# Mp Podcast > Creates a personalized educational podcast about a requested topic from the listener's repositories and notes, using NotebookLM or Gemini multi-speaker TTS. - Skill: `martinopolo/mp-podcast` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add martinopolo/mp-podcast` - Raw SKILL.md: https://api.skillmd.com/api/skills/martinopolo/mp-podcast/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: MartinoPolo (https://skillmd.com/u/martinopolo) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/martinopolo/mp-podcast --- # Topic to Personalized Podcast Produce a walk-listenable MP3 that teaches a topic **through the listener's own code**. You research the topic, sweep his repositories and notes, and write a fact-dense source brief; NotebookLM writes the dialogue and renders the audio from that brief. $ARGUMENTS Two artifacts drive everything: a `-resource.md` brief (the only facts the hosts will ever know) and a `-prompt.txt` customize instruction (how they discuss it). Format rules for both: [`reference/BRIEF_FORMAT.md`](reference/BRIEF_FORMAT.md). ## Step 1: Parse the request | Input | Source | Default | | ---------- | ----------------------------------------------- | ------------ | | Topic | everything in `$ARGUMENTS` that is not a flag | required | | Length | `length short`, `length default`, `length long` | `default` | | Work repos | `--include-work` | excluded | | Backend | `--backend` | `notebooklm` | Derive `` as kebab-case from the topic (`Shadow DOM` → `shadow-dom`); it names both artifacts, the final MP3 and the folder it lands in. Resolve machine roots and `MPX_SKILLS_DIR` from the environment with `env | grep '^MPX_'`. A project/content root that comes back unset is unavailable — report it and continue without it. If `MPX_SKILLS_DIR` is unset, that is a blocker because Steps 3 and 5 need its shared protocol and NotebookLM skill; report that the selected skill cannot continue. When `MPX_PROJECTS`, `MPX_CLONED` and `MPX_WORK` are all missing, the terminal predates the variables being set; say so, and note that fixing it takes closing the whole Windows Terminal window. ## Step 2: Preflight the backend ```bash notebooklm auth check --test --json ``` Proceed when `status` is `ok` **and** `checks.token_fetch` is `true`. Anything else means the cookies went stale: run `notebooklm auth refresh`, and if that still fails ask the user to run `notebooklm login` once, then re-check. When `notebooklm` is absent from PATH the shell predates the install: use `$LOCALAPPDATA\Python\pythoncore-3.14-64\Scripts\notebooklm.exe` for this run. ## Step 3: Research and personalization — one message, parallel Send every spawn below in a **single message** so they run concurrently, and orchestrate them from this thread by reading `/mp/skills/shared/SUBAGENT_PROTOCOL.md` section 2. **Topic research.** The brief is built from gathered sources, so gather them first: - Library or framework topic → spawn `mp-context7-docs-fetcher` with the library name and the specific APIs to document. It declares its own model, so pass no `model`. - General topic → spawn `general-purpose` with `model: "sonnet"` to web-search and fetch the authoritative primary sources (spec, MDN, official docs, the canonical blog post), asking for URL + the technical substance of each, current as of today. **Personalization sweep.** Fan out one `Explore` per root, breadth `medium` — raise to `very thorough` on a narrow topic that returns nothing on the first pass. `Explore` pins its own model, so pass no `model`; read `/mp/skills/shared/EXPLORATION.md` section `Never pass model`. | Root | Looking for | | --------------------------------- | ----------------------------------------------- | | `MPX_PROJECTS` | where the topic already appears in his own code | | `MPX_OBSIDIAN_VAULT/Programming/` | his own notes on the topic | | `MPX_CLONED` | how the topic is done well in OSS he studies | | `MPX_WORK` | **only** when `--include-work` was passed | `Explore` skips CLAUDE.md and is one-shot, so each delegation prompt carries everything it needs: the root to search, an instruction to resolve it at runtime with `env | grep '^MPX_'` and to report an unset variable rather than guessing a path, the topic with its concrete API/keyword spellings, and this return shape per hit: > repository · `file:line` · how the topic is used there · idiomatic, outdated, or a latent > bug · one line on what changing it would buy him **Listener profile.** Read these two directly — known paths are not exploration: - `$MPX_OBSIDIAN_VAULT\Programming\Webdev Fundamentals Learning System.md` - `$MPX_OBSIDIAN_VAULT\Programming\Webdev Fundamentals Assessment.md` They give his real level, priority map and known gaps. Derive the listener-context line of the customize prompt from them, and skip the basics they show he already owns. ## Step 4: Write the two artifacts Read [`reference/BRIEF_FORMAT.md`](reference/BRIEF_FORMAT.md) and write both files into the scratchpad directory: - `-resource.md` — 200-250 lines, `## Part 1..N`, direct technical language, syntax spelled out for text-to-speech, every claim traceable to a gathered source. - `-prompt.txt` — the customize instruction, ≤308 words and ≤2263 characters. Count both before saving and trim until they fit. The sweep's evidence becomes the final `## Part N: In Your Own Code`, each claim tied to a real `file:line`, plus a matching numbered topic in the prompt so the hosts actually reach it. When a sweep returns nothing, drop that part and that topic — NotebookLM narrates whatever the brief says as fact, so the brief holds only what the sweep returned. **Privacy.** The brief is uploaded to Google through an unofficial API on a personal account. Keep secrets, `.env` contents and credentials out of it; keep personal-project snippets to about 15 lines; describe work-repo patterns in prose instead of pasting proprietary code. ## Step 5: Generate the audio Follow [`reference/NOTEBOOKLM_FLOW.md`](reference/NOTEBOOKLM_FLOW.md): create notebook → add the brief as a source → wait → `generate audio --prompt-file --format deep-dive --length --json` → background `general-purpose` waiter with `model: "haiku"` → `download audio`. Full CLI surface: `/mp/skills/notebooklm/SKILL.md`. Length maps straight through, and the same choice sets the prompt's closing duration line: | Argument | `--length` | Prompt line | Measured | | --------- | ---------- | ------------------------------ | --------------------------- | | `short` | `short` | `Target duration: 10 minutes.` | — | | `default` | `default` | `Target duration: 15 minutes.` | — | | `long` | `long` | `Target duration: 25 minutes.` | 33.5 min from a 15-min line | Lengths are hints rather than contracts, and overshoot is acceptable. On quota exhaustion (3 audio overviews per day) or repeated generation failure, offer the Gemini TTS backend — you write every spoken line, then `python ${CLAUDE_SKILL_DIR}/scripts/gemini-tts-podcast.py` renders it — or waiting for tomorrow's quota. `--backend gemini-tts` selects it directly. See [`reference/GEMINI_TTS.md`](reference/GEMINI_TTS.md). ## Step 6: Post-process and deliver Every podcast gets its own folder, `$MPX_AI_GENERATED\_PODCASTS\\`. Re-encode to a phone-friendly bitrate straight into it: ```bash mkdir -p "$MPX_AI_GENERATED/_PODCASTS/" ffmpeg -i .mp3 -codec:a libmp3lame -b:a 64k -ac 1 "$MPX_AI_GENERATED/_PODCASTS//.mp3" ``` 64 kbps mono took a 33-minute episode from 62 MB to about 15 MB. Keep the original only when the re-encode lands within 20% of it; otherwise the re-encode is the deliverable. Write two companion files into that same folder, so the audio ships with what produced it: - `script.txt` — the spoken script. Under Gemini TTS that is the dialogue you wrote; under NotebookLM, which writes its own dialogue, it is `-prompt.txt` (the customize instruction the hosts followed) so the folder still records what was asked for. - `sources.md` — the source list fed to NotebookLM: the research URLs gathered, the repository and note `file:line` hits the sweep returned, and the roots skipped as unset. Intermediate work — the raw download, chunk WAVs, temp files — stays in the session scratchpad. Only `.mp3`, `script.txt` and `sources.md` are promoted to the final folder. ## Step 7: Report - MP3 path as a clickable `file:///` link, plus duration and size, and the per-slug folder holding it alongside `script.txt` and `sources.md` - Which backend ran, and the resolved `--length` - Sources the research phase gathered (count and the primary ones) - Personalization: repositories swept, hits found, `file:line` references that made the brief — or a plain statement that the topic appears nowhere in his code yet - Roots skipped because their variable was unset ## Notes - `notebooklm-py` is an unofficial client and using it goes against Google's Terms of Service. It runs under a secondary Google account, which is what keeps the exposure contained. - NotebookLM was renamed "Gemini Notebook" in July 2026. There is no consumer API; the free tier allows 3 audio overviews per day. - Stored cookies expired within a day of the first login, and `auth refresh` did not recover them — it failed with the same "Authentication expired" error. Budget for an interactive `notebooklm login` at the start of a run rather than treating it as an edge case. - English output only for now.