social-notebooklm-artifacts

name

social-notebooklm-artifacts

description

Turn an existing NotebookLM notebook into video assets — a bulleted SCALETTA (focal-point talking points, NOT a verbatim script) AND a 2×2 matrix of 4 Italian infographics (simple+detailed × landscape+portrait). Content comes ENTIRELY from the notebook's sources (scaletta via notebooklm ask; image concepts derived from the notebook too) — you pass nothing, no persona. No citation numbers. By default produces long+short scaletta AND 4 PNGs, in Italian, under a chronological folder <YYYY-MM-DD> - <notebook-title>/ inside a base dir (default /Volumes/Crucial-4T/social, override with --out). Use when invoked as /social-notebooklm-artifacts [<notebook>] [--long | --short] [--script-only | --images-only] [--out <dir>], or asked to turn a NotebookLM project into a video script/scaletta and/or infographics. Agent-independent (Claude Code or Codex). Pairs with social-new-notebooklm-project (which builds the notebook).

social-notebooklm-artifacts

Turn an existing NotebookLM notebook into video assets:

1. A scaletta — a bulleted list of focal points / talking points to glance at while filming and expand in your own words (not a verbatim teleprompter script).
2. A 2×2 infographic matrix — exactly 4 Italian PNGs: simple_landscape, detailed_landscape, simple_portrait, detailed_portrait. Simple = one concept, huge text, mobile-readable B-roll; detailed = richer/accurate, reusable as overlays/reference. Landscape for normal videos, portrait for Shorts/Reels.

Both are grounded entirely in the notebook's own sources — the scaletta via notebooklm ask, the 4 image concepts derived from the notebook the same way. Do not supply a topic, persona, or voice file.

Companion to social-new-notebooklm-project (which researches a topic and builds the notebook). This skill is the next step: notebook → assets.

Input

Invoked as /social-notebooklm-artifacts [<notebook>] [--long | --short] [--script-only | --images-only] [--out <dir>].

• <notebook> — optional. Notebook id or title (partial match works). If omitted, use the active notebook (notebooklm status). If neither resolves, halt: social-notebooklm-artifacts: no notebook — pass an id/title or run 'notebooklm use <id>' first.
• --long / --short — optional. Limit the scaletta to one length. Default: both. (Ignored under --images-only.)
• --script-only / --images-only — optional, mutually exclusive. Restrict to one artifact class. Default (neither): generate scaletta(s) AND the 4 infographics.
• --out <dir> — optional. Base output folder. Default /Volumes/Crucial-4T/social. ~ is expanded. Also accepts --out=<dir>.

The per-run folder is named <YYYY-MM-DD> - <notebook title> (today's date first) under the base, so folders sort chronologically.

Run

Run the bundled script from this skill directory:

cd skills/social-notebooklm-artifacts
scripts/scaletta.sh [<notebook id|title>] [--long|--short] [--script-only|--images-only] [--out <dir>]

The script implements the full stable flow: preflight, notebook resolution, availability probe, scaletta ask generation (--quiet, --timeout 180, retry/backoff, Python citation stripping), then image generation (derive 4 concepts → images.py), Markdown/PNG writing, and summary. Output is written under <out-base>/<YYYY-MM-DD> - <notebook-title>/:

• script-long.md and/or script-short.md (scaletta)
• simple_landscape.png, detailed_landscape.png, simple_portrait.png, detailed_portrait.png (infographics)

(default <out-base> = /Volumes/Crucial-4T/social; override with --out).

Infographics (the 4-image 2×2 matrix)

The notebooklm CLI exposes no infographic generation (only artifact management), so image generation runs the notebooklm-py library under a dedicated venv. The script invokes images.py with $NBLM_PYTHON (default ~/.venvs/nblm/bin/python; override via the NBLM_PYTHON env var). If that interpreter is missing or can't import notebooklm, the script halts with the install command — so a missing venv fails fast before any generation.

Flow:

1. Derive concepts — one notebooklm ask pulls the 4 most important visual concepts from the notebook's sources, as TITOLO :: dettaglio lines (Italian, no numbering/citations). Concepts are never hardcoded — they fit whatever the notebook is about.
2. Generate paced — images.py produces the 2×2 matrix: same PROFESSIONAL style across all 4 for a consistent channel look; the only knobs separating quadrants are orientation (landscape/portrait) and detail_level (simple=CONCISE + one-concept hard instructions; detailed=DETAILED + "completa e accurata"). All instructions are Italian.
3. Quota-safe — free tier is ~8 generations / rolling-24h. Budget-capped at 4/run (NBLM_BUDGET), spaced (NBLM_SPACING, default 60s), exponential backoff on rate-limit refusals (NBLM_BACKOFF_START→NBLM_BACKOFF_MAX, NBLM_MAX_RETRIES). A refused/quota-exhausted run stops cleanly and lists what didn't generate so you retry tomorrow — it never blind-retries past the cap.

Watermark "Made with NotebookLM" is burned in server-side; crop/cover it in your editor.

Format Rules

• Italian, first person framing, bullet list of focal points. Each top bullet is one short concept, speakable from memory.
• Each focal point is developed over multiple sub-bullets so there's enough material to speak from without inventing — not a bare headline. Sub-bullets stay short, speakable phrases (not paragraphs): rich but lean, no walls of text.
• A real example is never bare: say what it is, the number/source if any, and why it proves the point.
• Every technical term, acronym, law, theory, proper noun, or specialist concept is glossed in plain Italian the first time it appears, as a short aside ("X, cioè …") — assume the viewer knows nothing about the topic, so the speaker can explain it from scratch. Never drop a bare name, acronym, or term. (Domain-agnostic: the glossed terms come from whatever the notebook is about.)
• Long: bold section titles, 3–4 focal points per section, each with 2–3 sub-bullets — (a) the mechanism/definition, (b) a contextualized example or data point, (c) optional: why it matters / the consequence. HOOK section, final CTA.
• Short: 1 HOOK bullet, 3–4 substance bullets, 1 CTA bullet; each substance bullet gets 2 sub-bullets — one explaining the concept, one with a contextualized example/data point.
• No citation numbers ([1], [1-3], etc.), no chatty preamble, no b-roll or production jargon.

Each file header is:

# <title> — scaletta <long|short>
<!-- focal points + contesto · NotebookLM (solo fonti) · <iso-date> -->

Prompts

Long:

Scrivi una SCALETTA per un video YouTube in italiano come ELENCO PUNTATO di punti da dire a braccio con parole mie (NON un testo da leggere parola per parola). Usa solo le informazioni nelle fonti di questo notebook; non inventare nulla. Organizza in sezioni: ogni sezione un titolo breve in grassetto; sotto, 3-4 PUNTI CHIAVE come bullet (max ~12 parole, il concetto). SOTTO OGNI punto chiave aggiungi 2-3 SOTTO-BULLET indentati che SVILUPPANO il concetto su piu righe, così da avere abbastanza materiale davanti agli occhi per parlarne senza inventare: (a) un sotto-bullet che SPIEGA il meccanismo o la definizione (come/perché funziona), (b) un sotto-bullet con un ESEMPIO o un DATO CONCRETO ben contestualizzato — non l'esempio nudo: di' cos'è, il numero/la fonte se c'è, e perché dimostra il punto, (c) facoltativo: perché conta o quale conseguenza ha. REGOLA FONDAMENTALE: ogni termine tecnico, sigla, legge, teoria, nome proprio o concetto specialistico va SPIEGATO in parole semplici la prima volta che compare, con un breve inciso ('X, cioè ...' / 'X, ovvero ...'), come se chi guarda non sapesse nulla dell'argomento. Non lasciare mai un nome, una sigla o un termine nudi: io devo poterli spiegare a voce partendo da zero. Ogni sotto-bullet è una frase breve e parlabile (max ~22 parole), non un paragrafo: spunti ricchi ma asciutti, niente muri di testo. Il bullet principale è il concetto; i sotto-bullet sono il contesto su cui mi appoggio mentre parlo. Includi una sezione HOOK iniziale e una CTA finale. NIENTE numeri di citazione [1]/[1-3]. Niente frase introduttiva, niente regia.

Short:

Scrivi una SCALETTA per un Reel/Short verticale in italiano come ELENCO PUNTATO da dire a braccio con parole mie (non un testo da leggere). Solo dalle fonti del notebook, niente invenzioni. Formato: 1 bullet HOOK, 3-4 bullet di sostanza, 1 bullet CTA; per ogni bullet di sostanza aggiungi 2 SOTTO-BULLET indentati brevi: uno che SPIEGA il concetto in una frase, uno con un ESEMPIO o DATO contestualizzato (cos'è + il numero/perché conta, non l'esempio nudo). REGOLA FONDAMENTALE: ogni termine tecnico, sigla, legge, teoria, nome proprio o concetto specialistico va SPIEGATO in parole semplici con un breve inciso ('X, cioè ...'), come se chi guarda non sapesse nulla dell'argomento. Mai un nome, una sigla o un termine nudi: io devo poterli spiegare a voce da zero. Sotto-bullet parlabili (max ~20 parole), un concetto per bullet. NIENTE numeri di citazione [1]/[1-3]. Niente frase introduttiva, niente regia.

Known failure modes

Symptom	Cause	Handling
report text won't save locally	generate report body not exposed by CLI; artifact export is Docs/Sheets-only	use notebooklm ask and capture the answer field (Step 2)
Unknown language code: Italian	language wants a code	use it (notebooklm language list); the ask prompt itself also requests Italian
long script cut off	chat answer length cap	stage it: outline → one ask per beat → concatenate (Step 3)
NETWORK_ERROR: chat.ask timed out after retries	flaky/soft-rate-limited NotebookLM backend (NOT a fixable timeout)	--timeout 180 + ask_to retry/backoff; availability probe first; halt cleanly if the backend is down
empty/KeyError: answer when parsing	status line on stdout, or an error JSON	always --quiet; parse with .get('answer') and treat missing as failure (retry)
want to know "API calls left"	notebooklm-py exposes no quota/usage endpoint	not possible to query; the cheap ask "ok" probe is the only availability signal
._script-*.md files appear	macOS AppleDouble metadata on external/exFAT volumes	harmless; ignore (or dot_clean the folder)
[1]/[1-3] numbers or "Ecco… :" preamble in output	NotebookLM adds citations + a lead-in even when told not to	strip_cites post-process removes both before writing (Step 3) — never ship raw ask text
user reads verbatim, loses his place	prose paragraphs are unspeakable	output is a bulleted scaletta of short focal points, not a verbatim script (the whole point)
bare headlines, nothing to say	focal points with no supporting detail	each top bullet gets 1–2 context sub-bullets (data/example/term) to speak from
sed: extra characters at the end of d command	BSD/macOS sed can't do 1{/…/d}	do citation+preamble strip in Python, not sed (Step 3 writer)
wrong notebook scripted	ambiguous/unset active notebook	Step 1 resolves + confirms via notebooklm status before generating
output base on an unmounted /Volumes/*	external volume not mounted	mkdir -p fails → script detects the missing volume root and halts "<vol> not mounted"
want folders sorted by date	flat title-only folders	folder name is <YYYY-MM-DD> - <title>; base dir overridable via --out
notebooklm: No such command 'infographic'	CLI exposes artifact management only, not generation	image step uses the notebooklm-py library via $NBLM_PYTHON, not the CLI
notebooklm library not importable	venv missing / lib not installed	uv venv --python 3.13 ~/.venvs/nblm && uv pip install --python ~/.venvs/nblm "notebooklm-py[browser]", or set NBLM_PYTHON
infographic text too small / too busy	NotebookLM auto-fits the whole topic into one poster	scope is the dial: simple quadrants use one concept + CONCISE + hard Italian "testo ENORME, max 3 elementi" instructions
RateLimitError / "Resource exhausted" mid-batch	~8 gen / rolling-24h free-tier quota	budget-capped at 4/run + spacing + backoff; stops cleanly, lists ungenerated quadrants, retry tomorrow — never blind-retries
only some of the 4 PNGs appear	quota hit partway, or a quadrant refused	summary lists IMAGES_DONE vs failed; rerun --images-only next day for the rest
empty task_id then a bogus duplicate PNG	a refused generate returns empty id; download-latest then grabs a stale artifact	is_refusal() guard in images.py treats empty task_id as a failure, never downloads