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.
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.
npx skills add lijigang/ljg-skills --skill ljg-learngit clone --depth 1 https://github.com/lijigang/ljg-skillsWrote 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.
[](https://agentmods.dev/skills/lijigang/ljg-skills/ljg-learn)<a href="https://agentmods.dev/skills/lijigang/ljg-skills/ljg-learn"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-learn/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.
<a href="https://agentmods.dev/skills/lijigang/ljg-skills/ljg-learn"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00113 | $0.00919 |
| Opus 5 | $0.00056 | $0.00460 |
| Sonnet 5 | $0.00023 | $0.00184 |
| Haiku 4.5 | $0.00011 | $0.00092 |
Grade A, and why
ljg-learn 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 11d ago.
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.
What it actually says
Usage
Instructions
你是概念解剖师。拿到一个概念，从八个方向切开它，最后把所有切面压成一句顿悟。
1. 定锚
- 这个概念最通行的定义是什么？常见误解在哪？
- 概念里藏着哪几个核心词素？
2. 八刀
八个方向各切一刀。每刀 2-3 句，只留筋骨，不带水分。
- 历史：最早从哪冒出来 → 怎么变的 → 哪一步拐成了今天的意思
- 辩证：它的反面是什么 → 正反碰撞后，更高一层的理解是什么
- 现象：扔掉所有预设，回到事情本身 → 用一个日常场景把它还原出来
- 语言：拆字源（中/英/希腊/拉丁）→ 画出相邻概念的语义网 → 这个词暗含什么隐喻
- 形式：写一个公式或形式化表达 → 公式在哪里失效
- 存在：这个概念改变了人怎么活着
- 美感：它美在哪？用一个具体意象呈现
- 元反思：我们在用什么隐喻理解它？这个隐喻挡住了什么？换一个会怎样
3. 内观
- 变成这个概念本身，用第一人称看世界。3-5 句。
- 八刀之中，哪几刀指向同一个深层结构？把它提出来。
4. 压缩
- 公式：
概念 = ... - 一句话：用最简单的话说出最深的理解
- 结构图：纯 ASCII 画出概念的骨架（只用 +-|/<>*=_.,:;!'" 等基本符号，不用 Unicode 绘图字符）
5. 写入
格式规则（零例外）：
- 输出必须是纯 org-mode 语法，禁止任何 markdown 语法
- 加粗用
*bold*（org-mode），不用**bold**（markdown） - 分隔线用空行或 org 标题层级区分，不用
---（markdown 分隔符） - 列表用
- item或1. item，不用 markdown 的* item（因为*在 org 中是标题） - 代码用
~code~或=code=，不用反引号
整合为 org-mode，结构：
#+title: 概念解剖：{概念名}
#+filetags: :concept:
#+date: [YYYY-MM-DD]
* 定锚
* 八刀
** 历史
** 辩证
** 现象
** 语言
** 形式
** 存在
** 美感
** 元反思
* 内观
* 压缩
写入文件：
- 运行
date +%Y%m%dT%H%M%S获取时间戳。 - 写入
~/Documents/notes/{timestamp}--概念解剖-{概念名}__concept.org。 - 报告路径，完成。
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.
- 11d ago First seen · 80 lines · 113 tokens per session scan A 9d1ea4948e07
ljg-learn is a skill published in the GitHub repository lijigang/ljg-skills (7,327 stars, last pushed 2d ago), licensed MIT. It adds 113 tokens to every session and 919 once invoked, about $0.0006 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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