ljg-is

ljg-is is a skill for Claude Code from lijigang/ljg-skills. It costs 140 tokens per session (1,709 once invoked), scanned A, original, MIT.

An explanation method for turning a concept into something a reader can recognize, understand, judge, and use. It covers what the concept is, how it works, how it differs from nearby ideas, and what to do when it appears.

In plain words
What is it for?
Use it to explain a technical concept, role, system, rule, or practice. It produces a connected explanation with examples and practical guidance rather than four disconnected dictionary-style sections.
Why use it?
A short definition may be easy to memorize but still leave the reader unable to spot the concept or apply it in a real situation.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Part of the ljg-skills plugin — 22 skills shipped together

Good fit Use it to explain a technical concept, role, system, rule, or practice. It produces a connected explanation with examples and practical guidance rather than four disconnected dictionary-style sections.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lijigang/ljg-skills/ljg-is
About the project

ljg-skills is a collection of custom Codex skills for tasks such as learning, writing, reading, relationship analysis, image creation, and investment analysis. Codex users install selected skills or the whole collection through a skills command-line interface. The catalogue entries are the collection's individual skills, plugin, and instruction.

lijigang/ljg-skills · 7,340 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add lijigang/ljg-skills --skill ljg-is
Clone the repo
git clone --depth 1 https://github.com/lijigang/ljg-skills

Made for: Claude Code.

Or install ljg-skills, the plugin that ships this one along with the rest of its 22 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ljg-is

README.md
[![agentmods](https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-is/github.svg)](https://agentmods.dev/skills/lijigang/ljg-skills/ljg-is)
Your own site
<a href="https://agentmods.dev/skills/lijigang/ljg-skills/ljg-is"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-is/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ljg-is

Your own site · 80×15
<a href="https://agentmods.dev/skills/lijigang/ljg-skills/ljg-is"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-is.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 140 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,709 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 24 Aug 2026
  • Snyk pass 24 Aug 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00140 $0.01709
Opus 5 $0.00070 $0.00855
Sonnet 5 $0.00028 $0.00342
Haiku 4.5 $0.00014 $0.00171

Measured 12d ago against content hash c3490ae27d2a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ljg-is scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (Tools/ValidateNote.test.ts, Tools/ValidateNote.ts), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ljg-is/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

把一个名词讲到能用

只给定义,读者可能记住了词,却仍不知道怎样认出它。只讲动词,读者又可能知道它做了什么,却不清楚它究竟是哪一类东西、边界在哪里。

ljg-is 把两者接起来:名词回答「它是什么」,让概念在认知地图里站稳;动词回答「它怎样起作用」,让概念在现实里动起来。真正完成的理解还要继续走一步:它改写了读者原来的什么判断,下次遇到它时又能怎样行动。

它是什么,和什么不一样?
它靠什么动作产生结果?
知道这一点后,我不再怎样想?
下次遇到它,我先看什么、怎样做?

这四个问题是一条理解路径,不是四个正文栏目。文章要像一个人顺着疑惑把事情讲明白,而不是把答案依次填进表格。

赵汀阳的「动词」思想在这里仍然重要,但它是一副有条件的透镜。面对制度、平台、角色或规范,可以继续追问谁选择了这种做法,它怎样改变参与者;面对目标函数、递归这类技术概念,动词首先是对象自身的运算与作用。材料没有显示社会反制,就不把技术说明硬拽成制度批判。

Workflow Routing

Workflow Trigger File
UnderstandInUse 把「是什么」与「怎样运作」接成可辨认、可判断、可行动的 Org 解读 Workflows/TraceCreation.md

成品标准

一篇合格解读会让读者发生四个连续变化:

  • 能用一句普通话说出 X 属于什么,以及它与最容易混淆的对象差在哪里。定义要给边界,不能只给比喻或用途。

  • 能顺着一个真实例子说明 X 怎样起作用。技术概念讲清输入、关键动作和结果;制度概念讲清参与者、规则怎样进入行动以及实际后果。只写对象真正具有的机制。

  • 能指出自己原来哪种看法需要修改。认知变化必须由前面的定义和机制推出,不另起炉灶追求「深刻」。

  • 能在一个具体情境里使用这番理解。行动指导要说清先看什么、怎样判断、何时调整,并能解释为什么。

正文从一个普通读者真的会有的疑惑、误解或使用场景进入,不强制编造人物故事。概念要尽早出现,例子负责验证解释,不负责制造戏剧性。

最后可以落在一句判断、一个行动原则或一个真实未决问题上。哪一种自然,由前文决定;问号不是深度证明。

默认写入:

~/Context/{时间戳}--理解-{目标片段}__is.org

新成品使用 ljg-is-v5 schema。正文使用两到四个随内容生出的标题和四到十个自然段,不把「是什么 / 怎样运作 / 认知改变 / 行动指导」直接用作四个栏目。

判断边界

  • 定义与理解不同。 用户若只要形式化定义、公式证明或术语翻译,直接回答即可;只有当他想理解概念如何工作、意味着什么或怎样使用时,才进入本技能。

  • 动词不等于社会创制。 技术对象可以通过计算、映射、比较、压缩或递归起作用,不必虚构一个创造者故事。制度对象才追问选择者、规则与反作用。

  • 行动指导不是操作手册。 本技能给判断抓手,不替代某个软件、流程或行业的逐步教程。

  • 理由不能伪造。 区分材料支持的事实和分析者的推断。不知道就缩小判断,不替人物补写动机,也不把一般机制冒充具体案例事实。

Gotchas

  • 不要把新合同写成新清单。 「定义、机制、启发、建议」若各占一栏,文章仍然生硬。它们应当像一条推理:因为 X 是这样运作的,所以原来的判断不够准确,下一次才应当这样做。

  • 不要只给用途当定义。 「目标函数用来优化」没有说明它是什么;要补上它怎样给候选方案建立可比较关系,读者才可能把它与约束、指标或奖励区分开。

  • 不要强制具体场景。 一个真实疑惑往往比虚构工程师或司机更自然。只有当人物行动确实承载机制时才使用故事。

  • 不要强造赵汀阳式转折。 未选可能、反制、本源和未来不是必填项。它们只有能解释对象,并且有现实根据时才进入正文。

  • 不要在结尾突然升级尺度。 前文讲算法,结尾忽然质问社会正义,通常不是深刻,而是换了问题。认知改变与行动指导必须能够逐句追溯到已经讲清的机制。

  • 不要给万能建议。 「保持关注」「综合考虑」「具体问题具体分析」删除概念名后仍然成立,说明它没有使用前文的理解。

  • 不要让元数据替正文说话。 definitionoperationrecognitionguidance 是后台索引;正文可以用更自然的说法,但全文读回必须找到同一内容。

Examples

目标函数

不要写:目标函数是优化的灵魂,它最终会反过来支配设计者。
可以写:目标函数是一把供求解过程比较方案的尺子。它给每个候选方案一个
可比较的结果,让程序知道往哪边改进。因此看到「最优」时,先补问一句:
这是按照哪把尺子得到的最优?

Read the full file on GitHub · 110 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 110 lines · 140 tokens per session scan A c3490ae27d2a

Subscribe to this mod's changes

ljg-is is a skill published in the GitHub repository lijigang/ljg-skills (7,340 stars, last pushed 3d ago), licensed MIT. It adds 140 tokens to every session and 1,709 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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