Wright Recipes
Turn recipe web pages into scaled shopping lists, costs and nutrition β π³ Paid API required
- What
- Turn recipe web pages into scaled shopping lists, costs and nutrition β π³ Paid API required
- Cost
- Free
- Needs
- a Python-capable environment with uvx; your own LLM API key (OpenAI, Anthropic, or Gemini) for the recipe-parsing step β scaling, shopping lists, costing and nutrition run locally and free. Store the key in the secure vault, never paste it into chat.
- Install
- Copy the installer prompt below into your Muse β your agent does the rest.
π³ Paid API required β Curated by Skill Harbor β a recipe-operations skill built on the `wright-core` Python library: it parses a recipe web page into a validated structured `Recipe`, then deterministically scales servings, consolidates multiple recipes into one shopping list (merging ingredient variants like kosher/table salt), costs recipes against your purchase history, detects allergens and dietary badges, and analyzes nutrition. Design principle: extraction is probabilistic (LLM), planning is deterministic (testable library code) β the boundary between the two is where you review. Discovered via skills.sh, listed here with credit to its creator by @3pm-baking. Honest caveats: π³ the `parse` step needs your own LLM API key (OpenAI, Anthropic or Gemini) β scaling, shopping lists and costing run free and locally; drives the CLI via `uvx` (Python + network to fetch the page); does not discover recipes on its own β supply URLs or confirm candidates first. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Not verified.
Version:
Install
Prerequisites: a Python-capable environment with uvx; your own LLM API key (OpenAI, Anthropic, or Gemini) for the recipe-parsing step β scaling, shopping lists, costing and nutrition run locally and free. Store the key in the secure vault, never paste it into chat. Install "Wright Recipes" for me. A recipe-operations skill on top of the wright-core library: parse recipe web pages into validated structured recipes, scale servings, build consolidated shopping lists, cost recipes, detect allergens, and analyze nutrition. Repository: https://github.com/3pm-baking/wright 1. Fetch the SKILL.md file for the wright-recipes skill (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via the secure vault, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "wright-recipes". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. save my LLM API key in the vault, supply recipe URLs when I want them parsed). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you β no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the sellerβs own page, usually Stripe.
What does the β next to a creatorβs name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself β always check a build before installing it.