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ljg-word

An AI agent skill from lijigang/ljg-skills, scored 57 out of 100 by code.

57/ 100C

Code score by group

  • Spec20/20
  • Description10/20
  • Procedure0/15
  • Done criteria0/15
  • Resources10/10
  • Scripts5/8
  • Safety7/7
  • Upkeep5/5

It loses the most points in Procedure (15), the group that checks whether the steps are numbered, read the context first and say what to do when something fails.

What the author says it does

Deep-dive English word mastery tool. Deconstructs a single English word into core semantics and epiphany. Use when user asks to explain/master a specific English word.

This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.

Repository
lijigang/ljg-skills
Folder
skills/ljg-word
License
MIT
GitHub stars
7,736
SKILL.md lines
42
Other files
0

Add ljg-word to your agent

npx skills add lijigang/ljg-skills --skill ljg-word

The command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored.

More skills from lijigang/ljg-skills

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  2. ljg-card

    Content caster (铸). Transforms text into PNG through precise HTML typography and, when the mold needs it, generated raster imagery. Four molds: -l (default) long reading card, -f…

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  3. ljg-book

    Explain a whole book to readers without specialist knowledge: what it follows, how its main threads connect, where it ends, and how its evidence or events change understanding. Ke…

    69C
  4. ljg-constraint

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    69C
  5. ljg-qa

    信息提问机。给一篇文章/论文/书，把核心观点抽成 Q-A 对——Question 切要害，不教科书；Answer 简洁清晰，有形式化收口，逻辑链完整。读者顺 Q 链走过，每个 A 砸下一枚钉子，复现作者整套推理。Use when user says '问答', 'Q&A', 'QA', '提问', '抽取问题', '/ljg-qa', or shares…

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  7. ljg-explain

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    66C
  8. ljg-roundtable

    一个议题，一场圆桌：主持人请来 3-5 位真实人物，定义开场，逐轮交锋， 每轮收一张 ASCII 结构图，用户用指令控节奏（可/止/深入此节/引入新人物）， 散场后全文存入 Markdown 笔记。Use when user says "圆桌讨论", "圆桌", "roundtable", "辩论", or wants to explore a topic…

    64C

All skills in lijigang/ljg-skills

Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.