mise-en-place 0.2.1

Extract structured recipes from YouTube cooking videos with Claude, stored in local SQLite.

Mise En Place (mep)

A personal CLI that turns YouTube cooking videos into a searchable recipe database. It pulls a video's transcript, extracts a structured recipe with Claude, and stores it in local SQLite. Everything stays on your machine.

Install

git clone https://github.com/AveryClapp/MiseEnPlace.git mep && cd mep
python -m venv .venv && source .venv/bin/activate
pip install -e .

Or simply

pip install mise-en-place

Setup

mep init

This creates ~/.mep/, prompts for the keys, and builds the database at ~/.mep/mep.db.

• An LLM key (required for extraction): set ANTHROPIC_API_KEY (https://console.anthropic.com/ → API Keys) or OPENAI_API_KEY. For OpenAI, also install the extra: pip install 'mise-en-place[openai]'.
• YouTube Data API v3 key (only needed for --channel ingestion): see below.

Provider selection: if you set only one of the two LLM keys, that provider is used automatically. If you set both, it defaults to Anthropic; set LLM_PROVIDER=openai (or choose it at the mep init prompt) to pick OpenAI. An explicit LLM_PROVIDER always wins.

Keys are stored in ~/.mep/config.json. You can also set ANTHROPIC_API_KEY, OPENAI_API_KEY, LLM_PROVIDER, or YOUTUBE_API_KEY as environment variables, which override the config file. EXTRACTION_MODEL overrides the default model for the chosen provider.

Getting a YouTube Data API v3 key

1. Go to the Google Cloud Console.
2. Create a project (top bar → project dropdown → New Project).
3. In the search bar, open YouTube Data API v3 and click Enable.
4. Go to APIs & Services → Credentials → Create Credentials → API key.
5. Copy the key. (Optional: Edit API key → Restrict key → YouTube Data API v3.)

Single-video adds use YouTube's public oEmbed endpoint and do not need this key. It is only required to walk a channel's uploads.

Usage

mep add https://www.youtube.com/watch?v=VIDEO_ID    # one video
mep add --channel @JKenjiLopezAlt --limit 10        # latest 10 from a channel
mep add --channel @JKenjiLopezAlt                    # whole channel

mep search "garlic confit"                           # full-text search
mep list                                             # browse, newest first
mep list --tag italian --limit 20                    # filter by tag
mep show 42                                           # full recipe
mep show 42 --servings 8                              # scale ingredient amounts
mep show 42 --macros                                 # estimated nutrition breakdown
mep set-servings 42 4                                 # record how many it makes

mep plan 42                                          # AI cooking timeline (experimental)
mep plan 42 --servings 8                             # ...scaled to 8 servings
mep cook 42                                          # step-by-step walkthrough (experimental)

mep show 42 --parts                                  # what each ingredient is for
mep adapt 42                                         # rewrite around what you have (interactive)
mep adapt 42 --have pita --sub "yogurt=sour cream"   # ...or state it directly
mep cook 42 --have pita                              # adapt just for this cook

plan makes one Claude call to reorder a recipe's steps into an efficient timeline and caches it. Each step is tagged hands-on or hands-off, with the ingredients and equipment it uses, a named timer for waits, and a "prep this during the wait" hint. The summary shows realistic wall-clock time (hands-off waits run in the background, not added end to end). Re-run plan --regenerate to rebuild.

cook walks that timeline live: it opens with a mise en place gather + equipment list, then one step at a time. On a hands-off step, pressing Enter starts a named background timer that keeps counting while you move on to the next step (like a real kitchen timer); it rings when done. It also nudges you to preheat the oven a couple steps ahead. Ctrl-C stops cleanly and reports any timers still running.

--servings N (on show, plan, cook) scales ingredient amounts to N servings. It is best-effort and display-only: only leading quantities are scaled, vague amounts like "a handful" pass through untouched, and nothing is saved. A recipe with no recorded serving count is treated as a single serving (the batch as written), so --servings 3 simply makes 3× the recipe. Use mep set-servings <id> <count> to record the real serving count when you know it, so scaling maps to people instead.

Both plan and cook are experimental: the timings are AI estimates.

show --macros shows an estimated nutrition breakdown (calories, protein, carbs, fat) for the whole recipe and per serving. It's computed lazily on first request (one model call, then cached, so it's free afterward and costs nothing if you never ask) and is a rough estimate from the ingredients, not exact.

show --parts breaks a recipe into its components (marinade, pita, sauce…) so you can see what each ingredient is for. adapt rewrites the recipe around what you already have: pick the parts you bought or made ahead and it drops the steps and ingredients needed only to make those (keeping the steps that use them), and applies any ingredient swaps. It then offers to save the result as a new copy, overwrite the original, or discard it. cook --have/--sub does the same rewrite in memory for a single cook without saving anything. These are experimental and use Claude; the rewrite is intentionally light (it shifts and trims the recipe, it doesn't reinvent it).

Channel ingestion is idempotent: videos already stored are skipped, so you can re-run it to pick up only what's new. Non-recipe videos and videos without transcripts are stored as empty entries (not errors) so they aren't re-fetched.

Cost per video empirically is around $0.05, maybe more depending on length.

Channels to try

Recipe-forward channels that work well (most videos are real walkthroughs with transcripts). Single-video adds need no key; the --channel walk needs a YouTube Data API key (see above).

Channel	Handle
Babish Culinary Universe	@babishculinaryuniverse
J. Kenji López-Alt	@JKenjiLopezAlt
Joshua Weissman	@joshuaweissman
Adam Ragusea	@aragusea
Ethan Chlebowski	@EthanChlebowski
Brian Lagerstrom	@brianlagerstrom
Food Wishes (Chef John)	@foodwishes

mep add https://www.youtube.com/watch?v=iErqWGwso7o   # a single Babish video, no key needed
mep add --channel @aragusea --limit 5                 # latest 5 from Adam Ragusea

How it works

url → transcript (youtube-transcript-api) → an LLM (Claude or OpenAI) → JSON → SQLite. Search uses SQLite FTS5 over dish name, ingredients, and channel. Vague quantities like "a handful" are stored verbatim — nothing is normalized.

Develop

pip install -e '.[dev]'
pytest

The test suite is fully offline (no network, no API keys).