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Generate Writeup

Community
pamelafox
generate-writeup

Generate an annotated blog-style write-up from a presentation's slides and video recording. Orchestrates the full pipeline: fetching slides, converting to images, extracting transcript, generating chapters, outlining slides, and producing the final annotated markdown. USE FOR: write-up, blog post, presentation summary, annotated write-up, generate writeup.

Overview

Publisherpamelafox
Repositorypresentation-skills
Skill namegenerate-writeup
Stars
129
Forks
23
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Generate Writeup 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/pamelafox/presentation-skills/tree/main/.agents/skills/generate-writeup
  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/pamelafox/presentation-skills.git /tmp/presentation-skills
mkdir -p .claude/skills
cp -r /tmp/presentation-skills/.agents/skills/generate-writeup .claude/skills/generate-writeup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Generate Writeup 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 Generate Writeup 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 Generate Writeup 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.

Generate annotated write-up from a presentation

You are generating a blog-style write-up from a presentation. The user will provide a presentation folder path (e.g., presentations/my-talk). That folder must contain a presentation.md file.

presentation.md format

markdown
# Talk title

- **Date:** 2026-01-15
- **Video:** https://www.youtube.com/watch?v=VIDEO_ID
- **Slides:** slides.pdf
- **Transcript:** transcript.txt

## Notes

Extra notes here

Required fields: Video and Slides. All others are optional. Slides can be a local PDF path, PDF URL, PPTX URL, OneDrive link, or RevealJS URL. Video can be a YouTube URL or local MP4 path.

Pipeline

Follow these steps in order. Cache all intermediate outputs in <presentation_folder>/outputs/ so subsequent runs skip completed steps.

Step 1: Resolve slides

  • If Slides in presentation.md is a URL, fetch and convert to PDF:
    bash
    uv run .agents/skills/fetch-slides/fetch_slides.py <url> <presentation_folder>/outputs
    This produces slides.pdf (and slides_content.md for RevealJS URLs).
  • If slides is a local file path (relative to the presentation folder), use it directly.
  • Skip if <presentation_folder>/outputs/slides.pdf already exists.

Step 2: Convert slides to images

Split the PDF into individual PNGs:

bash
uv run .agents/skills/convert-slides-to-images/convert_slides_to_images.py <pdf_path> <presentation_folder>/outputs/slide_images

Skip if <presentation_folder>/outputs/slide_images/ already contains slide_*.png files.

Step 3: Extract transcript

  • If Transcript is specified in presentation.md, read from that path (relative to presentation folder).
  • If <presentation_folder>/outputs/transcript.txt exists, use the cached version.
  • Otherwise, extract from YouTube:
    bash
    uv run .agents/skills/extract-transcript/extract_transcript.py <youtube_url> <presentation_folder>/outputs/transcript.txt

Step 4: Generate video chapters

If <presentation_folder>/outputs/chapters.txt exists, use the cached version. Otherwise:

  1. Read the transcript from Step 3.
  2. Write a brief summary paragraph (2-3 sentences) describing what the video is about.
  3. Create a list of timestamped chapters in "MM:SS - Chapter Title" format covering the main topics.
  4. Save to <presentation_folder>/outputs/chapters.txt.

Step 5: Extract slide text

Extract the text content of each PDF page to provide ground truth for what each slide actually says. This prevents misidentifying embedded screenshots or demo captures as actual slide content.

bash
uv run .agents/skills/extract-slide-text/extract_slide_text.py <pdf_path> <presentation_folder>/outputs/slide_ascii.md <presentation_folder>/outputs/slide_images

Skip if <presentation_folder>/outputs/slide_ascii.md already exists.

Step 6: Outline slides

If <presentation_folder>/outputs/outline.txt exists, use the cached version. Otherwise:

  1. Find all slide_*.png files in <presentation_folder>/outputs/slide_images/, sorted numerically.
  2. Read <presentation_folder>/outputs/slide_ascii.md (from Step 5) as ground truth for each slide's text content.
  3. Look at each slide image and write a one-sentence summary. Base the summary primarily on the extracted text from slide_ascii.md, using the image only for visual context (diagrams, layout, screenshots).
  4. For large presentations (50+ slides), work in batches of 50.
  5. Save to <presentation_folder>/outputs/outline.txt.

Step 7: Generate the annotated write-up

Gather all context, then generate the write-up following the rules below.

Context to gather:

  • TRANSCRIPT — Full transcript text (Step 3)
  • VIDEO_CHAPTERS — Chapter summary (Step 4)
  • SLIDE_TEXT — Extracted text per slide from slide_ascii.md (Step 5). Use as ground truth for what each slide contains.
  • SLIDE_OUTLINE — Numbered slide outline (Step 6)
  • VIDEO_SOURCE — YouTube URL or MP4 path from presentation.md
  • IS_LOCAL_VIDEO — true if VIDEO_SOURCE is a local MP4 file
  • SLIDES_HTML_CONTENT — Contents of slides_content.md if it exists (RevealJS only)
  • Check if SLIDES_HTML_CONTENT contains "SECTION HEADING" markers → HAS_SECTION_HEADINGS

Write-up generation prompt:

Create an annotated blog post that explains the content of each slide.

STRUCTURE REQUIREMENTS:

  1. Start with a level-1 heading (#) containing the talk title.
  2. Follow with an overview paragraph introducing the talk.
  3. Do NOT include a table of contents — it will be generated automatically.

If HAS_SECTION_HEADINGS is true (RevealJS slides with section markers):

  • Use level-2 headings (##) for SECTION HEADING slides — these are section dividers.
  • Use level-3 headings (###) for regular slides within each section.

If HAS_SECTION_HEADINGS is false:

  • Use level-2 headings (##) with a descriptive title for each slide's content.

For each slide section, include:

  • The heading
  • The slide image reference immediately after the heading: ![Alt text](slide_images/slide_N.png)
  • A timestamp link to the video (or [MM:SS] for local MP4s)
  • The explanatory text

End with a ## Q&A section containing questions and answers, each question as a level-3 heading (###).

HEADING CAPITALIZATION: Capitalize the first letter like a normal sentence. Only capitalize proper nouns and acronyms elsewhere.

  • CORRECT: "Hybrid search combines keyword and vector retrieval"
  • WRONG: "Hybrid Search Combines Keyword And Vector Retrieval" (title case)

Example structure:

markdown
# Building intelligent agents with RAG

This talk introduces techniques for building AI agents...

## Hybrid search combines multiple retrieval methods

![Diagram showing hybrid search architecture](slide_images/slide_8.png)
[Watch from 04:12](https://www.youtube.com/watch?v=VIDEO_ID&t=252s)

Hybrid search combines keyword search and vector search...

## Q&A

### How do I configure specific indexes as knowledge sources?

Answer text here...

WRITING GUIDELINES:

  1. Write like speaker notes: state the actual information and explain how things work. Do NOT describe or analyze the talk from the outside.
  2. Never narrate what speakers, slides, or the talk itself did. Forbidden patterns include "The speaker explains...", "This slide shows...", "The talk demonstrates...", "This section marks the shift to...", "The diagram illustrates...". Instead, just state the content directly.
  3. Make every sentence information-dense without repetition.
  4. Provide enough detail per slide that a reader does not need to watch the video.
  5. Capture supplementary information from the talk that is NOT visible on the slides.
  6. Use short words, fewer words. Get to the point.
  7. Avoid multiple examples if one suffices. Cut transitional fluff.
  8. Prefer flowing prose over bullet points.
  9. Use simple language. No emojis. No hedge words unless exceptions matter.
  10. Include relevant links from the slides where appropriate.
  11. For YouTube videos, create timestamped links: [Watch from MM:SS](VIDEO_SOURCE&t=SECONDSs)
  12. For local MP4 files, just provide timestamps in [MM:SS] format.
  13. Reference slides as slide_images/slide_1.png, slide_images/slide_2.png, etc.

If SLIDES_HTML_CONTENT exists, use it to find URLs that should be included in the writeup.

Step 8: Insert table of contents

After generating the write-up:

  1. Find all ## headings in the generated markdown.
  2. For each heading, create an anchor link:
    • Convert to lowercase
    • Remove special characters (keep alphanumeric, spaces, hyphens)
    • Replace spaces with hyphens
    • Collapse multiple hyphens
  3. Build a TOC:
    markdown
    ## Table of contents
    
    - [Heading text](#anchor-link)
    - [Another heading](#another-heading)
  4. Insert the TOC before the first ## heading (after the # title and intro paragraph).

Step 9: Save output

Save the final write-up to <presentation_folder>/outputs/writeup.md.

Output structure

<presentation_folder>/
├── presentation.md
└── outputs/
    ├── writeup.md           ← Final output
    ├── transcript.txt       ← Cached transcript
    ├── chapters.txt         ← Cached video chapters
    ├── slide_ascii.md       ← Extracted text per slide
    ├── outline.txt          ← Cached slide outline
    ├── slides_content.md    ← RevealJS extracted content (if applicable)
    └── slide_images/
        ├── slide_1.png
        ├── slide_2.png
        └── ...

Frequently asked questions

What does the Generate Writeup AI skill do?

Generate an annotated blog-style write-up from a presentation's slides and video recording. Orchestrates the full pipeline: fetching slides, converting to images, extracting transcript, generating chapters, outlining slides, and producing the final annotated markdown. USE FOR: write-up, blog post, presentation summary, annotated write-up, generate writeup.

Why use Generate Writeup on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pamelafox/presentation-skills/tree/main/.agents/skills/generate-writeup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Generate Writeup?

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 Generate Writeup?

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

Is the Generate Writeup AI skill free?

Yes. It is published on GitHub by pamelafox 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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