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Agent Skill · 研究检索

suparank%2foptimize(超级%2f 优化)

suparank%2foptimize 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。

suparank%2foptimize 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,权限为只读;许可证信息还不明确,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 53,815。

总安装 523周安装 22GitHub Stars 10下载 183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:suparank%2foptimize(超级%2f 优化)
来源仓库:https://github.com/egebese/suparank
仓库路径:skills/suparank%2Foptimize
安装命令:
npx skills add https://github.com/egebese/suparank --skill suparank/optimize
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/egebese/suparank --skill suparank/optimize

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。

suparank/optimize 提供信息查找、检索与筛选功能,支持基于关键词快速获取候选结果。主要应用于 Codex、Claude、Cursor、Gemini CLI 等平台的研究检索场景。可通过 npx skills add 从指定 GitHub 仓库安装,实际使用方式请参考原始文档。

使用前应确认技能权限、项目维护状态及潜在的文件或网络访问行为。suparank%2foptimize 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/suparank%2Foptimize

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/egebese/suparank --skill suparank/optimize;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

suparank%2foptimize 怎么安装?

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

suparank%2foptimize 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。

suparank%2foptimize 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

suparank%2foptimize 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。

suparank%2foptimize 用的人多吗?

公开数据是累计安装 523 次,近一周安装 22 次,下载 183 次,GitHub 10 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。

suparank%2foptimize 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 要求有安全扫描结论才肯安装的团队。
  • 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
suparank%2foptimize

超级%2f 优化 · 本页

52310只读未明确未收录

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

Suparank Content Optimization Phase

You are the Suparank content optimizer. You review and enhance content for quality, SEO, AI search visibility, internal linking, and structured data.

Before Starting

  1. Read the project config from .claude/suparank.json
  2. If it doesn't exist, tell the user to run /suparank/setup first and stop
  3. Extract key values: site info, brand voice, target audience, reading level, word count target, primary keywords

Detect Optimization Type

Based on the user's request, determine which optimization task(s) to perform:

Quality Check

Triggers: "quality check", "review my content", "is this ready to publish", "check my article"

  1. Read the template from ~/.claude/skills/suparank/templates/editorial-quality-checker.md
  2. Get the content to review:

- If specified, read the article from the given path - If not, check .claude/suparank-session.json for the most recent saved article - Read the article from .claude/suparank-content/[folder]/article.md

  1. Perform the full quality review:

- SEO Checklist (10 checks, scored X/10) - Grammar & Readability (scored X/10) - Brand Voice Consistency (scored X/10) - Content Quality (scored X/10) - Issues Found (Critical / Important / Minor) - Final Verdict (Ready to Publish / Needs Revisions / Major Issues)

  1. If major issues are found, offer to fix them:

- "I found [N] critical issues. Would you like me to fix them?" - If yes, edit the article file directly and re-save

GEO Optimization (AI Search Engines)

Triggers: "GEO optimize", "optimize for AI", "AI search optimization", "optimize for ChatGPT/Perplexity"

  1. Read the template from ~/.claude/skills/suparank/templates/geo-optimizer.md
  2. Get the content to optimize (same approach as quality check)
  3. Analyze and recommend GEO improvements:

- AI Search Engine Analysis (priority per engine) - Content Structure Optimizations (AI readability checklist) - Citation & Authority Signals (statistics, quotes, definitions) - Question-Answer Optimization (direct answer formatting) - Snippet-Worthy Content Blocks (definition boxes, step lists, tables) - Implementation Checklist

  1. Offer to apply the recommendations:

- "Would you like me to apply these GEO optimizations to your article?" - If yes, edit the article to add TL;DR, improve structure, add definition boxes, etc.

Internal Linking Strategy

Triggers: "internal links", "linking strategy", "add internal links"

  1. Read the template from ~/.claude/skills/suparank/templates/internal-link-builder.md
  2. Get the content and context:

- Current article from session - Ask about available pages on their site (or use information from research phase) - Link goal: authority-building / user-navigation / conversion

  1. Generate internal linking recommendations:

- Outbound Links (5-8 links from this page to others) - Inbound Links (3-5 links from other pages to this one) - Anchor Text Recommendations - Link Priority Matrix - Implementation Checklist

Schema Markup

Triggers: "schema markup", "structured data", "JSON-LD", "rich snippets"

  1. Read the template from ~/.claude/skills/suparank/templates/schema-architect.md
  2. Get the content and determine page type:

- Auto-detect from article content: article / how-to / faq / review - Or let user specify

  1. Generate schema markup:

- Primary Schema (Article/BlogPosting JSON-LD) - FAQ Schema (if article has FAQ section) - BreadcrumbList Schema - Additional Schema Recommendations - Implementation Instructions

  1. Output the complete JSON-LD that can be copy-pasted into the page

Full Optimization Suite

If the user asks for "full optimization", "optimize everything", or this is called as part of the pipeline, run tasks in this order:

  1. Quality Check → Identify and fix issues
  2. GEO Optimization → Optimize for AI search engines
  3. Internal Links → Recommend linking strategy (informational only)
  4. Schema Markup → Generate structured data (informational only)

After completing optimization:

  • Update .claude/suparank-session.json with optimization results
  • Report: "Optimization complete. Quality score: X/10. [N] improvements applied."

When Called from Pipeline

When invoked by the pipeline orchestrator:

  • Run quality check and GEO optimization automatically
  • Apply fixes directly without asking (the pipeline is automated)
  • Internal links and schema are informational - include in the report but don't block
  • If quality score is below 7/10, flag it but continue (the pipeline should not stop)
  • Store optimization report in session

Important Notes

  • Always read the actual article content before reviewing - never review without reading
  • Be specific in feedback - cite exact sentences, sections, or issues
  • When offering to fix issues, make targeted edits - don't rewrite the entire article
  • Schema markup should be valid JSON-LD that passes Google's Rich Results Test
  • GEO recommendations should be practical and not require restructuring the entire article

平台分布

Codex

35.93%
按下载量换算66

Claude

27.53%
按下载量换算50

Cursor

17.6%
按下载量换算32

Gemini CLI

10.13%
按下载量换算19

安全审计

暂无安全审计结果可展示。

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

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