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 GGbond-bo/MemOmics-Agent --skill fitness-nutritiongit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote 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/ggbond-bo/memomics-agent/fitness-nutrition)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/fitness-nutrition"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/fitness-nutrition/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/ggbond-bo/memomics-agent/fitness-nutrition"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/fitness-nutrition.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 | $0.00087 | $0.02983 |
| Opus 5 | $0.00044 | $0.01491 |
| Sonnet 5 | $0.00017 | $0.00597 |
| Haiku 4.5 | $0.00009 | $0.00298 |
Grade A, and why
fitness-nutrition scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
commands: [curl, python3] This is a copy
84% identical to fitness-nutrition — 36 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fitness & Nutrition
Expert fitness coach and sports nutritionist skill. Two data sources plus offline calculators — everything a gym-goer needs in one place.
Data sources (all free, no pip dependencies):
- wger (https://wger.de/api/v2/) — open exercise database, 690+ exercises with muscles, equipment, images. Public endpoints need zero authentication.
- USDA FoodData Central (https://api.nal.usda.gov/fdc/v1/) — US government nutrition database, 380,000+ foods.
DEMO_KEYworks instantly; free signup for higher limits.
Offline calculators (pure stdlib Python):
- BMI, TDEE (Mifflin-St Jeor), one-rep max (Epley/Brzycki/Lombardi), macro splits, body fat % (US Navy method)
When to Use
Trigger this skill when the user asks about:
- Exercises, workouts, gym routines, muscle groups, workout splits
- Food macros, calories, protein content, meal planning, calorie counting
- Body composition: BMI, body fat, TDEE, caloric surplus/deficit
- One-rep max estimates, training percentages, progressive overload
- Macro ratios for cutting, bulking, or maintenance
Procedure
Exercise Lookup (wger API)
All wger public endpoints return JSON and require no auth. Always add
format=json and language=2 (English) to exercise queries.
Step 1 — Identify what the user wants:
- By muscle → use
/api/v2/exercise/?muscles={id}&language=2&status=2&format=json - By category → use
/api/v2/exercise/?category={id}&language=2&status=2&format=json - By equipment → use
/api/v2/exercise/?equipment={id}&language=2&status=2&format=json - By name → use
/api/v2/exercise/search/?term={query}&language=english&format=json - Full details → use
/api/v2/exerciseinfo/{exercise_id}/?format=json
Step 2 — Reference IDs (so you don't need extra API calls):
Exercise categories:
| ID | Category |
|---|---|
| 8 | Arms |
| 9 | Legs |
| 10 | Abs |
| 11 | Chest |
| 12 | Back |
| 13 | Shoulders |
| 14 | Calves |
| 15 | Cardio |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 256 lines · 87 tokens per session scan A 433eb4dd583d
fitness-nutrition is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 87 tokens to every session and 2,983 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 84% identical to fitness-nutrition, differing in 36 lines, and is treated as a copy.
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