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Openakita/Skills@Ppt Creator

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openakita
openakita/skills@ppt-creator

Create professional presentations using the Pyramid Principle methodology. Supports PPTX generation, Marp/Reveal.js Markdown slides, chart creation, speaker notes, and self-evaluation rubrics. Minimal intake form to rapid output workflow.

Overview

Publisheropenakita
Repositoryopenakita
Skill nameopenakita/skills@ppt-creator
Stars
2K
Forks
277
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openakita on GitHub. Read the source before you install it.

Installation

Install the Openakita/Skills@Ppt Creator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openakita/openakita.git /tmp/openakita
mkdir -p .claude/skills
cp -r /tmp/openakita/skills/ppt-creator .claude/skills/openakita-openakita-skills-ppt-creator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openakita/Skills@Ppt Creator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openakita/Skills@Ppt Creator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openakita/Skills@Ppt Creator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PPT Creator — 专业演示文稿制作

When to Use

  • 用户需要制作商业演示文稿(汇报、路演、培训、产品介绍)
  • 需要将非结构化的想法整理为逻辑清晰的幻灯片
  • 需要生成包含数据图表的演示文稿
  • 需要自动生成演讲稿/备注
  • 需要将 Markdown 转换为演示文稿格式(Marp / Reveal.js)
  • 需要评估已有 PPT 的质量

Prerequisites

必需工具

工具用途安装方式
Python ≥ 3.10运行生成脚本系统预装
python-pptx生成 PPTX 文件pip install python-pptx

可选工具

工具用途安装方式
marp-cliMarkdown → PPT/PDFnpm install -g @marp-team/marp-cli
matplotlib数据图表生成pip install matplotlib
Pillow图片处理pip install Pillow
plotly交互式图表pip install plotly kaleido

验证安装

bash
python -c "from pptx import Presentation; print('python-pptx OK')"
marp --version  # 可选

Instructions

核心方法论:金字塔原理(Pyramid Principle)

本技能采用 Barbara Minto 的金字塔原理组织幻灯片结构:

           ┌──────────────┐
           │  核心结论     │  ← 1 句话概括
           └──────┬───────┘
        ┌─────────┼─────────┐
   ┌────┴────┐ ┌──┴───┐ ┌──┴────┐
   │ 论点 A  │ │论点 B│ │论点 C │  ← 3 个支撑论点
   └────┬────┘ └──┬───┘ └──┬────┘
     证据1-3    证据1-3   证据1-3   ← 每个论点 2-3 条证据

原则:

  1. 结论先行 — 每页幻灯片的标题就是该页的结论
  2. 以上统下 — 上层观点是下层内容的概括
  3. 归类分组 — 同层内容属于同一逻辑范畴
  4. 逻辑递进 — 同层内容有明确的排列顺序(时间、结构、重要性)

演示文稿结构模板

幻灯片内容时间
封面标题、副标题、演讲者、日期
目录/议程演讲结构概览30s
背景/问题现状描述、痛点阐述45-60s
核心论点 1第一个支撑论点 + 数据/案例45-60s
核心论点 2第二个支撑论点 + 数据/案例45-60s
核心论点 3第三个支撑论点 + 数据/案例45-60s
方案/建议具体行动方案45-60s
时间线/路线图实施计划45-60s
总结回扣核心结论、关键要点30s
Q&A / 致谢联系方式、讨论时间

Workflows

Workflow 1: 快速创建(Minimal Intake Form)

步骤 1 — 收集关键信息

向用户询问以下 5 个要素(尽量精简):

#问题示例回答
1演讲主题是什么?"Q4 产品路线图汇报"
2目标受众是谁?"公司管理层"
3核心结论/诉求?"需要追加 30% 研发预算"
4有哪些关键数据或论据?"用户增长 200%、竞品分析、技术债"
5幻灯片数量偏好?"10-15 页"

如果用户只给了主题,根据常识和金字塔原理自行推导其余要素,并在生成前确认。

步骤 2 — 构建金字塔结构

基于收集的信息,输出结构大纲:

核心结论:追加 30% 研发预算以支撑用户增长
├── 论点A:用户量爆发增长(200%)带来系统压力
│   ├── 证据:月活跃用户趋势图
│   ├── 证据:服务器负载数据
│   └── 证据:用户投诉工单增长
├── 论点B:竞品正在加速投入
│   ├── 证据:竞品融资/招聘动态
│   ├── 证据:功能对比矩阵
│   └── 证据:市场份额变化
└── 论点C:技术债已影响迭代速度
    ├── 证据:发布周期对比
    ├── 证据:Bug 趋势
    └── 证据:开发者满意度调查

步骤 3 — 生成幻灯片内容

逐页编写:

  • 标题:一句完整的结论性陈述(非主题词)
  • 正文:3-5 个要点,每点 ≤ 15 字
  • 图表/数据:识别需要数据可视化的页面
  • 备注:45-60 秒的演讲脚本

步骤 4 — 输出文件

根据用户需要选择输出格式(见 Output Format 部分)。


Workflow 2: 数据图表生成

支持的图表类型

图表适用场景
柱状图对比数据matplotlib / plotly
折线图趋势变化matplotlib / plotly
饼图占比分布matplotlib / plotly
散点图相关性分析matplotlib / plotly
瀑布图增减分解plotly
热力图矩阵数据matplotlib / seaborn
雷达图多维对比matplotlib

图表设计原则

  1. 标题即结论 — 图表标题描述洞察而非数据("销售额同比增长 40%" vs "Q4 销售数据")
  2. 极简配色 — 使用 2-3 种颜色,重点数据用强调色
  3. 去除噪音 — 删除网格线、多余边框、3D 效果
  4. 标注关键值 — 在图表上直接标注最重要的数据点
  5. 适当留白 — 图表不超过幻灯片面积的 60%

生成图表的 Python 示例

python
import matplotlib.pyplot as plt
import matplotlib

matplotlib.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei']
matplotlib.rcParams['axes.unicode_minus'] = False

fig, ax = plt.subplots(figsize=(10, 6))
categories = ['Q1', 'Q2', 'Q3', 'Q4']
values = [120, 180, 240, 350]
colors = ['#e0e0e0', '#e0e0e0', '#e0e0e0', '#4A90D9']

ax.bar(categories, values, color=colors, width=0.6)
ax.set_title('Q4 营收突破 350 万,环比增长 46%', fontsize=16, fontweight='bold', pad=20)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

for i, v in enumerate(values):
    ax.text(i, v + 5, f'{v}万', ha='center', fontsize=12)

plt.tight_layout()
plt.savefig('chart_revenue.png', dpi=200, bbox_inches='tight')

Workflow 3: 演讲备注生成

为每张幻灯片生成 45-60 秒的演讲稿:

规则:

  1. 开头:过渡语连接上一页("接下来看...""那么...")
  2. 中间:用口语化表达阐述幻灯片要点
  3. 结尾:用一句话总结本页核心信息
  4. 字数:中文约 150-200 字 ≈ 45-60 秒
  5. 标注停顿点:[停顿][看向观众]
  6. 标注交互点:[提问][举手调查]

示例:

那么我们来看第一个关键发现。[停顿]

过去一个季度,我们的月活跃用户从 50 万增长到了 150 万,
同比增长了 200%。[看向观众] 这是一个非常可喜的数字,
但同时也带来了挑战。

大家可以看到右边的图表,随着用户量的激增,
我们的服务器平均响应时间从 200 毫秒上升到了 800 毫秒,
高峰期甚至出现过 5 秒的延迟。[停顿]

这意味着什么?如果我们不尽快扩容,
用户体验将会显著下降,这直接影响留存率。

Workflow 4: 自评打分

生成完成后,用以下 rubric 评估质量:

维度满分评分标准
结构逻辑25金字塔结构完整、论点 MECE
视觉设计20一致的配色、排版、留白
数据可视化20图表清晰、标题即结论
演讲备注15流畅、口语化、时间合适
受众适配10语言风格匹配目标受众
行动导向10明确的 CTA 和下一步

目标分数:≥ 80/100。低于 80 分时自动修订弱项。


Output Format

格式 1: PPTX 文件(python-pptx)

使用 python-pptx 生成标准 PowerPoint 文件:

python
from pptx import Presentation
from pptx.util import Inches, Pt
from pptx.enum.text import PP_ALIGN

prs = Presentation()
prs.slide_width = Inches(13.333)
prs.slide_height = Inches(7.5)

slide_layout = prs.slide_layouts[6]  # blank layout
slide = prs.slides.add_slide(slide_layout)

title_shape = slide.shapes.add_textbox(Inches(0.8), Inches(0.5), Inches(11.7), Inches(1.2))
tf = title_shape.text_frame
tf.text = "Q4 营收同比增长 46%,超额完成目标"
tf.paragraphs[0].font.size = Pt(28)
tf.paragraphs[0].font.bold = True

notes_slide = slide.notes_slide
notes_slide.notes_text_frame.text = "演讲备注内容..."

prs.save('presentation.pptx')

格式 2: Marp Markdown

markdown
---
marp: true
theme: default
paginate: true
header: "公司名 | Q4 汇报"
footer: "机密"
---

# Q4 产品路线图汇报
### 需要追加 30% 研发预算以支撑用户增长

**演讲者** | 日期

---

## 用户量爆发增长带来系统压力

- 月活从 50 万 → 150 万(+200%)
- 服务器响应时间 200ms → 800ms
- 用户投诉工单增长 3 倍

![bg right:40%](chart_growth.png)

---

转换命令:

bash
marp slides.md --pptx -o presentation.pptx
marp slides.md --pdf -o presentation.pdf
marp slides.md --html -o presentation.html

格式 3: Reveal.js HTML

html
<!DOCTYPE html>
<html>
<head>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/reveal.js@4/dist/reveal.css">
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/reveal.js@4/dist/theme/white.css">
</head>
<body>
<div class="reveal">
    <div class="slides">
        <section>
            <h1>Q4 产品路线图汇报</h1>
            <p>需要追加 30% 研发预算以支撑用户增长</p>
            <aside class="notes">演讲备注...</aside>
        </section>
        <section>
            <h2>用户量爆发增长带来系统压力</h2>
            <ul>
                <li>月活从 50 万 → 150 万(+200%)</li>
                <li>服务器响应时间 200ms → 800ms</li>
            </ul>
            <aside class="notes">演讲备注...</aside>
        </section>
    </div>
</div>
<script src="https://cdn.jsdelivr.net/npm/reveal.js@4/dist/reveal.js"></script>
<script>Reveal.initialize({ hash: true });</script>
</body>
</html>

格式 4: PNG 图表

每张数据图表单独输出为 PNG,分辨率 ≥ 200 DPI。


Common Pitfalls

1. 标题写成了"主题词"而非"结论"

错误:幻灯片标题 "Q4 销售数据" 正确:幻灯片标题 "Q4 销售额同比增长 40%,超额完成年度目标"

2. 每页内容过多

每张幻灯片不超过 5 个要点,每个要点不超过 15 个汉字。当内容过多时拆分为多页。

3. 配色不一致

整套 PPT 使用同一配色方案。推荐的安全配色:

  • 主色 1 种 + 强调色 1 种 + 灰色系
  • 避免使用超过 4 种颜色

4. 图表与结论脱节

每张图表必须支撑其所在页的标题结论。如果图表无法直接证明标题,要么修改图表要么修改标题。

5. 忽略受众水平

  • 给管理层:聚焦商业影响、省略技术细节
  • 给技术团队:可以深入架构、代码层面
  • 给客户:突出价值和收益

6. 中文字体兼容

使用 python-pptx 时确保指定中文字体:

python
from pptx.util import Pt
paragraph.font.name = 'Microsoft YaHei'

7. 演讲时间估算不准

中文演讲速度约 200-250 字/分钟。10 页幻灯片 × 1 分钟/页 = 10 分钟演讲。如果用户要求 30 分钟演讲,需要约 25-30 页内容。


EXTEND.md 扩展

用户可在技能同目录下创建 EXTEND.md 添加:

  • 公司 PPT 模板路径
  • 品牌配色和字体
  • 常用 PPT 结构模板
  • 特定行业/场景的幻灯片范例

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Openakita/Skills@Ppt Creator AI skill do?

Create professional presentations using the Pyramid Principle methodology. Supports PPTX generation, Marp/Reveal.js Markdown slides, chart creation, speaker notes, and self-evaluation rubrics. Minimal intake form to rapid output workflow.

Why use Openakita/Skills@Ppt Creator on TypingMind?

Because you install it once and use it with any model. Openakita/Skills@Ppt Creator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openakita/Skills@Ppt Creator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openakita/openakita/tree/main/skills/ppt-creator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Openakita/Skills@Ppt Creator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openakita/Skills@Ppt Creator?

As many as you like. As long as a model supports skills, you can use Openakita/Skills@Ppt Creator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openakita/Skills@Ppt Creator AI skill free?

Yes. It is published on GitHub by openakita under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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