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Wowerpoint

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thedotmack
wowerpoint

Turn one document into a kawaii NotebookLM slide-deck PDF. Use for "wowerpoint this", "make a deck about <file>", "turn this report into slides", or any request to render a single document as shareable narrative slides.

Overview

Publisherthedotmack
Repositoryclaude-mem
Skill namewowerpoint
Stars
94.1K
Forks
8.3K
Bundled files
Instructions only
LicenseApache-2.0
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 thedotmack on GitHub. Read the source before you install it.

Installation

Install the Wowerpoint 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/thedotmack/claude-mem.git /tmp/claude-mem
mkdir -p .claude/skills
cp -r /tmp/claude-mem/plugin/skills/wowerpoint .claude/skills/wowerpoint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wowerpoint 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 Wowerpoint 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 Wowerpoint 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.

Wowerpoint

One doc in, one PDF out. Slide-deck only — videos and podcasts from the same engine are noticeably worse and out of scope; refer the user to the notebooklm CLI directly if they want those.

Triggers

  • "Wowerpoint "
  • "Make a slide deck about "
  • "Turn this report into slides"
  • "Kawaii-deck this"

Setup (one-time per machine)

If notebooklm auth check returns 0 and command -v jq resolves, skip.

bash
uv tool install --with playwright --force notebooklm-py
$(uv tool dir)/notebooklm-py/bin/playwright install chromium

jq is required by the workflow's JSON parsing; install if missing (brew install jq on macOS, or your distro's package manager).

Then the user authenticates interactively — do not script. Tell them to type ! notebooklm login so the OAuth ENTER lands in their terminal.

Workflow

1. The source doc

You need exactly one source doc. If it doesn't exist or is too thin to carry a deck, write it first — use mem-search and sequential thinking to make it comprehensive (long-form, narrative, several thousand words is normal). Do not paper over a weak source by adding more sources.

2. Auth pre-flight

bash
notebooklm auth check 2>&1 | tail -5

Exit 1 with Run 'notebooklm login' to authenticate. = halt and tell the user.

3. Create notebook, add the source

bash
NOTEBOOK_ID=$(notebooklm create "<title>" --json | jq -r .notebook.id)
SOURCE_ID=$(notebooklm source add "<doc-path>" --notebook "$NOTEBOOK_ID" --json | jq -r .source.id)

Title: H1 of the source doc, or its filename stem; append a date for dated work.

JSON envelope keys differ — create.notebook.id, source add.source.id, generate.task_id. Wrong key = empty string = silent downstream failure.

4. Spawn the subagent

Generation takes ~10 minutes; never block on it. Use the template below with run_in_background: true.

5. End your turn

Print the notebook URL so the user can watch live:

text
https://notebooklm.google.com/notebook/<NOTEBOOK_ID>

The subagent's completion notification fires when the file is on disk.

Output path

Adjacent to the source, parallel filename:

text
<source-dir>/<source-stem>-slides.pdf

If the source isn't somewhere that makes sense as an output location, default to reports/<stem>-slides.pdf.

Share link (WOWerpoint Server)

After the PDF lands on disk, the subagent also POSTs it to the WOWerpoint Server, which converts the 16:9 deck into a 9:16 mobile twin and returns a share URL. The share URL is the primary deliverable to the user; the PDF on disk is the backup.

Required env (exported in the user's shell — the subagent inherits the parent's environment, so plain export is enough; no dotenv loader runs):

bash
WOWERPOINT_API_BASE=https://wowerpoint-api.<subdomain>.workers.dev
WOWERPOINT_VIEWER_BASE=https://wowerpoint-viewer.<subdomain>.workers.dev
WOWERPOINT_UPLOAD_TOKEN=<token>

If any var is missing, skip the share-link step and just hand the PDF over.

Upload pattern (run AFTER the subagent confirms the PDF exists on disk). Capture the full response so empty id and error payloads are handled — jq -r '.id' returns the literal string null on a missing key, so always pipe through .id // empty:

bash
if [ -n "$WOWERPOINT_API_BASE" ] && [ -n "$WOWERPOINT_UPLOAD_TOKEN" ] && [ -n "$WOWERPOINT_VIEWER_BASE" ]; then
  UPLOAD_JSON=$(curl -sS --connect-timeout 10 --max-time 30 -X POST "$WOWERPOINT_API_BASE/api/decks" \
    -H "Authorization: Bearer $WOWERPOINT_UPLOAD_TOKEN" \
    -F "file=@<OUTPUT_PATH>" \
    -F "title=<TITLE>")
  DECK_ID=$(printf '%s' "$UPLOAD_JSON" | jq -r '.id // empty')
  API_ERROR=$(printf '%s' "$UPLOAD_JSON" | jq -r '.error // empty')
  if [ -n "$API_ERROR" ] || [ -z "$DECK_ID" ]; then
    echo "WOWerpoint upload warning: ${API_ERROR:-missing id}"
  else
    echo "Share URL: $WOWERPOINT_VIEWER_BASE/$DECK_ID"
  fi
fi

The returned id is a kebab-case slug derived from the title with a random creature suffix (e.g. tokenrouter-quest-hawk, or velvet-comet-tiger if the title is empty or non-ASCII). The share URL is:

text
$WOWERPOINT_VIEWER_BASE/<id>

It works immediately (shows a "still converting…" page that auto-reloads when ready). Conversion takes ~1–2 min per slide. Print the share URL in your final response.

The prompt

One sentence. Default:

text
Use kawaii characters to tell the story of <subject>. Keep it warm and clear.

Replace <subject> with a one-phrase description from the source doc's H1 or the user's framing. If the user supplies their own prompt, pass it through verbatim — don't expand it.

Subagent template (copy-paste, parameterize)

text
You're handling NotebookLM slide-deck generation. Work in `<repo-absolute-path>`.

Context:
- The `notebooklm` CLI is installed and authenticated (parent verified with `notebooklm auth check`).
- A notebook and source already exist.

Inputs:
- Notebook ID: `<NOTEBOOK_ID>`
- Source ID: `<SOURCE_ID>`
- Generation prompt: `<PROMPT>`
- Output path: `<OUTPUT_PATH>`
- Deck title: `<TITLE>` (the notebook title, used by the share-link step)

Steps:

1. Wait for source: `notebooklm source wait <SOURCE_ID> -n <NOTEBOOK_ID> --timeout 600`
   Exit 0 = ready, 1 = error, 2 = timeout. On timeout, run `notebooklm source list -n <NOTEBOOK_ID> --json` and report status.

2. Generate: `notebooklm generate slide-deck "<PROMPT>" --format detailed --length default --notebook <NOTEBOOK_ID> --json --retry 2`
   Parse `task_id` from the JSON (key is `task_id` at top level).
   On `GENERATION_FAILED` or "No result found for RPC ID": sleep 300, retry once, then give up.

3. Wait for artifact: `notebooklm artifact wait <task_id> -n <NOTEBOOK_ID> --timeout 1800`

4. Download: `notebooklm download slide-deck <OUTPUT_PATH> -a <task_id> -n <NOTEBOOK_ID>`

5. Verify: `ls -la <OUTPUT_PATH>` confirms the file exists.

6. Upload to WOWerpoint Server for a mobile share link. Skip silently if any of `WOWERPOINT_API_BASE`, `WOWERPOINT_UPLOAD_TOKEN`, or `WOWERPOINT_VIEWER_BASE` is unset. Otherwise:

   ```bash
   if [ -n "$WOWERPOINT_API_BASE" ] && [ -n "$WOWERPOINT_UPLOAD_TOKEN" ] && [ -n "$WOWERPOINT_VIEWER_BASE" ]; then
     UPLOAD_JSON=$(curl -sS --connect-timeout 10 --max-time 30 -X POST "$WOWERPOINT_API_BASE/api/decks" \
       -H "Authorization: Bearer $WOWERPOINT_UPLOAD_TOKEN" \
       -F "file=@<OUTPUT_PATH>" \
       -F "title=<TITLE>")
     DECK_ID=$(printf '%s' "$UPLOAD_JSON" | jq -r '.id // empty')
     API_ERROR=$(printf '%s' "$UPLOAD_JSON" | jq -r '.error // empty')
     if [ -n "$API_ERROR" ] || [ -z "$DECK_ID" ]; then
       echo "WOWerpoint upload warning: ${API_ERROR:-missing id}"
     else
       echo "Share URL: $WOWERPOINT_VIEWER_BASE/$DECK_ID"
     fi
   fi

On warning, the PDF on disk is still a valid deliverable — do not retry the upload.

Report briefly (under 200 words):

  • Final artifact ID
  • Time per phase (source wait, generation, render wait, download)
  • Output file path + size
  • Share URL (if produced)
  • Any retries or warnings
  • Exact error message if any step failed

Do NOT poll status manually. The wait commands handle backoff.


## Failure modes

- **`pip: command not found`** — modern macOS doesn't ship pip on PATH. Use `uv tool install`.
- **`Playwright not installed`** — install `notebooklm-py` with `--with playwright`, then `playwright install chromium`.
- **`Run 'notebooklm login' to authenticate`** — only the user can complete OAuth.
- **`task_id` parsed as empty string** — wrong JSON envelope key. `generate` returns `{"task_id": "..."}` at top level.
- **Rate-limit (`GENERATION_FAILED` or "No result found for RPC ID")** — `--retry 2` handles transients; persistent failure means wait 5–10 minutes or fall back to the web UI.
- **Source upload denied for sensitive docs** — confirm before adding sources containing credentials, customer data, or unreleased product info. NotebookLM is a Google service.
- **`--length long` does not exist** — only `default|short`. If the user asks for "long slides," use `default` and explain.
- **No `--style` flag** — kawaii lives in the prompt text.

## Operational tips

- **Rerun cheaply** — once the notebook + source exist, regenerating with a different prompt only repeats generation + download. Reuse `NOTEBOOK_ID` and `SOURCE_ID`.
- **Web UI fallback** — if generation is rate-limited >30 minutes, open the notebook URL, trigger generation in the UI, then `notebooklm artifact list -n <NOTEBOOK_ID>` and `download`.

Frequently asked questions

What does the Wowerpoint AI skill do?

Turn one document into a kawaii NotebookLM slide-deck PDF. Use for "wowerpoint this", "make a deck about <file>", "turn this report into slides", or any request to render a single document as shareable narrative slides.

Why use Wowerpoint on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thedotmack/claude-mem/tree/main/plugin/skills/wowerpoint. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wowerpoint?

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 Wowerpoint?

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

Is the Wowerpoint AI skill free?

Yes. It is published on GitHub by thedotmack under the Apache-2.0 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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