ljg-plain▌
lijigang/ljg-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
让人 grok。
ljg-plain: 白
让人 grok。
不规定怎么写。规定不能怎么写。下限锁死,上限放开。不同主题有不同的最佳写法——类比、故事、问答、递进的例子、一个长场景——由内容决定形式。
格式约束
Org-mode 语法
- 加粗用
*bold*(单星号),禁止**bold** - 标题层级从
*开始,不跳级
ASCII Art
所有图表用纯 ASCII 字符。允许:+ - | / \ > < v ^ * = ~ . : # [ ] ( ) _ , ; ! ' " 和空格。禁止 Unicode 绘图符号。
Denote 文件规范
- 时间戳:
date +%Y%m%dT%H%M%S - 可读时间:
date "+%Y-%m-%d %a %H:%M" - 文件名:
{时间戳}--plain-{简短标题}__plain.org - 输出目录:
~/Documents/notes/
Org 文件头
#+title: plain-{简短标题}
#+date: [{YYYY-MM-DD Day HH:MM}]
#+filetags: :plain:atom:
#+identifier: {YYYYMMDDTHHMMSS}
#+source: {URL 或来源描述}
文件写入后报告路径。
红线(每条必须过,顺序即优先级)
- 口语检验 — 最高法则。读出声来,你会这样跟一个聪明的朋友说话吗?不会→改到会。连词不是敌人——"但是""所以"是思维转弯的声音,只砍机械连词("此外""值得注意的是")
- 零术语 — 聪明的 12 岁孩子能复述。专业词必须出现时,先用大白话把意思落地,再顺带提术语名
- 短词优先 — 能用两个字说的不用四个字。「进行分析」→「看」。大词不让你显得聪明,只让人读得累
- 一句一事 — 每句只推进一步。长句拆短
- 具体 — 名词看得见,动词有力气。「有人觉得情况不太好」→「张三说项目要黄了」。形容词能砍就砍
- 开头给理由 — 第一句话让人想读下一句。不铺垫、不背景、不「自古以来」
- 不填充 — 删开场白、拐杖词、夸大象征。每句都在干活
- 信任读者 — 跳过软化、辩解、手把手引导。说一遍够了
- 诚实 — 想不清楚就说想不清楚。"大概 70%" 比"可能"诚实
工具箱(选用,不必全用)
写的时候可以从这里拿工具,没有哪个是必须的:
- 类比 — 找结构对得上的日常经验。好类比承重(去掉它文章塌),多层(挖一层还像),自明(不需要解释类比本身)。动词延伸到新对象时检查中文动宾搭配是否自然
- 好问题 — 找读者的卡点,变成问题。读者被卡住,才想往下读
- 裂缝 — 模型/类比在哪里不够?那个点往往最值钱。不宣布它,让读者自己感到
- 画面 — 闭眼能看到的场景。硬造的画面比没有更糟
- 故事 — 一个具体的人遇到一个具体的问题。读者跟着走
- 反问入链 — 遇到隐含前提,用问题打开,然后回答它
- 骨架图 — 概念涉及空间关系时,嵌入 ASCII 图(
#+begin_example块)
执行
1. 获取内容
URL → WebFetch | 文本 → 直接用 | 文件路径 → Read | 概念 → 直接解释 | 书名/论文名 → WebSearch
2. 写
形式自由。从工具箱里选最适合这个主题的方式,也可以不选——如果有更好的写法,用它。
输出是一篇从第一行流到最后一行的连贯文章。全文只有文件标题,正文无子标题。
禁止:
- 结构标签(
* 类比/* 裂缝等) - 指向写作过程的元评论(「打个比方」「接下来我们讨论」)
3. 过红线
逐条扫红线清单。额外检查:
- 破公式——否定式排比全文不超过两处,三段式改两项或四项
- 变节奏——长短句交替,段落结尾多样
- 杀金句——听起来像可引用的,重写
- 查跳跃——每步逻辑可追?前句说 A,后句跳到 B→补桥
- 查译感——动宾搭配中文天然吗?不自然→换动词或换句式
扫完列修改清单(哪句触发什么,改前→改后)。清单不写入文件。
4. 生成 Org 文件
按 Denote 规范获取时间戳,写出文件头 + 正文,存入 ~/Documents/notes/。
验收
- Grok:读完能用自己的话复述核心
- 零术语:12 岁孩子能跟上
- 记得住:读完脑子里留下了什么——一个画面、一个问题、一个转折,什么都行
- 想读完:从头到尾没有想跳过的段落
How to use ljg-plain on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ljg-plain
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ljg-plain from GitHub repository lijigang/ljg-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ljg-plain. Access the skill through slash commands (e.g., /ljg-plain) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★74 reviews- ★★★★★Ama Shah· Dec 20, 2024
We added ljg-plain from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Arya Jain· Dec 16, 2024
I recommend ljg-plain for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Arjun Zhang· Dec 16, 2024
ljg-plain reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Liam Chawla· Dec 4, 2024
ljg-plain reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Arjun Choi· Dec 4, 2024
ljg-plain fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mei Ramirez· Nov 23, 2024
ljg-plain has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Arjun Robinson· Nov 23, 2024
We added ljg-plain from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Layla Yang· Nov 15, 2024
Useful defaults in ljg-plain — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Emma Jackson· Nov 11, 2024
ljg-plain fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mei Choi· Nov 7, 2024
Solid pick for teams standardizing on skills: ljg-plain is focused, and the summary matches what you get after install.
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