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Pptx

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AgentTeam-TaichuAI
pptx

Use this skill any time a .pptx file is involved — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading or extracting text from .pptx files; editing or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions 'deck', 'slides', 'presentation', or references a .pptx filename. If a .pptx file needs to be opened, created, or touched, use this skill.

Overview

PublisherAgentTeam-TaichuAI
RepositoryScienceClaw
Skill namepptx
Stars
670
Forks
73
Bundled files
52
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.

  • 52 bundled files

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

  • Open source

    Published by AgentTeam-TaichuAI on GitHub. Read the source before you install it.

Installation

Install the Pptx 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.
    https://github.com/AgentTeam-TaichuAI/ScienceClaw/tree/master/ScienceClaw/backend/builtin_skills/pptx
  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/AgentTeam-TaichuAI/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/ScienceClaw/backend/builtin_skills/pptx .claude/skills/pptx
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pptx 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 Pptx 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 Pptx 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.

PPTX Skill

Quick Reference

TaskApproach
Read/analyze contentpython3 -m markitdown presentation.pptx
Generate structured presentationUse generate_report.js — see below
Create custom from scratchUse pptxgenjs directly (see pptxgenjs section)

Generating Presentations (Recommended)

For structured presentations with cover slide, auto-generated TOC, content slides, and closing, use the pre-built template:

Step 1: Copy the generator script

bash
cp /builtin-skills/pptx/scripts/generate_report.js ./generate_pptx.js

Step 2: Build slides_data.json via Python script

Two phases: write slide content files, then assemble into JSON.

CRITICAL: NEVER write or edit .json files directly. Use a Python script with json.dump() to guarantee valid JSON output.

Phase 1 — Write each slide's content as a plain text file using write_file:

For each major topic, read_file the relevant research data, then write_file the slide content directly:

read_file("research_data/literature.md")           # refresh data in context
write_file("slides/slide_01_intro.txt", "...")      # write slide body
write_file("slides/slide_02_analysis.txt", "...")   # next slide
...

Each content slide body should be concise but substantive (3-6 bullet points or 2-4 sentences with specific data and citations).

NEVER write a Python script that contains slide text as string literals. The slide content goes directly into .txt files via write_file, not into Python code.

Language (CRITICAL):

  • All presentation content (title, subtitle, slide headings, body text, chart labels, speaker notes) MUST be written in the user's configured language as specified in the system prompt's ## Language section.
  • If the user's language is Chinese (zh), write the entire presentation in Chinese; if English (en), write in English. Do NOT mix languages unless quoting a proper noun or technical term that has no standard translation.

Phase 2 — Assemble into JSON using a standard assembler script:

python
import json, glob, os

SLIDES_DIR = "slides"
TITLE = "Presentation Title"
SUBTITLE = "Subtitle or tagline"

# Slide config: (file_pattern, type, title, extra_fields_or_None)
SLIDE_MAP = [
    ("slide_01_*.txt", "section", "Introduction", None),
    ("slide_02_*.txt", "bullets", "Key Findings", None),
    ("slide_03_*.txt", "content", "Market Overview", {"layout": "text-image", "image": "chart.png"}),
    ("slide_04_*.txt", "stat", "Key Metrics", {
        "stats": [
            {"value": "25%", "label": "Revenue Growth"},
            {"value": "15%", "label": "Cost Reduction"},
            {"value": "32%", "label": "Market Share"},
        ]
    }),
    ("slide_05_*.txt", "content", "Trend Analysis", None),
    (None, "chart_pie", "Distribution", {
        "labels": ["Type A", "Type B", "Type C", "Other"],
        "values": [35, 30, 20, 15],
    }),
    (None, "chart_line", "Yearly Trends", {
        "x_labels": ["2020", "2021", "2022", "2023", "2024"],
        "series": [{"name": "Metric", "values": [40, 55, 62, 70, 78]}],
    }),
]

data = {
    "title": TITLE, "subtitle": SUBTITLE,
    "author": "ScienceClaw", "date": "2026-03-09",
    "theme": "midnight", "toc": True, "slides": [],
}

for entry in SLIDE_MAP:
    pattern, stype, title, extra = entry
    slide = {"type": stype, "title": title}

    if pattern:
        matches = sorted(glob.glob(os.path.join(SLIDES_DIR, pattern)))
        if matches:
            body = open(matches[0], encoding="utf-8").read().strip()
            if stype == "bullets":
                slide["items"] = [line.strip() for line in body.split("\n") if line.strip()]
            else:
                slide["body"] = body

    if extra:
        slide.update(extra)
    data["slides"].append(slide)

# Closing slide
data["slides"].append({"type": "closing", "title": "Thank You", "subtitle": "Questions?"})

with open("slides_data.json", "w", encoding="utf-8") as f:
    json.dump(data, f, ensure_ascii=False, indent=2)
print(f"Generated slides_data.json ({len(data['slides'])} slides)")

Step 3: Generate the PPTX

bash
python3 build_slides_data.py   # outputs slides_data.json
node generate_pptx.js slides_data.json output.pptx

Supported slide types

TypeRequired FieldsOptional Fields
title(auto from top-level)title, subtitle, author, date
agendaitems (string array)title (default: "Agenda")
sectiontitlesubtitle
contenttitlebody, layout, image
bulletstitle, itemssubtitle
two_columntitle, left, righteach column: {heading, body} or {heading, items}
tabletitle, headers, rowscaption
stattitle, statsstats: [{value, label}] (2-4 items)
imagetitle, imagecaption
chart_bartitle, categories, seriesseries: [{name, values}]
chart_pietitle, labels, values—
chart_linetitle, x_labels, seriesseries: [{name, values}]
quotetextauthor, title
closingtitlesubtitle, contact

Chart types for data visualization (use 2-4 per presentation, mix types):

  • chart_bar — for comparisons, rankings (e.g. drug efficacy scores)
  • chart_pie — for proportions, distributions (e.g. mutation type breakdown)
  • chart_line — for trends over time (e.g. yearly incidence rates)

Top-level JSON keys

KeyRequiredDescription
titleYesPresentation title (cover slide)
subtitleNoSubtitle on cover slide
authorNoAuthor name
dateNoDate string
themeNoColor theme (see below)
tocNoAuto-generate Table of Contents slide (default: true)
slidesYesArray of slide objects

Available themes

ThemePrimarySecondaryAccentStyle
midnight1E2761CADCFC408EC6Dark navy, professional
forest2C5F2D97BC62F5F5F5Green, nature
coralF96167F9E7952F3C7EWarm, energetic
ocean065A821C729321295CDeep blue, calm
charcoal36454FF2F2F2212121Minimal, modern
teal02809000A89602C39AFresh, trustworthy
berry6D2E46A26769ECE2D0Elegant, warm

Design guidelines

  • Every slide needs a visual element — avoid text-only slides
  • Vary layouts — don't repeat the same layout across slides
  • Use section slides to separate major topics — these also populate the auto-generated TOC
  • Use stat slides for key numbers (big number + label format)
  • Mix chart types — aim for 2-4 charts per presentation (bar for comparisons, pie for distributions, line for trends)
  • Keep bullet points concise — 3-6 items per slide, 1-2 lines each
  • Content quality: Each slide body should contain specific data, not vague summaries. Include numbers, percentages, dates, and source references where possible.

Writing guidelines for long presentations

  1. Use the two-phase workflow: Write slide content as separate text files first, then assemble into JSON. This avoids context loss and allows iterative refinement.
  2. Be specific: Slide bodies should contain concrete data, not generic statements. "Revenue grew 25% to $4.2B in Q3 2025" beats "Revenue increased significantly".
  3. Use section slides as dividers: They auto-populate the TOC and give the audience clear navigation.
  4. Balance text and visuals: For every 2-3 text/bullet slides, include a chart, stat, or image slide.
  5. End with a closing slide: Include contact info or next steps.
  6. No page/slide count on cover: Do NOT include total page count or slide count on the cover slide or subtitle — the count is unknown at assembly time and produces inaccurate information.

Reading Content

bash
python3 -m markitdown presentation.pptx

Creating Custom Presentations (pptxgenjs)

For fully custom presentations beyond the template's capabilities, use pptxgenjs directly:

javascript
const pptxgen = require("pptxgenjs");
let pres = new pptxgen();
pres.layout = "LAYOUT_16x9";

let slide = pres.addSlide();
slide.addText("Hello World!", { x: 0.5, y: 0.5, fontSize: 36, color: "363636" });

pres.writeFile({ fileName: "output.pptx" });

Key rules for pptxgenjs

  • NEVER use "#" with hex colors — color: "FF0000" not color: "#FF0000"
  • Use bullet: true — NEVER unicode symbols like "•"
  • Use breakLine: true between array items for multi-line text
  • NEVER reuse option objects — pptxgenjs mutates them in-place; create fresh objects each time
  • Coordinates are in inches; LAYOUT_16x9 = 10" × 5.625"

Converting to Images (for QA)

bash
python3 /builtin-skills/pptx/scripts/office/soffice.py --headless --convert-to pdf output.pptx
pdftoppm -jpeg -r 150 output.pdf slide

Dependencies

  • npm install -g pptxgenjs — creating presentations
  • pip install "markitdown[pptx]" — text extraction
  • pip install Pillow — image processing
  • LibreOffice (soffice) — PDF conversion
  • Poppler (pdftoppm) — PDF to images

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 Pptx AI skill do?

Use this skill any time a .pptx file is involved — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading or extracting text from .pptx files; editing or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions 'deck', 'slides', 'presentation', or references a .pptx filename. If a .pptx file needs to be opened, created, or touched, use this skill.

Why use Pptx on TypingMind?

Because you install it once and use it with any model. Pptx 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 Pptx in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgentTeam-TaichuAI/ScienceClaw/tree/master/ScienceClaw/backend/builtin_skills/pptx. 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 Pptx?

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 Pptx?

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

Is the Pptx AI skill free?

It is published on GitHub by AgentTeam-TaichuAI. Check the repository for licensing terms. 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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