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Agent Skill · 研究检索

seo-maker(SEO maker 搜索)

seo-maker 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

seo-maker 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为操作浏览器;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 62,819。

总安装 326周安装 14GitHub Stars 3下载 114核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:seo-maker(SEO maker 搜索)
来源仓库:https://github.com/alpoxdev/hypercore
仓库路径:skills/seo-maker
安装命令:
npx skills add https://github.com/alpoxdev/hypercore --skill seo-maker
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/alpoxdev/hypercore --skill seo-maker

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果。适用于 SEO 制作、策略制定、信息收集和线索筛选等场景。通过关键词、任务场景或来源线索快速定位候选结果,可结合来源仓库继续核验具体用法。

安装命令:npx skills add https://github.com/alpoxdev/hypercore --skill seo-maker。建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

来源仓库内路径:skills/seo-maker

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/alpoxdev/hypercore --skill seo-maker;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

seo-maker 怎么安装?

在终端执行 npx skills add https://github.com/alpoxdev/hypercore --skill seo-maker。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

seo-maker 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

seo-maker 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

seo-maker 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

seo-maker 用的人多吗?

公开数据是累计安装 326 次,近一周安装 14 次,下载 114 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

seo-maker 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
seo-maker

SEO maker 搜索 · 本页

3263操作浏览器未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

@rules/seo-workflow.md @rules/validation.md

SEO Maker

Audit and improve a project's search visibility across traditional search engines and AI answer engines.
  • Audit website or project SEO in a systematic way.
  • Cover on-page SEO, technical SEO, content SEO, and Core Web Vitals.
  • Evaluate AEO readiness for featured snippets, voice search, and direct-answer surfaces.
  • Evaluate GEO readiness for citation likelihood in generative AI responses.
  • Evaluate LLMO readiness for AI crawler access, freshness, and model-readable context.
  • Save prioritized recommendations and evidence under .hypercore/seo-maker/[slug]/.
  • Update existing reports so SEO improvement history remains traceable.
  • If the user asks for highest score, max score, maximum score, perfect score, or continuous improvement, run an audit to fix/recommendation to re-audit loop and keep the best result.

<routing_rule>

Use seo-maker when the main outcome is an SEO/AEO/GEO/LLMO audit, optimization report, or evidence-backed search visibility improvement plan.

Route neighboring work elsewhere:

  • Page or product UI design: use designer or the relevant frontend design skill.
  • Competitor or market research without site audit: use research.
  • Pre-release build and deployment checks: use pre-deploy.
  • Pure performance engineering without search context: use the relevant performance or optimization workflow.
  • Broad AI search trend research without a target site or content set: use research.

</routing_rule>

<trigger_conditions>

Positive examples:

  • "Audit this site's SEO."
  • "Check metadata and structured data."
  • "Create an SEO audit report."
  • "Review search-engine optimization status and give improvement recommendations."
  • "Summarize how to improve Core Web Vitals scores."
  • "Optimize our content so AI search engines can cite it."
  • "Check whether ChatGPT or Perplexity can surface our brand."
  • "Analyze this site from AEO and GEO perspectives."
  • "Keep iterating fixes until the SEO score is as high as possible."
  • "Audit, fix, and re-verify until the search optimization score is close to perfect."

Negative examples:

  • "Design this landing page." -> use designer.
  • "Research competitor market positioning." -> use research.
  • "Check the pre-deploy checklist." -> use pre-deploy.

Boundary examples:

  • "Optimize this page's performance." Use seo-maker only when performance is evaluated through SEO/Core Web Vitals impact.
  • "Research AI search trends." Use seo-maker only when the output is tied to a target site, page, or content inventory.

</trigger_conditions>

SituationMode
Full SEO audit for a new project or sitecreate
On-page SEO review for a specific pagecreate
Add a new analysis to an existing SEO reportupdate
Focused Core Web Vitals or technical SEO analysiscreate
Re-check after SEO improvementsupdate
Iterative improvement toward best or perfect scoreoptimize
AEO/GEO citation readiness analysiscreate
Add AEO/GEO analysis to an existing reportupdate

<supported_targets>

  • Metadata and SEO elements in HTML pages and Next.js/React components.
  • robots.txt, sitemap.xml, llms.txt, canonical tags, and structured data.
  • Core Web Vitals signals such as LCP, INP, and CLS.
  • <head> elements including title, meta description, Open Graph, and Twitter Card.
  • Heading hierarchy from h1 through h6.
  • Image alt text and internal link structure.
  • Schema.org JSON-LD markup, including AI trust signals.
  • AEO elements such as Q&A formats, direct-answer structure, and featured-snippet optimization.
  • GEO elements such as citable sentence structure, statistics with sources, and entity authority.
  • LLMO elements such as llms.txt, AI crawler accessibility, and content freshness.

</supported_targets>

<complexity_routing>

ComplexitySignalsHandling
SimpleSingle-page review, one SEO element, quick metadata auditDirect: write report.md immediately
ComplexFull-site audit, many pages, technical SEO plus content SEO plus Core Web Vitals, competitor comparisonTracked: use flow.json for phase tracking

Before starting, record:

Complexity: [simple/complex] — [one-line reason]
Mode: [create/update/optimize]
Target: [site/page/project path]
Proof surface: [commands, browser checks, web sources, or local files]

</complexity_routing>

<universal_intake>

Before scoring any project, classify the audit context so this skill works across stacks:

  • target_type: live-url, local-static, nextjs, react-spa, docs-site, ecommerce, blog, or app-with-marketing-pages
  • access_level: live URL, local files only, Search Console available, analytics available, field Core Web Vitals available, or AI citation probe available
  • allowed_action: audit-only, recommend, edit-code, or optimize-loop
  • measurement_confidence: lower confidence when live URL, Search Console, field Core Web Vitals, or AI citation probes are unavailable

Do not hide missing evidence. If a recommendation is based on static files, lab data, synthetic probes, or heuristics, label it that way in results.json.

</universal_intake>

<artifact_contract>

Create or update .hypercore/seo-maker/[slug]/.

Expected files:

.hypercore/seo-maker/[slug]/
├── dashboard.html      # Browser-readable dashboard
├── results.json        # Structured audit results
├── results.js          # File URL fallback for browser rendering
├── report.md           # Markdown report
├── sources.md          # Source and evidence log
└── flow.json           # Required for complex or optimize mode

For simple mode, report.md and sources.md are the minimum. For complex or optimize mode, all files are expected.

Follow references/artifact-spec.md for the file schema.

Render order:

  1. Gather evidence and write/update results.json.
  2. Generate results.js for direct local browser viewing.
  3. Render dashboard.html from the current results.
  4. Write report.md and sources.md with links or file references.

</artifact_contract>

PhaseTaskOutput
0Determine target, mode, complexity, proof surface, and universal intake fieldsExecution brief
1Establish measurement methods and confidence limitsmeasurement_methods
2Collect evidence from local code, pages, browser checks, and web sourcesEvidence log
3Audit technical SEO, platform policy, AEO, GEO, LLMO, Core Web Vitals, and structured dataStructured findings
4Separate official requirements from field/tool/lab/synthetic/heuristic findingsEvidence-graded findings
5Prioritize issues by impact, confidence, effort, and source tierRecommendation set
6Write artifacts and dashboard.hypercore/seo-maker/[slug]/
7If optimize mode, apply or recommend fixes and re-auditBest verified result
8Summarize score, wins, confidence limits, risks, and next actionsFinal report

<audit_dimensions>

Check these dimensions when relevant to the target:

  • Technical SEO: crawlability, indexability, canonicalization, sitemap, robots directives, response status, redirects, and duplicate pages.
  • Platform policy: Googlebot, Google-Extended, OAI-SearchBot, GPTBot, ChatGPT-User, PerplexityBot/ClaudeBot when present, snippet controls, X-Robots-Tag, and optional llms.txt.
  • On-page SEO: title, description, heading hierarchy, keyword alignment, URL readability, and internal links.
  • Content SEO: intent match, depth, freshness, topical coverage, uniqueness, and readability.
  • Core Web Vitals: LCP, INP, CLS, render-blocking resources, image sizing, and interaction latency.
  • Structured data: JSON-LD validity, Schema.org fit, visible-content parity, entity identifiers, breadcrumbs, FAQs, products, articles, or organization markup. Do not imply structured data guarantees rich results or AI citations.
  • AEO: concise visible answer blocks, Q&A structure, snippet-ready summaries, voice-search phrasing, and direct-answer clarity. Treat fixed answer lengths as heuristic.
  • GEO: citable claims, statistics with sources, entity authority, author or brand trust signals, and content that AI systems can quote safely.
  • LLMO: optional llms.txt, AI crawler access, clean markdown or semantic HTML, clear entity relationships, and updated canonical content. Missing llms.txt is not critical by default.

</audit_dimensions>

Use a transparent 100-point score when enough evidence exists:

  • Technical SEO: 20
  • On-page SEO: 20
  • Content SEO: 15
  • Core Web Vitals: 15
  • Structured data: 10
  • AEO readiness: 10
  • GEO/LLMO readiness: 10

If evidence is incomplete, mark affected categories as unknown instead of inventing certainty.

Each finding should include:

  • Severity: critical, warning, or info (use impact/effort fields for prioritization beyond severity).
  • Confidence: high, medium, or low.
  • evidence_grade: official, field, tool, lab, synthetic, or heuristic.
  • measurement_method: scan, tool, probe, source, or command used.
  • source_tier: official-doc, observed-file, field-data, tool-output, synthetic-probe, or research-backed-heuristic.
  • Evidence: command output, URL, local file path, browser observation, or saved probe result.
  • Recommendation: specific action and expected impact.
  • Owner surface: code, content, infrastructure, analytics, or external platform.

<optimize_loop>

Use optimize mode when the user requests a maximum score, perfect score, continuous iteration, or "keep fixing until it passes" behavior.

Loop rules:

  1. Run a baseline audit and write the score.
  2. Pick the highest-impact fix or recommendation with the best confidence/effort ratio.
  3. Apply safe local code/content fixes when they are in scope; otherwise record an actionable recommendation.
  4. Re-run the relevant audit checks.
  5. Keep the change only if the score or verified evidence improves without regression.
  6. Stop when the score target is met, no safe local fixes remain, or further work requires external credentials or business decisions.

Do not fake a perfect score. If external evidence is unavailable, report the unknowns and the best verified score.

</optimize_loop>

At completion, .hypercore/seo-maker/[slug]/ should contain:

  • results.json with structured audit results and status complete for complex or optimize mode.
  • dashboard.html rendered from the latest results when dashboard output is expected.
  • results.js for local browser fallback when dashboard output is expected.
  • report.md with prioritized findings, score, and recommendations.
  • sources.md with the evidence log.

Validate:

  • Every critical or warning finding has evidence.
  • Recommendations are specific enough for an engineer, marketer, or content owner to act on.
  • Scores are derived from observed evidence, not assumptions.
  • Google AI features are not described as requiring special schema, AI text files, or magic markup.
  • FAQPage recommendations distinguish Google rich-result eligibility from answer-friendly visible FAQ content.
  • Unknowns are explicitly marked.
  • Optimize mode records baseline score, changes/recommendations, re-audit evidence, and the best verified result.

平台分布

Codex

34.34%
按下载量换算39

Claude

31.14%
按下载量换算35

Cursor

17.77%
按下载量换算20

Gemini CLI

8.48%
按下载量换算10

安全审计

Gen Agent Trust Hub

未通过

Socket

可疑

Snyk

可疑

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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