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Agent Skill · 研究检索

technical-articles(技术文章)

technical-articles 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。

technical-articles 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,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 39,065。

总安装 1,469周安装 60GitHub Stars 4,512下载 470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:technical-articles(技术文章)
来源仓库:https://github.com/epicenterhq/epicenter
仓库路径:skills/technical-articles
安装命令:
npx skills add https://github.com/epicenterhq/epicenter --skill technical-articles
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/epicenterhq/epicenter --skill technical-articles

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 中根据关键词或任务场景快速定位候选结果。通过 npx skills add 命令从指定 GitHub 仓库安装使用。安装前需确认权限范围、维护状态,以及是否会触发联网或文件读写操作。

technical-articles 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/technical-articles

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/epicenterhq/epicenter --skill technical-articl…;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

technical-articles 怎么安装?

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

technical-articles 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。

technical-articles 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。

technical-articles 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。

technical-articles 用的人多吗?

公开数据是累计安装 1,469 次,近一周安装 60 次,下载 470 次,GitHub 4,512 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。

technical-articles 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 4,512 星,维护活跃度相对可查。
  • 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。
  • 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。
  • 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
technical-articles

技术文章 · 本页

1,4694,512只读未明确通过

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

Technical Articles

All articles must follow writing-voice rules.

When to Apply This Skill

Use this pattern when you need to:

  • Write technical articles in docs/articles/ or blog-style engineering posts.
  • Turn headings and titles into arguments instead of neutral topic labels.
  • Structure prose, code, and visuals with deliberate rhythm and pacing.
  • Open with the core insight first instead of announcing the topic.
  • Edit drafts to remove AI tell patterns and keep writing concrete.

Core Principles

Title should BE the takeaway, not a topic. "Write Context to a File, Not a Prompt" not "Context Management in Agent Workflows".

Lead with a strong opening paragraph that states the key insight in plain language. Reader should get it in 5 seconds. Then go straight into code. Don't force a blockquote or pull-quote after the opening; if the insight needs a quotable summary, the opening paragraph already is one.

Code speaks louder than prose. Show real examples from actual codebases, not abstract foo/bar illustrations. If the code is self-explanatory, don't over-explain.

Section Headings Are Arguments

Section headings should make claims, not announce topics. The reader should know your position from the heading alone.

Bad (topic headings):

## What's in the binary ## How Go and Rust compare ## Why tree-shaking is difficult

Good (argument headings):

## Go and Rust: Your code IS the binary ## Bun (and Deno/Node): Your code rides on top of a VM ## Why tree-shaking the runtime is brutally hard

The first set describes what the section is about. The second set tells you what the section argues. A reader who only skims headings should walk away with the article's core argument.

This applies to the title too: "Bun Compile Is 57MB Because It's Not Your Code" is an argument. "Understanding Bun Compile Binary Size" is a topic.

Conversational Directness

Write like you're explaining to a peer, not presenting to an audience. Short declarative sentences. Opinions stated plainly. Concessions acknowledged without hedging.

Bad (formal article-speak):

The resulting bundle size of the bun build --compile command is notably large. With careful analysis, we can identify several contributing factors.

Good (direct, conversational):

A console.log("Hello World") compiles to 57MB. Your code adds almost nothing. The binary is the entire Bun runtime.

Parenthetical asides, dashes for emphasis, and sentence fragments are all fine when they serve clarity. "Stripping the JIT? Now your code runs 10-100x slower." reads better than a formally constructed alternative.

Visual Elements Are Tools, Not Checkboxes

ASCII diagrams, tables, and before/after code blocks are tools to reach for when they clarify something prose can't. They are not required ingredients.

Use a diagram when showing flow or architecture that's hard to describe linearly. Use a table when there's a genuine comparison with 3+ items. Use before/after code when the contrast IS the point. Skip all of them when the article doesn't need them.

When you have multiple independent reasons for something, write them as regular prose with natural transitions. Don't use numbered bold headings (**1. Bold heading** followed by explanation)—that pattern is one of the most recognizable AI writing tells.

Rhythm and Pacing

This is the most important section. Good articles alternate between prose and visuals. The reader's eye should bounce: context → code → explanation → diagram → implication. Neither prose nor code should dominate for long stretches.

The Rules

  1. Max 3-4 sentences of prose before a code block, diagram, or table. If you're writing more than that without a visual break, you're missing an opportunity.
  2. Every code block gets 1-2 sentences of setup before it. Don't drop code without context. But don't write a paragraph either.
  3. After a code block, one sentence of explanation is often enough. If the code is self-explanatory, skip it entirely and bridge to the next idea.
  4. Use line breaks between distinct thoughts. Don't pack three ideas into one paragraph. Each paragraph: one idea.

Good rhythm — prose and code alternate:

[1-2 sentences: what the problem is]

\`\`\`typescript
// code showing the problem
const result = table.find(id);  // O(n) scan every time
\`\`\`

[1 sentence: why this is bad, bridge to solution]

\`\`\`typescript
// code showing the solution
const result = index.get(id);   // O(1) lookup
\`\`\`

[1-2 sentences: what this means for the reader]

Bad rhythm — wall of prose, code at the end:

[Paragraph explaining the problem]
[Paragraph explaining the approach]
[Paragraph explaining the implementation]
[Paragraph explaining the result]

\`\`\`typescript
// single code block at the bottom
\`\`\`

The first version lets the reader verify each claim against code as they go. The second forces them to hold four paragraphs in memory, then mentally map them to code.

Writing the Opening

The opening paragraph carries the entire article. If someone reads nothing else, this paragraph should give them the insight.

Bad (topic announcement):

In this article, we'll explore how context management works in agent workflows and discuss some approaches to improving it.

Good (insight up front):

Write your context to a file, not a prompt. When a conversation spawns sub-agents, each one starts with a blank slate. If the context lives in a spec file on disk, every agent can read it fresh instead of relying on copy-pasted prompt fragments that drift.

The bad version tells the reader what the article is about. The good version tells them the answer. They'll keep reading to see why.

Writing Explanatory Prose

When you need to explain how something works between code blocks, show the mechanism. Don't describe it abstractly.

Bad (abstract narration):

The system uses a layered approach to handle data storage efficiently. Each layer provides a different level of abstraction, allowing consumers to interact with data at the appropriate granularity for their use case.

Good (shows the mechanism):

RowStore wraps CellStore, which wraps YKeyValueLww. Each layer adds one thing: YKeyValueLww handles conflict resolution, CellStore parses cell keys into row/column pairs, and RowStore maintains an in-memory index for O(1) lookups. The consumer only sees RowStore.

The first version could describe anything. The second version could only describe this system.

Constraints

Bullet lists and numbered lists: max 1-2 of each per article. If you need more, convert to prose or a table.

Section headings: use sparingly. Not every paragraph needs a heading. Let content flow naturally between ideas using bridge sentences (see writing-voice "Connect ideas without headers").

Bold text: avoid in body content. Use sparingly if needed for emphasis.

No space-dash-space: use colons, semicolons, or em dashes per writing-voice.

No rigid template: structure should fit the content, not the other way around. Some articles need a "Problem/Solution" flow; others just show code and explain. Don't force sections.

Articles Have Different Shapes

Don't follow a single template. Structure should fit the content. Some common shapes:

Problem → fix (short, practical): State the problem, show the code that fixes it, explain why. No diagrams, no tables, no extra sections. 30-50 lines.

Mechanism explainer (medium): Explain how something works under the hood, alternating prose and code. Diagrams when the flow is non-obvious. 50-80 lines.

Comparison or tradeoff analysis (longer): Show two or more approaches with real code from each. A table can help here because the comparison IS the point. ASCII diagrams when architecture differs between approaches. 80-150 lines.

The shape emerges from the content. If you find yourself adding a diagram or table to fill a perceived gap, you don't need it.

What Makes Articles Good

  • Real code from real codebases, not abstract examples
  • Tight prose that explains WHY, not WHAT (code shows WHAT)
  • Prose and visuals alternate naturally; neither dominates for long stretches
  • Opening paragraph delivers the insight, not a topic announcement
  • Visual elements (diagrams, tables) only when they earn their place
  • Length matches content: 30-50 lines for focused fixes, 80-150 for comparisons

What Makes Articles Bad

  • Rigid section structure that doesn't fit the content
  • Multiple bullet lists and numbered lists throughout
  • Abstract foo/bar code examples
  • Over-explaining self-explanatory code
  • Bold formatting scattered through body text
  • Numbered bold headings for multi-part arguments (**1. Bold heading** pattern)
  • Summary tables tacked on at the end that restate what the prose already said
  • Marketing language or AI giveaways
  • More than 4-5 sentences of prose without a visual break
  • Opening that announces the topic instead of delivering the insight
  • Closing that reaches for a grand summary or call to action

Narrative Mode (Rare)

Most "journey to an insight" articles still work better as punchy. Use narrative only when the discovery process itself is the insight and can't be compressed.

When narrative fits: specific details ("750 lines", not "a large file"), direct statements over manufactured drama, build to an insight rather than starting with it.

Closings

End with a plain statement of the implication. Don't reach for a grand summary or a clever sign-off. If the article showed that X solves Y, just say what that means for the reader in one or two sentences. Avoid closing with superlatives ("the most elegant", "truly powerful") or calls to action ("try it today!").

平台分布

Codex

35.84%
按下载量换算168

Claude

28.08%
按下载量换算132

Cursor

16.85%
按下载量换算79

Gemini CLI

9.5%
按下载量换算45

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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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