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 berba-q/faostat-skills --skill trendsgit clone --depth 1 https://github.com/berba-q/faostat-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/berba-q/faostat-skills/trends)<a href="https://agentmods.dev/skills/berba-q/faostat-skills/trends"><img src="https://agentmods.dev/badge/skills/berba-q/faostat-skills/trends/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/berba-q/faostat-skills/trends"><img src="https://agentmods.dev/badge/skills/berba-q/faostat-skills/trends.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 | $0.00117 | $0.01936 |
| Opus 5 | $0.00059 | $0.00968 |
| Sonnet 5 | $0.00023 | $0.00387 |
| Haiku 4.5 | $0.00012 | $0.00194 |
Grade A, and why
faostat-trends 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 10d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agricultural Trend Monitor
Identify the biggest changes and anomalies in agricultural production data over a specified time window and geography.
Prerequisites
Before starting, confirm the FAOSTAT MCP tools are available by checking that tools faostat_get_data, faostat_search_codes, and faostat_get_rankings are accessible. If they are not, inform the user that this skill requires the FAOSTAT MCP server to be connected and stop.
Workflow
Step 1 — Gather Parameters
Ask the user for:
- Region or countries — one or more countries, a continent, or a region (e.g., "Africa", "Brazil and Argentina", "Southeast Asia")
- Time window — default to the last 5 years if not specified
- Focus (optional) — specific commodity groups to monitor, or leave broad for all major groups
If the user provides these in their initial message, proceed without re-asking.
Step 2 — Resolve Area Codes
Use faostat_search_codes with domain_code='QCL' and dimension_id='area' to resolve each country or region name to its FAOSTAT area code.
CRITICAL: If requires_confirmation is true in the response (multiple matches), present the options to the user and ask them to choose before proceeding. Do NOT guess.
Step 3 — Pull Production Data for Major Commodity Groups
Query the QCL (Crops and Livestock Products) domain using faostat_get_data. Pull production quantity data (element FILTER code resolved at runtime via faostat_search_codes(domain_code='QCL', dimension_id='element', query='production') → e.g. '2510') across major commodity groups.
For broad monitoring, query across these key items:
- Cereals (wheat, rice, maize, barley, sorghum, millet)
- Oilcrops (soybeans, palm fruit, sunflower seed, rapeseed)
- Roots and tubers (cassava, potatoes, yams, sweet potatoes)
- Fruits (bananas, citrus, mangoes, avocados)
- Vegetables (tomatoes, onions)
- Livestock products (milk, meat — cattle, chicken, pig, sheep)
Use faostat_search_codes with domain_code='QCL' and dimension_id='item' to resolve each item name to its item code.
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.
- 10d ago First seen · 143 lines · 117 tokens per session scan A a8a4e4651253
faostat-trends is a skill published in the GitHub repository berba-q/faostat-skills (7 stars, last pushed 4mo ago), licensed MIT. It adds 117 tokens to every session and 1,936 once invoked, about $0.0006 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.
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