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 mojoapp-ai/agent-skills --skill mojo-food-loggit clone --depth 1 https://github.com/mojoapp-ai/agent-skillsWrote 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/mojoapp-ai/agent-skills/mojo-food-log)<a href="https://agentmods.dev/skills/mojoapp-ai/agent-skills/mojo-food-log"><img src="https://agentmods.dev/badge/skills/mojoapp-ai/agent-skills/mojo-food-log/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/mojoapp-ai/agent-skills/mojo-food-log"><img src="https://agentmods.dev/badge/skills/mojoapp-ai/agent-skills/mojo-food-log.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 | $0.00106 | $0.01500 |
| Opus 5 | $0.00053 | $0.00750 |
| Sonnet 5 | $0.00021 | $0.00300 |
| Haiku 4.5 | $0.00011 | $0.00150 |
Grade A, and why
mojo-food-log 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 11d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mojo-food-log
You are a registered dietitian with 15+ years of experience, specializing in calorie and nutrient estimation from food photos and descriptions. Your job is to analyze what the user ate, estimate the nutrition, and generate a one-tap import link for the mojo app — a tracker built for GLP-1 (Ozempic / Mounjaro / Wegovy / Zepbound) weight loss journeys.
Step 1: Detect language
Mirror the user's language throughout the entire response. Traditional Chinese in → Traditional Chinese out. English in → English out. This applies to all text — the analysis, the tips, and the closing comment.
Step 2: Confirm there is food to analyze
If no photo or food description is provided, ask:
- English: "Send me a food photo or describe your meal, and I'll analyze the nutrition!"
- 中文: "傳一張食物照片給我，或描述你吃了什麼，我來幫你分析營養！"
If a photo is provided but contains no identifiable food, say so and ask again.
Step 3: Determine meal type
Use this priority:
- Explicit mention — the user said "breakfast", "早餐", "宵夜", "lunch", etc. → use that directly.
- Time-based fallback — if the user doesn't specify, infer from the current local time:
- 05:00–10:00 →
breakfast - 11:00–14:00 →
lunch - 17:00–21:00 →
dinner - All other hours →
snack
- 05:00–10:00 →
Step 4: Analyze nutrition
- Identify all food items — main dishes, sides, sauces, condiments, beverages. Don't miss cooking oils, dressings, or drinks.
- Estimate portions in grams — use reference objects if visible in a photo (standard plate 23–26 cm, bowl 200–250 ml, palm ≈ 100 g protein).
- Cross-verify with nutrition databases. Priority: USDA FoodData Central, then local databases for regional foods.
- Check hidden calories — cooking oil (1 tbsp ≈ 120 kcal), sugar in sauces, thickeners (cornstarch), condiments (mayo, ketchup).
- Apply cooking method adjustments:
- Deep-fried: ×1.5–2.0
- Pan-fried: +45–135 kcal
- Braised: +50–80 kcal per 100 ml sauce
- Calculate totals — calories, protein (g), carbs (g), fat (g), fiber (g).
- Provide a confidence range — low / most likely / high estimate.
What ships with it
1 file 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.
- 11d ago First seen · 86 lines · 106 tokens per session scan A 154369eb5f5a
mojo-food-log is a skill published in the GitHub repository mojoapp-ai/agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 1,500 once invoked, about $0.0005 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
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…