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 skills add SkillMedev/health-and-longevity --skill nutrition-plannergit clone --depth 1 https://github.com/SkillMedev/health-and-longevityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/skillmedev/health-and-longevity/nutrition-planner)<a href="https://agentmods.dev/skills/skillmedev/health-and-longevity/nutrition-planner"><img src="https://agentmods.dev/badge/skills/skillmedev/health-and-longevity/nutrition-planner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/skillmedev/health-and-longevity/nutrition-planner"><img src="https://agentmods.dev/badge/skills/skillmedev/health-and-longevity/nutrition-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00152 | $0.01642 |
| Opus 5.5 | $0.00061 | $0.00657 |
| Sonnet 5.5 | $0.00030 | $0.00328 |
| Haiku 4.5 | $0.00015 | $0.00164 |
Grade A, and why
nutrition-planner 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 23d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nutrition Planner
Most diets fail on adherence, not knowledge: targets are set too aggressively, protein is an afterthought, and there is no rule for when and how to adjust. This skill sets defensible targets, wraps them in a repeatable meal structure, and installs a review protocol - so the plan survives month three, not just week one. It sets the numbers; turning them into a specific week of recipes, a shopping list, and a prep schedule is meal-planner's job.
Operating procedure
Order matters: protein is set before calories because it is the highest-leverage macro and it anchors satiety; structure comes last because it exists to deliver the targets, not the reverse.
Step 1: Gather inputs
Collect: body weight (kg or lbs), goal (fat loss, muscle gain, or maintenance), training days per week and type, occupation activity (desk vs on-feet), dietary pattern or restrictions (vegetarian, allergies - allergies are non-negotiable), rough current eating pattern, and past diet attempts with why they broke down. If weight or activity is a guess, label it a guess and use the conservative end of every range. Screen for the medical conditions in Escalation before setting any deficit.
Step 2: Set the protein target
Default 1.6-2.2 g per kg of body weight per day: low end for sedentary individuals, high end for anyone training 3+ days per week. Distribute across at least 3 meals to maximize muscle protein synthesis - a 140 g target is roughly 3 × 45 g, not one 100 g dinner. Anchor foods: eggs, Greek yogurt, cottage cheese, chicken, fish, legumes, tofu. For plant-based users, bias toward the higher end of the range to offset lower per-food density.
Step 3: Set the calorie budget
Estimate maintenance as body weight in lbs × 14-16 (14 for desk workers, 16 for active individuals). Then apply the goal adjustment: fat loss, a 300-500 kcal deficit; muscle gain, a 200-300 kcal surplus; maintenance, no adjustment. Hard red line: never program a deficit beyond 750 kcal/day - it reliably destroys adherence and muscle mass. Whatever calories remain after protein split between carbohydrate and fat according to preference; adherence beats any particular ratio.
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.
- 23d ago Changed · +2 lines · +1 tokens per session bdb75963f0da
- 1mo ago First seen · 78 lines · 151 tokens per session scan A fd3fcbab37e2
nutrition-planner is a skill published in the GitHub repository SkillMedev/health-and-longevity (1 stars, last pushed 23d ago), licensed MIT. It adds 152 tokens to every session and 1,642 once invoked, about $0.0006 per session on Opus 5.5. 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.
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