Non Fiction Revision
jwynia/agent-skills
Diagnose and guide revisions in non-fiction books. An agent skill from jwynia/agent-skills.
Finds an apt analogy or relational metaphor for a scene, or for the difference between two things, based on structure-mapping theory, and states where the comparison breaks.
SKILL.md written in Chinese; this summary is our English description.
Install Analogy Finder
$ npx skills add lijigang/ljg-skills --skill ljg-analogy -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lijigang/ljg-skills ljg-analogy --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ljg-analogy .claude/skills/ljg-analogy && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ljg-analogy" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-analogy into .claude/skills/ljg-analogy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-analogy", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-analogyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add lijigang/ljg-skills --skill ljg-analogy -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lijigang/ljg-skills ljg-analogy --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ljg-analogy .agents/skills/ljg-analogy && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ljg-analogy" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-analogy into .agents/skills/ljg-analogy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-analogy", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lijigang/ljg-skills --skill ljg-analogy -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lijigang/ljg-skills ljg-analogy --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ljg-analogy .cursor/skills/ljg-analogy && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ljg-analogy" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-analogy into .cursor/skills/ljg-analogy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-analogy", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/lijigang/ljg-skills.git --path skills/ljg-analogy--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add lijigang/ljg-skills --skill ljg-analogy -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lijigang/ljg-skills ljg-analogy --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ljg-analogy .gemini/skills/ljg-analogy && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ljg-analogy" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-analogy into .gemini/skills/ljg-analogy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-analogy", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install lijigang/ljg-skills ljg-analogyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add lijigang/ljg-skills --skill ljg-analogy -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ljg-analogy .github/skills/ljg-analogy && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ljg-analogy" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-analogy into .github/skills/ljg-analogy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-analogy", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lijigang/ljg-skills --skill ljg-analogy -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lijigang/ljg-skills ljg-analogy --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ljg-analogy .opencode/skills/ljg-analogy && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ljg-analogy" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-analogy into .opencode/skills/ljg-analogy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-analogy", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ljg-analogy- GitHub stars
- 7.5k
- Token cost
- ~850 tokens
- SKILL.md length
- 73 words
- Files
- 4
- Skills in repo
- 26
- Repo updated
- First seen
- Licence
- MIT
At a glance
Finds an apt analogy or relational metaphor for a scene, or for the difference between two things, based on structure-mapping theory, and states where the comparison breaks.
- Explaining a hard idea by borrowing a familiar scene
- SKILL.md covers Workflow Routing, Gotchas, 输入是否够用 and 什么样的类比值得交付, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Showing how two products, tools or mechanisms differ through a shared set of roles
What it does
Given a scene, the skill delivers one main analogy that lets the reader first see a picture, then recognize the relation, and finally understand the original scene a little better. The scene the user wants to understand is the target domain and the borrowed one is the base domain; the search starts from the target to find a base, and knowledge carries back from the base to the target. The objects can be far apart as long as their relations match closely, and novelty never excuses distortion.
Requests are routed by type: one scene needing a metaphor, a comparison of two objects or mechanisms through a shared relation framework, constrained requests on audience or style, and comparing several candidates. A deliverable analogy fits the reader's current task, keeps a real relational structure rather than a list of shared traits, stands as a concrete picture, reveals at least one previously hidden relation such as a blind spot, constraint or cost, survives comparison with alternatives and names a specific boundary where the mapping fails.
Gotchas warn against vague mappings such as both being complex, against inventing facts about the target domain, and against treating an analogy as proof. Output is delivered in the conversation by default, with no notes or files created, and written for ordinary Chinese readers unless you say otherwise. When the input is a single word with no recognizable situation, the agent asks one question that would change the choice. The folder includes Theory.md and EvalCases.json. The skill text is written in Chinese.
When your agent uses it
- Explaining a hard idea by borrowing a familiar scene
- Showing how two products, tools or mechanisms differ through a shared set of roles
- Needing a metaphor that carries a real relationship, not just a similar surface
Example prompts
- “Find an analogy that explains how a message queue differs from a database to a non-technical reader.”
- “Give me a one-line metaphor for how technical debt compounds.”
- “Explain the difference between OAuth and API keys with an analogy, and say where it breaks.”
What it can do on your machine
Read from SKILL.md and the folder at commit 9e75497. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Analogy Finder loads about 850 tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 73 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from lijigang/ljg-skills at commit 9e75497, republished under its MIT licence (© lijigang). 73 words, ~850 tokens.
Download SKILL.mdSave it as
.claude/skills/ljg-analogy/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- ljg-analogy
- description
- 基于结构映射理论，为场景或两个对象的差异寻找精妙类比与关系隐喻，保留关键关系、解释增量与迁移边界。USE WHEN 用户说「找个类比」「打个比方」「有什么精妙的隐喻」「用类比讲清两者区别」「换个场景理解」或调用 ljg-analogy。NOT FOR 只解释现成比喻、纯修辞润色、理论定义或把类比当作事实证明。
- metadata.version
- 1.1.0
ljg-analogy：让关系在另一个场景里显形
用户给出一个场景，交付一个让人先看见画面、再认出关系、最后对原场景多懂一点的类比。物件可以相距很远，关系却对得很近。新奇不能抵消失真；熟悉的物件也能承载出人意料的关系。
术语方向固定：用户想理解的场景是目标域；借来帮助理解的场景是基域。 搜索从目标域出发寻找基域，知识迁移从基域回到目标域。用户用词相反时按实际意图工作，不为术语打断创作。
Workflow Routing
| 请求 | 处理 |
|---|---|
| 给一个场景，寻找类比或隐喻 | 按下文的成品标准交付一个主类比 |
| 比较两个对象、产品或机制 | 用共同的关系框架，同时保留共同点与关键差异 |
| 指定受众、领域、文风或只要一句 | 保留结构约束，服从用户的呈现要求 |
| 要多个方案或比较已有候选 | 比较不同关系焦点，点出推荐项与取舍 |
Gotchas
- 「都很复杂」「都是网络」「都需要平衡」适用面太宽，不能单独支撑类比。关系要说清谁对谁做了什么，哪些条件限制它，怎样产生结果或形成布局。
- 多个对象配对只是字典。优先保留受同一个因果、约束、比例或空间结构统辖的关系，不能以共同点数量充当系统性。
- 「更高阶」指关系之间的关系，不等于更抽象、更宏大，也不限定为因果链。比例、空间和情感关系同样可以承重。
- 一套映射中角色要稳定，不能为了下一句方便，让同一对象偷偷换身份；确需比较不同侧面时，明确换了一套映射。表面属性不随关系自动迁移。
- 基域是找来的，目标域事实是用户给的。不能为了让比方成立，替目标域补出动机、因果或结局；缺项只能作为明确的假设。
- 类比带来候选推论，不能证明目标域必然如此。尤其不要从自然过程推出社会行为的必然性、正当性或某个人的意图。
输入是否够用
至少能认出场景里发生了什么，以及用户希望照亮的关系。只有一个词而无可辨认情境时，问一个能改变类比选择的问题；词语有多种常见指向时，先确认它指什么，问题中的选项也不能默认某一种含义。已有可用场景时直接工作。未指定受众，默认面向普通中文读者，优先选少解释也能想象的生活场景。
类比对准读者当前要完成的理解任务：辨认什么、区分什么，或预测哪种变化。用途、组成、运作方式与体验是不同的解释焦点；读者问两者怎样不同，分别概括它们的用途可能答非所问。结合提问与已有上下文选焦点，必要时用一句「这里着重看……」交代，不把这项检查变成每次都向用户补问。
用户强调的目标、权力差异、时序和约束不能在抽象时被抹掉。版本与状态也是对象身份的一部分：新证据改变角色、流程或依赖关系时，重新检查取景与主类比。若原候选已不能保留承重关系，就换掉它，不能只在末尾补一句版本差异。
什么样的类比值得交付
任务吻合。 映射保留的是读者这次需要理解的关系。一个比方能解释对象的某个特点，仍可能与眼前的问题无关；关键看它能否改善当前的辨认、区分或理解。
关系成立。 两边都存在同一条有信息量的关系骨架。读者能把人物或物件逐一放回各自角色，核对作用方向、先后顺序、依赖条件与反馈。允许只解释局部，但不能用一句泛泛的「只是类比」掩盖承重关系相反。
关系连得起来。 有一组相互支持的对应，而非为了凑数收集的相似点。若一处改变会牵动另一处，说明这条依赖怎样在两边重现。简单的比例或空间对应已经足够时，不必硬加因果或凑满关系数量。
画面能独立站住。 基域里有具体对象和动作，普通读者不用先听一遍新理论就能理解。可以用明确标为「设想」的构造场景；不能编自然机制、历史事件或名人故事当事实。
多照出一点。 类比至少让一项原先不显眼的关系变得可见：盲点、约束、代价、后果或可区分两种判断的条件。这个增量要能从映射推出。验收解释性类比时，用一个未直接照抄原描述的小情境，检查它能否帮助读者辨认差异、推测结果或选择下一步；只能复述名称与特点的候选仍然偏弱。这是内部内容检查，不必每次给用户出题，也不等于读者实测。文学隐喻可以改变读者看待一种关系的方式，不强加操作性检验或科学实验。
候选经得起比较。 当不同候选都能说通时，用同一个理解任务比较它们：谁保留了更关键的关系，谁能支持更具体的区分或推论，谁需要额外解释得更少。只有喻体名词不同、关系骨架相同，不算另一种结构。关系可信与受众可理解是底线；熟悉的近域对应可以优于新奇的远域对应，距离本身不计作质量。候选探索留在内部，用户未要求时只交付最强方案；没有合格候选就说明具体缺口。
边界具体。 指出基域哪个醒目的特征不能带过来，或哪项目标域条件变化后，对应关系就会断开。用与本次类比有关的一句话完成，避免通用免责声明。
比较两个对象时
优先在同一熟悉领域中找一组场景，用同一套角色与关系描述两边，使共同点保持可比、关键差异清楚显现。分别给 A、B 找到贴切形象，仍不足以说明 A 与 B 怎样不同；可借用的是一组有区别的运作关系。若跨两个基域更准确，也应保留共同的比较依据，不强凑同域。
检查两种对应是否有区分力：交换喻体以后，如果不用改变承重关系仍能轻易圆回来，它们可能只命中了共同属性。把一个新输入或条件变化放进两边，读者应能从类比看出各自怎样处理、会出现什么不同。若不同答案全靠另外补写目标域事实，喻体本身就没有提供足够帮助。仅按原文换名词，或把优劣标签塞进比方，也不能替代这项检查。
成品怎样说
默认直接在对话交付，不自动创建笔记、图片或文件。通常用几段话说透一个主类比，篇幅随场景伸缩：
- 先给类比本身。 用一句有画面的中文，让人立即知道拿什么来比什么。把真正承重的关系带进这句话，避免空泛的「X 就像 Y」。
- 让对应运行。 把基域中的具体局面讲明白，再接回用户的场景。复杂对应可以用短表，表里写「关系怎样对应」，不能只填名词对名词。
- 落回新增理解。 说清这个比方让原场景哪一点变得更清楚，以及最容易被误带过去的那一点。不要只换词重述开头。
以上是信息要求，不是固定标题模板。用户只要一句时，只交付一句；用户要可直接引用的成稿时，先给干净成稿，不把验收语言塞进正文。隐喻可以省去「像」，结构对应仍须成立。
理论与事实边界
本技能借用 Gentner（1983）的关系映射与系统性原则。理解任务、双对象对照、候选淘汰、受众熟悉度、认知增量与具体边界，是为创作任务增加的设计标准，不宣称出自论文或已被论文验证。
关于定义、非因果关系、隐喻与理论外推有疑问时，读 Theory.md。维护或试跑时使用 EvalCases.json；这些用例只定义输入和验收依据，不规定唯一的正确比方。
日常构造场景不必联网。最终类比若依赖某项不确定的科学、历史或行业事实，先用当前可用检索工具核对一手材料；核对不了，就换成事实可靠的基域，或明确限定为假设场景。
Examples
- 「收藏了很多文章，真遇到问题却找不出来，给个精妙类比。」成品应照亮存入与取用之间的关系，映射到具体情境，不能只用「知识宝库」赞美数量。
- 「两个工具都能完成任务，一个由我逐项开会话安排，另一个从主会话接收需求并分派、汇报。用类比讲清区别。」成品要保留入口、分流与协调的差异；不把二者泛分成执行工具与资料容器，也不预设唯一的正确喻体。
- 「离家多年后，听见一句乡音就觉得有人在等我。只要一句隐喻。」成品保留声音、归属与关系被唤起的结构，只给一句，不自动加表格或行动清单。
© lijigang, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files in skills/ljg-analogy of lijigang/ljg-skills.
- SKILL.md
- EvalCases.json
- Theory.md
- agents/openai.yaml
Open the folder on GitHubat commit 9e75497
Compare with similar skills
Analogy Finder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analogy Finder this skilllijigang/ljg-skills | 7.5k | — | ~850 | Automated safety check: Pass | MIT | |
| Non Fiction Revisionjwynia/agent-skills | 170 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Story Multi-Perspective Reviewzenstory-ai/oh-story-claudecode | 7.4k | 3 repos | ~3k | Automated safety check: Pass | MIT | |
| Short Web Fiction Trend Scanzenstory-ai/oh-story-claudecode | 7.4k | 2 repos | ~1.2k | Automated safety check: Pass | MIT | |
| InkOS Creative HarnessNarcooo/inkos | 10k | 1 repos | ~1.1k | Automated safety check: Pass | AGPL-3.0 | |
| Novel Arteternityspring/shuohao-skills | 4.3k | — | ~1.1k | Automated safety check: Notes | Apache-2.0 |
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Questions about Analogy Finder
What does Analogy Finder do?
Finds an apt analogy or relational metaphor for a scene, or for the difference between two things, based on structure-mapping theory, and states where the comparison breaks. Given a scene, the skill delivers one main analogy that lets the reader first see a picture, then recognize the relation, and finally understand the original scene a little better. The scene the user wants to understand is the target domain and the borrowed one is the base domain; the search starts from the target to find a base, and knowledge carries back from the base to the target.
When should I use Analogy Finder?
Analogy Finder fits situations like: explaining a hard idea by borrowing a familiar scene; showing how two products, tools or mechanisms differ through a shared set of roles; needing a metaphor that carries a real relationship, not just a similar surface.
How do I install Analogy Finder in Claude Code?
Run `npx skills add lijigang/ljg-skills --skill ljg-analogy -a claude-code`. Or copy the skill folder (skills/ljg-analogy in lijigang/ljg-skills) into .claude/skills/ljg-analogy in your project. Claude Code loads it when a task matches its description.
How do I install Analogy Finder in Codex?
Run `npx skills add lijigang/ljg-skills --skill ljg-analogy -a codex`. Or copy the skill folder (skills/ljg-analogy in lijigang/ljg-skills) into .agents/skills/ljg-analogy in your project. Codex loads it when a task matches its description.
Can I use Analogy Finder in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lijigang/ljg-skills --skill ljg-analogy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ljg-analogy, .gemini/skills/ljg-analogy, .github/skills/ljg-analogy and .opencode/skills/ljg-analogy in your project.
What does Analogy Finder need to run?
SKILL.md names no scripts, command-line tools or credentials: Analogy Finder is instructions for the agent only.
Does Analogy Finder access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Analogy Finder safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Analogy Finder use?
Analogy Finder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Analogy Finder use?
About 850 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Analogy Finder?
Skills that share tags, products or a category with Analogy Finder: Non Fiction Revision (jwynia/agent-skills, 170 stars), Story Multi-Perspective Review (zenstory-ai/oh-story-claudecode, 7.4k stars), Short Web Fiction Trend Scan (zenstory-ai/oh-story-claudecode, 7.4k stars) and InkOS Creative Harness (Narcooo/inkos, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Analogy Finder?
lijigang (a GitHub user) maintains it in lijigang/ljg-skills, which has 7,481 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 8, 2026.
Source: lijigang/ljg-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.