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 hawkongz/concept-fable --skill zh-cngit clone --depth 1 https://github.com/hawkongz/concept-fableWrote 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/hawkongz/concept-fable/zh-cn)<a href="https://agentmods.dev/skills/hawkongz/concept-fable/zh-cn"><img src="https://agentmods.dev/badge/skills/hawkongz/concept-fable/zh-cn/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/hawkongz/concept-fable/zh-cn"><img src="https://agentmods.dev/badge/skills/hawkongz/concept-fable/zh-cn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00059 | $0.04195 |
| Opus 5 | $0.00030 | $0.02098 |
| Sonnet 5 | $0.00012 | $0.00839 |
| Haiku 4.5 | $0.00006 | $0.00419 |
Grade A, and why
concept-fable 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 10d 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
寓言故事概念解释器
Overview
当用户想理解一个抽象概念时，不直接抛出教科书定义，而是创作一篇精心设计的寓言故事。读者沉浸其中，在临近结尾时才恍然大悟，然后附上专业释义。
核心原则：用故事让人"悟到"，而不是用定义让人"记住"。
Workflow
按以下 8 步执行：
Step 1 — 理解概念
- 如果概念已充分掌握，直接进入下一步
- 如果概念有模糊之处，搜索或查阅权威资料
- 提炼出 2-3 个核心要素：关键矛盾、核心机制、为什么重要
- 写出概念的因果链——一步步拆解概念的实际运作机制：什么触发了什么、按什么顺序、导致什么结果。这是情节蓝图：故事必须在情节层面复现这个因果序列，而非只蹭主题
核心要素与隐喻点的关系：每个核心要素必须有一个对应的隐喻点来映射它（这是底线）。额外的隐喻点（共 3-5 个，含 ≥2 个核心映射）用于丰富故事细节，但不能喧宾夺主。简单来说：核心要素 = 必须映射的（≥2 个），隐喻点总量 3-5 个。
Step 2 — 确认范围与场景（按需）
以下情况需向用户确认：
- 概念有多个分支或流派（如「一致性」在不同语境含义不同）
- 概念在不同领域有不同解读
- 用户表述模糊，可能有多种理解
确认概念理解：「这个概念我理解为 ______，核心要点是 ______，对吗？」
确认场景偏好——先判断概念本身，再决定是否问用户：
- 日常场景就能精准映射核心流程 → 不打扰用户，直接用日常场景
- 概念抽象、离日常经验较远 → 问一句：「这个概念比较抽象，你平时做什么工作、学什么专业？我挑一个你熟悉的场景来讲，更容易懂。」
- 概念虽日常，但用户的专业能提供更精准的隐喻 → 同上，简短问一句
Step 3 — 选择故事类型与语气
故事类型——根据概念的核心特征选择：
- 概念涉及两方博弈/冲突 → 古代寓言
- 概念是渐进累积的过程 → 日常生活
- 概念是系统行为/涌现现象 → 自然现象或机械装置
- 概念的核心是"表面 vs 实际"的反差 → 角色对话或说明书场景
- 概念的本质是"为了解决某个问题而存在"（工具/方案型，如 Node.js、缓存、依赖注入）→ 对比式结构：先展示「没有它」时的困境，再展示「有它」后的变化
选择标准：
- 故事的「冲突」或「转折」必须精准映射概念的核心矛盾
- 2-3 个角色，一个核心情节线
叙事语气——根据概念的情感色彩选择：
| 概念的情感色彩 | 推荐语气 | 示例场景 |
|---|---|---|
| 告诫/警示（如技术债务、死锁） | 沉稳、略带惋惜 | 说书人风格，娓娓道来 |
| 揭示/反直觉（如抽象泄漏、幸存者偏差） | 轻快、有反转感 | 像在讲一个"你猜怎么着"的故事 |
| 权衡/两难（如 CAP 定理） | 中立、不偏袒 | 平实叙述，让读者自己判断 |
| 机制/原理（如依赖注入） | 日常、接地气 | 生活化口吻，拉近距离 |
简单概念豁免：对于「什么是变量」「什么是循环」等基础概念，默认用日常接地气语气，不必逐条对照上表。表格主要用于需要情感引导的复杂/抽象概念。
Step 4 — 验证隐喻映射（写作前必做）
动笔之前，验证所选故事类型的冲突结构是否能忠实复现 Step 1 中写下的因果链：
- 列出计划的隐喻点：列出 3-5 个计划使用的隐喻点，逐一标明映射到因果链的哪一环
- 因果链覆盖检查：因果链的每一环都必须有对应的隐喻点。如果有哪一环没有映射 → 当前故事结构不适合这个概念，回到 Step 3 重选
- 机制 vs 主题检查：故事的冲突映射的是实际的因果机制，还是只是结果的主题标签？「双方僵住了」是主题；「双方各自占着对方需要的东西，谁都不放手」才是机制。如果只映射到了主题 → 回到 Step 3 重选
- 全部通过 → 进入 Step 5（写故事）
Step 5 — 写故事
三段式结构：
- 铺垫（60%-70%）：引入角色和日常场景，看似与概念无关。情节自然展开，悄然埋入与概念的「神似」之处。让读者沉浸于故事，察觉不到这是在上课。
- 矛盾显现（20%-30%）：故事出现转折、冲突或困境，精准映射概念要解决的核心问题。敏锐的读者开始有所察觉。
- 揭晓（10%）：用「这就是我们今天要讲的概念——【概念名】」收束，一句话点破对应关系，点到为止。
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.
- 10d ago First seen · 229 lines · 59 tokens per session scan A 920f62dbc923
concept-fable is a skill published in the GitHub repository hawkongz/concept-fable (5 stars, last pushed 3mo ago), licensed MIT. It adds 59 tokens to every session and 4,195 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
reading-metaskill
A reading and learning guide based on building a regular reading habit, choosing books, and understanding difficult subjects through original works and explanation.
act-as-a-personal-tutor
Transform any AI assistant into an interactive, step-by-step personal tutor that assesses prior knowledge, explains concepts in bite-sized chunks, and quizzes comprehension before advancing.
rapid-domain-mastery
Builds field maps from multi-source academic or technical corpora: expert mental models, debates, assumptions, prerequisites, oral-exam questions, and sprint study plans. Use for exam prep, research onboarding, or rapid topic mastery from multiple sources. Not for plain summaries or single short docs.
cangjie-skill
A process for turning a book, course, podcast, interview, long video, or other long material into reusable instructions for an AI agent. It extracts methods and principles, checks them, and packages them as skills.
naval-almanack
A reference guide for applying ideas from The Almanack of Naval Ravikant, a book about wealth, work, happiness, judgment, and long-term thinking. It routes questions to the relevant topic guidance and notes when professional help is needed.
judgment-training
A thinking and decision-making guide based on basic reasoning, long-term consequences, and reusable mental models. Mental models are simple ways to understand recurring patterns in decisions.