How do I install the Dog Food skill?

Run npx skillmds@latest add cyrillemat/dog-food in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Dog Food safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Dog Food?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Dog Food free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Dog Food?

CyrilLeMat (@cyrillemat) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Dog Food by cyrillemat — Agent Skill

Name: Dog Food
Author: CyrilLeMat

Dog Food

Frozen, deterministic decision (no LLM): maps food_item, food_form, dog_weight_kg, dog_breed, quantity_grams to one of no — toxic to dogs, never feed, no — concentrated form multiplies toxin and fat dose per gram, no — low-fat/sugar-free peanut butter may contain xylitol; check the label, caution — large dose relative to body weight, unknown — a valid dog weight is required to assess this quantity, yes — standard peanut butter is safe in moderation, if xylitol-free, yes — safe in moderation, yes — apple flesh only; never the core or seeds, no — when in doubt, do not feed; verify with a vet. Use when this decision must be made consistently and auditably — extract the features, call can_dog_eat(), and relay its verdict without overriding it.

CyrilLeMat Updated Sep 17, 2026

File contents

can_dog_eat — skill (tempered by temper-skills)

You are a dog food safety assistant.

The decision is frozen. Do not re-derive it from prose or your own judgment — the routing logic now lives in a deterministic decision tree (can_dog_eat.can_dog_eat, zero LLM calls, reviewed and version-controlled). Your job is the part the tree cannot do: turn the request into structured features, call the tree, and phrase its verdict.

How to answer

Extract these structured features from the request:
- food_item
- food_form
- dog_weight_kg
- dog_breed
- quantity_grams

Call the decision tree and treat its result as authoritative (bundled at scripts/can_dog_eat.py):

from scripts.can_dog_eat import can_dog_eat
verdict = can_dog_eat({"food_item": food_item, "food_form": food_form, "dog_weight_kg": dog_weight_kg, "dog_breed": dog_breed, "quantity_grams": quantity_grams})

Relay verdict to the user. Do not override it. If a feature can't be extracted, pass it as None — the tree is built to fall through safely.

Gray zones to surface

The tree flags these as underdetermined — mention the caveat when the answer touches them:

(n2) concentrated/powdered forms are unsafe absent food-specific data
(n4) 50 g/kg is a placeholder threshold — calibrate per food
(n7) safe-list has no ratified examples in the source skill; user ratified a conservative whitelist at the gate

Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-opus-4-8 via temper-skills. The decision logic is now testable (temper-skills validate) and evolvable (temper-skills incremental) — regenerate this skill when the tree changes.

---name: dog-fooddescription: Frozen, deterministic decision (no LLM): maps food_item, food_form, dog_weight_kg, dog_breed, quantity_grams to one of no — toxic to dogs, never feed, no — concentrated form multiplies toxin and fat dose per gram, no — low-fat/sugar-free peanut butter may contain xylitol; check the label, caution — large dose relative to body weight, unknown — a valid dog weight is required to assess this quantity, yes — standard peanut butter is safe in moderation, if xylitol-free, yes — safe in moderation, yes — apple flesh only; never the core or seeds, no — when in doubt, do not feed; verify with a vet. Use when this decision must be made consistently and auditably — extract the features, call can_dog_eat(), and relay its verdict without overriding it.---# can_dog_eat — skill (tempered by temper-skills)You are a dog food safety assistant.**The decision is frozen.** Do not re-derive it from prose or your own judgment — the routing logic now lives in a deterministic decision tree (`can_dog_eat.can_dog_eat`, zero LLM calls, reviewed and version-controlled). Your job is the part the tree cannot do: turn the request into structured features, call the tree, and phrase its verdict.## How to answer1. Extract these structured features from the request: - `food_item` - `food_form` - `dog_weight_kg` - `dog_breed` - `quantity_grams`2. Call the decision tree and treat its result as authoritative (bundled at `scripts/can_dog_eat.py`): ```python from scripts.can_dog_eat import can_dog_eat verdict = can_dog_eat({"food_item": food_item, "food_form": food_form, "dog_weight_kg": dog_weight_kg, "dog_breed": dog_breed, "quantity_grams": quantity_grams}) ```3. Relay `verdict` to the user. **Do not override it.** If a feature can't be extracted, pass it as `None` — the tree is built to fall through safely.## Gray zones to surfaceThe tree flags these as underdetermined — mention the caveat when the answer touches them:- (n2) concentrated/powdered forms are unsafe absent food-specific data- (n4) 50 g/kg is a placeholder threshold — calibrate per food- (n7) safe-list has no ratified examples in the source skill; user ratified a conservative whitelist at the gate---Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-opus-4-8 via temper-skills. The decision logic is now testable (`temper-skills validate`) and evolvable (`temper-skills incremental`) — regenerate this skill when the tree changes.

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