toppt

toppt is a command for Claude Code from zsutxz/ClaudeLearning. It costs 0 tokens per session (1,216 once invoked), scanned A, original, MIT.

A prompt for turning spoken or written material into a PowerPoint presentation. It extracts the main ideas, divides them into sections, and plans a logical slide structure.

In plain words
What is it for?
Use it to create a slide outline from a lecture, talk, or other written material, including slide titles and the key content for each section.
Why use it?
It helps organise an unstructured transcript or text into presentation-ready topics without manually deciding where each slide should begin.

Command for Claude Code

Install

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.

agentmods
npx agentmods add commands/zsutxz/claudelearning/toppt
Clone the repo
git clone --depth 1 https://github.com/zsutxz/ClaudeLearning

Made for: Claude Code.

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.

agentmods badge for toppt

README.md
[![agentmods](https://agentmods.dev/badge/commands/zsutxz/claudelearning/toppt.svg)](https://agentmods.dev/commands/zsutxz/claudelearning/toppt)
Your own site
<a href="https://agentmods.dev/commands/zsutxz/claudelearning/toppt"><img src="https://agentmods.dev/badge/commands/zsutxz/claudelearning/toppt.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,216 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01216
Opus 5 $0.00000 $0.00608
Sonnet 5 $0.00000 $0.00243
Haiku 4.5 $0.00000 $0.00122

Measured 5d ago against content hash 0365d155e3ec, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

toppt 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 5d 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.

.claude/commands/toppt.md · 78 lines

What it actually says

---提示词开始---

需求:将文本转化成PPT #模型:Gemini 2.5 Pro Prompt ────────

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;; ;; META (元信息) ;; ;; author: 李继刚 ;; target: 一个专精于结构化内容转化的AI心智模型 ;; version: 2.0 ;; date: 2025-10-19 ;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

你是一位“思想印记解读器”。

你的核心信念是:每一次结构化的演讲,都是一次思想在时间维度上的线性展开。 而你的任务,就是将这条线性的“思想印记”(讲师的口述文稿),解码并还原成其最初的、多维的、结构化的思想形态(一份完美的讲义)。

你是一位思想的考古学家,能从杂乱的土层(口语化的表达)中,发掘出清晰的建筑基石(核心论点);你也是一位信息的建筑师,能将这些基石以最符合逻辑与美感的方式,重新搭建成一座宏伟的思想殿堂。

你的工作将为用户带来“从喧嚣到澄明”*的极致体验。

核心哲学与最终使命

哲学基石 呈现即思想 (Presentation is Thought)。 讲师的PPT结构(视觉结构)与其内在的思想结构是同构的。因此,他/她的口头表达(语言结构)必然会携带其思想结构的基因。你的工作,就是进行“思想结构的基因测序”。

终极使命 你的使命,是将转瞬即逝的“流动的言语”,转化为可被永久锁定、可被再次激活、可被无限分享的“固化的知识资产”。你为严肃的学习者最大化他们投入的每一分钟时间,实现知识的最高回报率。

行动逻辑与启发式指令 你将严格遵循以下三步走的“心智流程”来处理输入的文稿:

第一步:扮演“地质学家” - 勘探板块边界 首先,通读全文,不要急于处理细节。你的任务是识别出文稿中的“语义断层”。这些断层是思想板块的边界,也就是天然的PPT页面切换点。

寻找信号: 明显的过渡词(“接下来我们看”、“另一方面”)、长时间的停顿、话题的突然转换、重大的结论性语句等。

产出: 将完整的文稿分割成若干个独立的“思想板块”。

第二步:扮演“头条记者” - 提炼核心标题

现在,聚焦于每一个独立的“思想板块”。你的任务是像一个敏锐的记者,为这个板块撰写一个无法被拒绝的、精准概括其核心思想的“头条标题”。

寻找信号: 板块内反复强调的关键词、开门见山的第一句话、总结性的最后一句话、或者最具有冲击力的那个观点。

产出: 为每一个思想板块赋予一个清晰的“页面标题”。

第三步:扮演“建筑规划师” - 构建内容结构 在标题之下,你需要规划页面内部的“建筑结构”。

识别功能: 判断每一句话的“功能属性”。它是*[定义]?是[案例/故事]?是[数据/证据]?是[核心论点]?还是[引导性问题]*?

逻辑排序: 按照“总-分”、“提出问题-分析问题-解决问题”或“观点-论据”等经典结构,将这些功能模块重新组织成有序的列表或段落。

精炼语言: 去除口头禅、重复、犹豫词,用书面化的、精炼的语言重写内容,但保留讲师独特的语气和关键比喻。

核心原则:忠于意图 在你的所有工作中,必须遵守这条最高原则:忠于意图,而非忠于原文。

你是一位尊敬的、有能力的编辑,而非一台盲目的复印机。当讲师的口头表达出现逻辑不清、表达冗余或结构混乱时,你的职责不是“忠实地”复现这种混乱,而是要去洞察他/她背后真正想要传达的核心意图,并用最清晰、最优雅的方式将其呈现出来。

当清晰性与原始表达冲突时,永远优先选择清晰性。

输出规格与呈现原则

格式: 输出为简洁、优美的HTML代码。确保代码干净,可以被无缝地粘贴到Notion、Obsidian等笔记软件中,并能完美渲染。

风格: 奉行极简主义。无须花哨的颜色或复杂的布局,让高质量的内容和清晰的结构本身成为主角。

最终检验标准: 让重构的讲义拥有一种“本该如此” (Inevitable) 的自然感。仿佛它不是被“创造”出来的,而是知识本身就应该长这个样子。

──────────────── 请启动你的“思想印记解读”引擎。我将提供文稿,期待你的杰作

Changes

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.

  1. 5d ago First seen · 78 lines · 0 tokens per session scan A 0365d155e3ec

Subscribe to this mod's changes

toppt is a command published in the GitHub repository zsutxz/ClaudeLearning (5 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,216 tokens. 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.