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-rankgit 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-rank)<a href="https://agentmods.dev/skills/lijigang/ljg-skills/ljg-rank"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-rank/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-rank"><img src="https://agentmods.dev/badge/skills/lijigang/ljg-skills/ljg-rank.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 | $0.00101 | $0.07609 |
| Opus 5 | $0.00051 | $0.03805 |
| Sonnet 5 | $0.00020 | $0.01522 |
| Haiku 4.5 | $0.00010 | $0.00761 |
Grade A, and why
ljg-rank 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 13d 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 — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
降秩引擎
输入一个领域,输出它的秩。
秩是什么
秩不是"关键要素",不是"核心原则",不是"总结要点"。
秩是这么个东西:这个领域里真正独立的生成器,究竟有几根?拿这几根,能不能把全部现象一个个倒回来?能,才算找到。
但能倒回来只是底线。Deutsch 在《无穷的开始》里立的尺——好解释要过两关:
- 解释力(reach)——不光能推出清单里的现象,还能推出清单外的,而且现实里真验得上。
- 难以变更(hard to vary)——每根生成器、每个细节,都是被现象逼出来的。动一处,预测就崩。
坏解释能用十种说法糊过去——它根本不是在解释,是用模糊把面铺得很广。好解释只有这一种说法能把所有现象对上——动一根就塌。
找秩,找的就是这种"动一根就塌"的好解释。
怎么找:先抬头,再往下挖
挖之前,先抬头看一眼这个领域立在什么上面——它的基本假设是哪几条?
0. 先看基本假设(往上看,看到天花板)
一套理论,按亚里士多德的规矩,总要有几条不证自明的起点。基督教就立在三条上:
- 《圣经》是真的,一切思考从这里起。
- 上帝唯一,确实存在。
- 人是上帝造的,上帝爱人。
这三条不许问"为什么"——它们靠信,不靠证。动摇任何一条,整套教义就塌。
每个领域都有这种东西。物理学里是"自然律在时空中稳定"——你没法用实验去证,实验本身就预设了它。经济学里是"人会按自己的偏好排序选择"——这不是观察出来的结论,是入场的门票。哲学里是"语言能指称世界"——分析哲学整个家都建在这块地基上。
降秩之前,先把这几条挖出来,明明白白写在纸上。
操作问句两条,对着领域问:
- 这里什么是不许问的?(一问就被当外行,或者被当冒犯)
- 什么是靠相信才成立的?(没有它,后面所有论证都失去支点)
答出来的几条,就是这个领域的基本假设。
假设和 rank,一句话分清:
- 基本假设是天花板——往上不可追,信而立。
- root rank 是地板——一层一层往下追,追到追不动,挖而见。
两个方向,一上一下。地板没找到,鬼打墙;天花板没看清,容易把信仰当真理说出去——后者更危险,因为听上去也像真理。
写进文章里的方式:开头一两句,把基本假设交代清楚。不展开论证,不解释"为什么是这几条"——它们的位置就是不证。然后才开始往下挖 rank。
1-7. 往下挖:穿透工序
判据是事后才能验的事,挖 rank 的力气全在过程里。判据再严,也只能挑出伪根——前提是工序走到了底。
七步,心里走完,不写进文章:
-
铺现象——领域里 10+ 个有代表性的现象,光铺,不解释。要的是给 rank 留一份可反生成的检验靶。
-
列候选——每个现象问一句"为什么会这样",候选生成器全贴出来。这一步必然杂,伪根混在里头。
-
递归下沉——这是穿透的命门,也是最容易做错的一步。错法是想一铲子挖到底:从现象直接猜"最根本的是什么"。站在现象层看不见地基,只看得见脚下这一层,猜出来的底十有八九悬空。
对的做法是一层一层沉下去。先只找第一层生成器——现象底下紧挨着的、看得见够得着的那层。找齐了,把这一层当新地面,人站上去。 站稳了再问一遍"这层底下还有没有更基本的"——这时候第二层才露出来,它从现象层根本无从发问。第二层找齐,再当新地面,再站上去,再往下看。一级一级递归,直到站上某一层,底下再问不出更基本的——那层就是 root rank。
这正是黑箱往下开一层的笨功夫:当前这层是能监测的全部,看清了、站稳了,下一层才打开。想跳级是贪快,跳一层,root rank 就虚一层。
比如降秩"创业",第一铲挖出愿景、执行、团队、市场——看着都对,其实只是表面的"关键要素",停在这儿就是没下沉。把这四个当新地面站上去,再问:愿景从哪来?执行靠什么撑?这才下到第二层。一层层站到追不动,才摸到 root rank。
-
合并同源——两根候选其实是同一个生成器在两面上露脸,合掉。判别一句话:去掉一个,另一个还会不会自己冒出来?
-
砍——拿掉一根,剩下的能不能反生成全部现象?砍得掉的扔,砍不掉的留。这一步把判据里的"最小性"落到地。
-
反生成——剩下的几根,从头把现象清单走一遍,看能不能逐条复现。复现不了的那条,指向少了一根。
-
预测 + 变更双测——两道闸一起过:
- 预测清单外(reach):这几根能不能推出清单里没有的现象?推出来的,现实里真有吗?
- 变更测试(hard to vary):随便挑一根,改一个细节——换个条件、换个方向、换个强度。预测还对吗?还对,这根是松的伪根,可以换;改一处就垮,这根才是紧的真根,动不得。Deutsch 的原话:你能改的,都不是它的内核。
What ships with it
1 file 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.
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.
- 13d ago First seen · 485 lines · 101 tokens per session scan A e1041f26c0d4
ljg-rank is a skill published in the GitHub repository lijigang/ljg-skills (7,340 stars, last pushed 4d ago), licensed MIT. It adds 101 tokens to every session and 7,609 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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