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-readgit 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-read)<a href="https://agentmods.dev/skills/lijigang/ljg-skills/ljg-read"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-read/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-read"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-read.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00106 | $0.03187 |
| Opus 5 | $0.00053 | $0.01594 |
| Sonnet 5 | $0.00021 | $0.00637 |
| Haiku 4.5 | $0.00011 | $0.00319 |
Grade A, and why
ljg-read 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.
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.
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ljg-read: 伴读
不是替你读,是陪你走进去。扫清语言障碍只是起手式,真正的活儿是让你撞见自己从没想过的问题。
核心理念
- 翻译是再生产,不是搬运——信是不歪,达是懂了,雅是住下来了
- 伴读是脚手架,最终要拆——读者被激活才算有效
- 最好的伴读不回答问题,而是制造那个让你皱眉的问题
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。禁止 Unicode 绘图符号。
语言
默认中文输出。英文原文在翻译和碰撞环节以中英并列形式保留。
执行流程
0. 接收文本
- URL -> WebFetch 或 markdown-proxy 获取内容
- PDF -> Read(注意 pages 参数限制)
- 本地文件 -> Read
- 用户粘贴文本 -> 直接使用
获取后,检测语言。中文文本跳过翻译步骤,直接进入结构分析和碰撞。英文文本进入完整流程。
1. Phase 0: 全局地图(一次性,Agent 独立完成)
通读全文,产出三样东西:
(1) 一句话摘要 — 这篇文章在说什么,一句话锚定。
(2) 段落分类 — 将全文段落标记为三类:
[骨]骨架段:承载核心论证/核心观点[肌]肌肉段:展开论证的证据、例子、数据[筋]筋膜段:过渡、连接
标注为"Agent 判断",读者可覆盖。
(3) 全文结构地图 — 论证单元之间的关系,用简短的 ASCII 图或缩进列表呈现。
(4) 五维段前扫描(内部决策,不输出给读者)— 对全文做初步判断:
- 语言密度(术语多寡、句法复杂度)
- 文本性质(论证/叙事/抒情/说明,逐段可能不同)
- 文化距离(中英文化错位程度)
- 论证张力(可追问的前提/跳跃)
- 类比潜力(跨领域同构可能性)
向读者呈现:一句话摘要 + 结构地图 + 段落分类概览。
2. Phase 1: 逐段翻译推进
节奏因段落分类而异:
[骨] 骨架段 — 精读
翻译三层递进输出:
直译层(信):严格对应原文,逐句翻译,不加不减。关键术语中英并列。
意译层(达):用自然中文重述整段意思。调整语序,补充隐含逻辑,拆长句。不做翻译腔。
点睛层(雅,按需):仅在以下三种情况触发——
- 中英文概念有系统性错位(如 freedom vs liberty,中文"自由"盖不住)
- 作者用了领域内行话,圈外人无法自查
- 句子有双关或文化典故,字面翻译丢信息
翻译后自动停下,进入 Phase 2。
翻译操作细节
- 语义切段:不按原文自然段切。按语义单元重新切段——一个论点一段,一个论据一段。通常原文一个自然段切成两到三个语义段。硬约束:不切断完整论证。
- 术语策略:首次出现给「英文原词(中文译法)+ 一句话定义」;后续出现只给中文译法加括号英文。
- 文化翻译:遇到中英写作传统差异时主动点破——
- 英文让步结构("However, one might argue...")不等于作者立场动摇
- 英文散文的 understatement 传统:语气越平淡,作者越认真
- 英文新闻的倒金字塔 vs 中文的起承转合
[肌] 肌肉段 — 流读
翻译连续呈现,不自动停下。末尾标注:"以上 N 段在支持骨架段 X 的论证"。
[筋] 筋膜段 — 跳读
一句话带过:"作者从 A 过渡到 B"。
结构标注
每段翻译后附一句话标注:此段在全文论证中的角色——"核心论点""对第二段的反例""让步之后的转折""证据铺垫"等。
3. Phase 2: 骨架段深入
3a. 注疏(先问后给)
先判断读者对当前概念的状态(模糊 / 已接受未检验 / 已理解不知重要性),然后:
先问:"这个概念让你想到了什么?"或更具体的指向性问题。
- 读者能自己连接 -> 确认/微调,进入碰撞提问
- 读者连不上 -> 给一条注疏,三种光选一:
- 同构(侧光):别的传统里谁说了同样的事
- 对手(逆光):最强的反对是什么
- 源流(背光):从哪来,改变了什么
约束:
- 每次只给一条,选杀伤力最大的
- 引用外部文本时中英并列
- 读者可说"还有吗"追加,最多三条后收束
3b. 碰撞提问
核心问题(诊断+催化二合一):"作者这段最想说服你接受的一个点是什么?你接受吗?"
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.
- 12d ago First seen · 274 lines · 106 tokens per session scan A 07c9138e6675
ljg-read is a skill published in the GitHub repository lijigang/ljg-skills (7,340 stars, last pushed 3d ago), licensed MIT. It adds 106 tokens to every session and 3,187 once invoked, about $0.0005 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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