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 hwl668/Scientific-learning-skills- --skill text-memorizergit clone --depth 1 https://github.com/hwl668/Scientific-learning-skills-Wrote 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/hwl668/scientific-learning-skills-/text-memorizer)<a href="https://agentmods.dev/skills/hwl668/scientific-learning-skills-/text-memorizer"><img src="https://agentmods.dev/badge/skills/hwl668/scientific-learning-skills-/text-memorizer/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/hwl668/scientific-learning-skills-/text-memorizer"><img src="https://agentmods.dev/badge/skills/hwl668/scientific-learning-skills-/text-memorizer.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 | $0.00094 | $0.01896 |
| Opus 5 | $0.00047 | $0.00948 |
| Sonnet 5 | $0.00019 | $0.00379 |
| Haiku 4.5 | $0.00009 | $0.00190 |
Grade A, and why
text-memorizer 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 11d 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
目标
把一段需要背诵的文本转化为结构化记忆材料，通过主动提取练习和薄弱点追踪，让背诵不再靠机械重复。
适用场景
- 思政类课程（马原、毛概、思修、近代史）知识点背诵
- 政治/历史大题的要点记忆
- 任何需要结构化记忆的文本段落
- 期末考试前需要快速过一遍的知识点清单
Memory 系统
类型
内容记忆——存储文本知识点和复习状态，使用共享复习引擎管理间隔。
存储位置
memory/text-memorizer/：
questions.json：题库（按模块组织）weak_points.json：薄弱点追踪（引用 review-engine.md 的间隔字段）
复习引擎
间隔规则和复习算法见 memory/review-engine.md。本 Skill 遵循其中的：
- 间隔规则（1→2→4→8→16→32）
- 复习抽取算法
- 自评机制
- 掌握标准（连续 5 次正确 = mastered）
额外规则（文本记忆特有）：
- 抽背时 60% 来自薄弱点（
correct_streak <= 2），40% 随机 - 每次抽 3-5 题，避免疲劳
- 错题立即展示正确答案和辨析
管理命令
| 命令 | 行为 |
|---|---|
出题 / 抽背 |
按 60/40 比例混合抽题 |
复习薄弱点 |
只抽薄弱点 |
全部复习 |
忽略间隔，全部题库随机 |
只看 [模块名] |
只抽指定模块 |
记忆状态 |
显示：总题数、薄弱点数、已掌握数 |
清除 text-memorizer 记忆 |
删除 memory/text-memorizer/（二次确认） |
输入判断
- 用户输入一段文本（直接粘贴、引用文件、或描述内容）。
- 确认文本类型：逐字背诵的定义 / 要点复述的问答题 / 结构化理解的知识点。
- 如果用户说"出题""抽背""复习"等 → 进入检测模式。
- 如果用户说管理命令 → 执行对应操作。
执行流程
输入文本 → 内容分类 → 结构化拆分 → 思维导图 → 关键词压缩 → 生成题库 → 常见误区提示 → 写入存储
检测模式：读取存储 → 按比例抽题 → 自评 → 更新间隔 → 薄弱点追踪
1. 内容分类
| 类型 | 特征 | 拆分策略 |
|---|---|---|
| 定义型 | 一句话式精确表述 | 逐词挖空 |
| 要点型 | 3-8 个并列/递进要点 | 每点一块，关键词 |
| 过程型 | 有时间/因果顺序 | 流程图式拆分 |
| 对比型 | 两个以上事物的比较 | 表格拆分 |
2. 结构化拆分
按逻辑拆成 3-7 个模块。每个模块：主题标签 + 核心内容 + 1-3 个关键词。
3. 思维导图
缩进文本形式的树形结构。
4. 关键词压缩
每模块 1-3 个关键词，作为回忆触发器。
5. 生成题库
三种检测工具：
- 填空检测：梯度挖空（30% → 50% → 80%）
- 问答检测：每个要点 → 一个问题
- 关键词触发：看关键词复述要点
6. 常见误区提示（P0 强制）
对这段文本涉及的考点，列出 2-3 个最常见的记忆/理解错误：
| 常见错误 | 为什么错 | 正确理解 |
|---|---|---|
| ... | ... | ... |
例如思政类："把'唯一标准'记成'重要标准'"、"把'直接现实性'和'自觉能动性'混淆"。
7. 抽背与追踪
按 review-engine.md 间隔规则。抽背时 60% 薄弱点 + 40% 随机。每次 3-5 题。错题即时反馈。
输出格式
新文本输入时
## 内容分类
[类型] — [背诵精度要求]
## 结构化拆分
### 模块 1：[主题标签]
[核心内容]
> 关键词：[...]
### 模块 2：[主题标签]
...
## 思维导图
[缩进文本结构]
## 检测题
### 填空检测
- [低难度]
- [中难度]
- [高难度]
### 问答检测
Q1: [...]
Q2: [...]
### 关键词触发
关键词：[...] → 请复述
## 常见记忆误区
| 常见错误 | 为什么错 | 正确理解 |
|----------|---------|---------|
## 抽背
输入"出题"开始。输入"清除 text-memorizer 记忆"删除。
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.
- 11d ago First seen · 203 lines · 94 tokens per session scan A 1d6572f04178
text-memorizer is a skill published in the GitHub repository hwl668/Scientific-learning-skills- (13 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,896 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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