edu-math-tutorial

edu-math-tutorial is a skill for Claude Code, Codex from agentscope-ai/QwenPaw. It costs 146 tokens per session (1,994 once invoked), scanned A, original, Apache-2.0.

A Chinese-language guide for turning a maths problem into a step-by-step teaching video. It explains how to break down the solution, write narration, format equations, and structure scenes.

In plain words
What is it for?
Use it when creating Chinese maths explanation videos about equations, formulas, or geometry.
Why use it?
It provides a consistent teaching structure and helps avoid unclear steps, incorrect equation reading, and mismatched subtitles.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when creating Chinese maths explanation videos about equations, formulas, or geometry.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agentscope-ai/qwenpaw/edu-math-tutorial
About the project

QwenPaw is a personal AI assistant that runs on a local machine or in the cloud and connects to multiple chat applications. It provides memory, file workspaces, multiple agents, skills, plugins, and integrations with language-model providers and external tools. The catalogue entries are skills that extend its capabilities.

agentscope-ai/QwenPaw · 35,726 stars · on GitHub · qwenpaw.agentscope.io

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.

Any agent
npx skills add agentscope-ai/QwenPaw --skill edu-math-tutorial
Clone the repo
git clone --depth 1 https://github.com/agentscope-ai/QwenPaw

Made for: Claude Code, Codex.

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 edu-math-tutorial

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/edu-math-tutorial/github.svg)](https://agentmods.dev/skills/agentscope-ai/qwenpaw/edu-math-tutorial)
Your own site
<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/edu-math-tutorial"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/edu-math-tutorial/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.

agentmods 80×15 button for edu-math-tutorial

Your own site · 80×15
<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/edu-math-tutorial"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/edu-math-tutorial.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 146 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,994 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. ✓ AI security review Fable 5.1 · 6 Sept 2026 📄 Read the review Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00146 $0.01994
Opus 5.5 $0.00058 $0.00798
Sonnet 5.5 $0.00029 $0.00399
Haiku 4.5 $0.00015 $0.00199

Measured 1mo ago against content hash 78692d6afd28, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

edu-math-tutorial 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 1mo 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.

plugins/apps/qwenpaw-creator/backend/skills/edu-math-tutorial/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

数学讲解视频 · 领域知识

本 skill 指导如何把一道数学题组织成高质量的分步讲解视频。它不提供渲染脚本: 每个讲解场景是主时间轴上的一个 creation.type=motion_clip Element,旁白是 creation.type=audio Element,成片由 Creator 的确定性渲染管线合成。

1. 题目拆解方法

先完成解题分析,再规划视频。按顺序产出三层事实(写进委派任务书,不需要落盘):

  1. 题面:完整题目文本,全部数学表达式用 LaTeX 表示;有图形时先文字化描述。
  2. 解法链:选择最常规、面向学生的解法,把过程拆成 2–5 个原子步骤,每步 只做一件事。一元一次方程的标准链是:移项 → 合并同类项 → 系数化为 1; 几何题的标准链是:读图标注 → 引用定理 → 代入求解。
  3. 易错点:每步至多标注一个学生最易犯的错误(如"移项要变号"),在旁白 与画面中同步提醒。

2. 视频结构与讲解节奏

固定五段式结构,每段一个场景(motion_clip Element):

段落 内容 时长基准
开场 完整题面 + 一句目标("我们的目标是求出 x") 6–10s
步骤 N 每个原子步骤一段:上一步结果 → 本步操作标注 → 本步结果 各 8–14s
收尾 最终答案 + 一句验算或总结 5–8s

节奏规则:

  • 旁白按语速约 4.5 字/秒估算时长;场景时长 = 对应旁白时长 + 1–2s 静读缓冲。
  • 每段旁白 1–3 句,单句不超过 25 字;步骤段先说操作依据,再报结果。
  • 相邻场景之间内容必须有承接:下一场景开头展示上一场景的结论("上一步"栏)。

3. 旁白文案规范

  • 全程中文口语,数学式读法固定:5x-7=13 读作"5x 减 7 等于 13";分数读作 "5 分之 20";除法读作"20 除以 5"。不读 LaTeX 符号本身。
  • 操作句式:"把常数项负 7 移到等号右边,移项要变号,所以右边变成 13 加 7。"
  • 每句都要能独立成字幕:不用"然后呢"“接下来我们再"之类的口头填充词。
  • 旁白文本写入对应 audio Element 的 creation.script,后续可改词重合成。
  • 旁白逐句同步上字幕:每一句旁白对应一张字幕卡(见第 5 节),字幕文本必须与该句旁白逐字一致。

4. 版式与画面风格(motion_clip 的 prompt 要点)

每个场景的 creation.prompt 用自然语言写清画面意图,包含以下要素,实际动效 文档由 design_motion_overlays 生成(不要手写 creation.motion):

  • 主题:默认"Aurora Scholar"——浅色淡紫/靛蓝渐变背景、柔和光斑;同一 视频内所有场景共用一个主题。
  • 教学面板:白色实心不透明圆角卡片承载公式与步骤(禁止半透明磨砂), 卡片带分层柔和阴影;步骤编号用紫色圆形徽标。
  • 公式排版:数学表达式按 KaTeX 风格排版(衬线斜体变量、正体数字与 运算符),题面/结果公式为视觉主体、居中放大;操作说明用小号胶囊标签放在 两个公式之间(如"将 -7 移到右边,变号为 +7")。
  • 步骤卡布局:"上一步"公式在上(浅灰底栏),操作标签居中,"得到"结果 在下(浅绿底栏高亮)。
  • 画面文字全中文:步骤徽标、操作标签、栏目标题("上一步""得到"等)一律 用中文,禁止出现英文标注;每段卡片内容必须与本段旁白所讲的步骤严格一致 (写 prompt 时把本段旁白原文附在后面作为对齐锚)。
  • 字幕:由独立的字幕 Overlay Element 承担(见第 5 节),不要画进 motion_clip 文档里;教学面板预留底部约 15% 高度的留白,确保字幕不与 面板重叠。
  • 动效:入场用轻柔上浮淡入,公式变化处做逐项高亮;不使用炫技转场。

5. 落地为 Creator 原生结构(执行流程)

  1. 按第 1–2 节完成拆解与分段规划,估算每段旁白文本与时长。
  2. 委派 ai_editing_director 到 timeline:<timelineId>,任务书写明:
    • 按分段各创建一个 creation.type=motion_clip 的主轨 Element(span 按 第 2 节时长基准顺序接排,creation.prompt 按第 4 节要点写足画面内容, 包含该段的具体公式 LaTeX、中文操作标注文字与本段旁白原文);
    • 为每段创建 creation.type=audio 的旁白 Element(creation.script 用 第 3 节规范的文案,span 与对应场景对齐);
    • 为每一句旁白创建一个 creation.type=overlay 的字幕 Element:text 填该句旁白原文(逐字一致,不改写不省略),span 按该句在本段内的 时间位置排布(按字数占比分配本段时长),location 用底部居中胶囊 默认值 x=0.50, y=0.88, width=0.80, height=0.14;字幕不可省略, 每句旁白都必须有对应字幕覆盖其时段;
    • 写入后调用 design_motion_overlays 生成各 motion_clip 的动效文档 与字幕卡样式,必须同时传 captionStyle="uniform" 和 sceneStyle="edu_steps":解说字幕全片用同一固定模板,只换文字; 教学场景用确定性推导卡模板(步骤徽章/上一步/推导行/结果高亮固定 骨架,底部自带字幕留白),模型只填内容文案且强制中文,全片版式 绝对一致。为此每个 motion_clip 的 prompt 只需写清本段要讲的内容 (步骤名、上一步公式、本步推导、结果),不需要描述视觉风格。
  3. 成片合成走 Creator 确定性渲染(用户在 UI 上或由既有 compose 流程触发), 不在任何沙箱内渲染视频。
  4. 自查:逐场景核对公式推导正确、画面标注全中文且与本段旁白一致、每句旁白 都有字幕且不与面板重叠、字幕样式全篇一致、场景时长覆盖对应旁白。

Read the full file on GitHub · 100 lines

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. 1mo ago First seen · 100 lines · 146 tokens per session scan A 78692d6afd28

Subscribe to this mod's changes

edu-math-tutorial is a skill published in the GitHub repository agentscope-ai/QwenPaw (35,726 stars, last pushed yesterday), licensed Apache-2.0. It adds 146 tokens to every session and 1,994 once invoked, about $0.0006 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

fp-brief

First-principles briefing from technical documents. Use when: understanding why decisions were made, onboarding to feature reasoning, reviewing decision chains, explaining doc from first principles. Not for: PM/CTO summary (use project-brief), pre-doc analysis (use feasibility-study), code explanation (use…

sd0xdev/sd0x-harness · 77 tokens

codex-explain

Explain complex code via Codex exec. Use when: understanding complex logic, tracing data flow, onboarding to unfamiliar code. Not for: code review (use codex-code-review), exploration (use code-explore). Output: structured explanation at chosen depth.

sd0xdev/sd0x-harness · 56 tokens

learning-opportunities

Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Triggers on "learning exercise", "help me…

tech-leads-club/agent-skills · 98 tokens

sharingan

Replicate knowledge from any source as sd0x-dev-flow skill definition. Use when: copying skills from repos, adapting patterns from articles/papers/code, converting knowledge to skill format. Not for: research without skill output (use deep-research), creating skills from scratch (use skill-creator), project onboarding…

sd0xdev/sd0x-harness · 87 tokens

video-tutorial-maker

Create scripted tutorial videos with narration, TTS, aligned subtitles, MP4 rendering, and platform variants. Use when Codex needs to create or revise tutorial, explainer, product walkthrough, course, demo, or short-form videos; generate 16:9 landscape output by default unless the user explicitly asks for 9:16, 1:1…

guanyang/super-publisher · 81 tokens

azure-lab-services

Expert knowledge for Azure Lab Services development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring lab plans, VM templates/schedules, VNet-integrated labs…

MicrosoftDocs/Agent-Skills · 115 tokens