |
| E | Off-site / Citation Surface Presence | 8.4 |
| F | Measurement & Governance | 3 |
Scoring (already applied by the tool): pass = full weight, partial = half,
fail = 0, na = excluded from the denominator. Bands: Critical ≤40 ·
Below average ≤55 · Average ≤70 · Strong ≤85 · Excellent >85.
Report, in order:
score/100, its band.label, and the band.interpretation.score/maxScore and the
pass/partial/fail counts.topPriorities
and the matching items (cite each check's id, evidence, and
recommendation).agentPrompts.full verbatim in a
copy-paste block.Report only the checks and numbers the tool returned. Never invent or recompute scores, checks, evidence, or content.