Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/mpaarating/ai-workflow-kit/recipenpx skills add mpaarating/ai-workflow-kit --skill recipegit clone --depth 1 https://github.com/mpaarating/ai-workflow-kitWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00017 | $0.00716 |
| Opus 5 | $0.00009 | $0.00358 |
| Sonnet 5 | $0.00003 | $0.00143 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
recipe scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recipe
Save recipes from anywhere — URLs, pasted text, or just telling the AI what you made. Search by ingredient or cuisine later.
Trigger Phrases
- "recipe:"
- "save recipe"
- "add recipe"
- "cooking:"
Workflow
Saving from a URL
- Fetch the page and extract the recipe, stripping ads and life-story preamble
- Parse into structured fields (see format below)
- Save and confirm
Saving from text or dictation
- Parse the provided text into structured recipe format
- Ask for any missing required fields (title, ingredients, steps)
- Save and confirm
Recipe Format
Every saved recipe includes:
- Title
- Cuisine: Italian, Mexican, Thai, Indian, American, Japanese, etc.
- Meal type: Breakfast, Lunch, Dinner, Snack, Dessert
- Difficulty: Easy, Medium, Hard
- Prep time / Cook time
- Servings
- Ingredients: Listed with quantities
- Steps: Numbered instructions
- Source: URL or "personal" if from text/dictation
- Notes: Optional — substitutions, tips, tweaks
Searching recipes
When the user asks to find a recipe (e.g., "what can I make with chicken?", "any Italian recipes?"):
- Search saved recipes by ingredient, cuisine, meal type, or keyword
- Return a short list with title, cuisine, prep time, and difficulty
- Offer to show the full recipe for any match
Scaling servings
When asked to scale (e.g., "double that recipe", "make it for 6"):
- Calculate the scaling factor from current to desired servings
- Adjust all ingredient quantities
- Display the scaled ingredients list
- Note: cooking times may need adjustment for significantly different quantities
Storage
Using {{notes}}: Store each recipe as a page with the fields above.
Markdown fallback: Save to ~/.ai-workflow/recipes/ as individual markdown files, named by slugified title (e.g., chicken-tikka-masala.md).
Examples
Save from URL:
Saved: "Spaghetti Carbonara"
Italian | Dinner | Easy | 25 min | Serves 4
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 99 lines · 17 tokens per session scan A e0ccf97581c4
recipe is a skill published in the GitHub repository mpaarating/ai-workflow-kit (2 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 716 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
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brainstorming
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auto-perf-optimize
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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