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 vizgit 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/viz)<a href="https://agentmods.dev/skills/berba-q/faostat-skills/viz"><img src="https://agentmods.dev/badge/skills/berba-q/faostat-skills/viz/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/viz"><img src="https://agentmods.dev/badge/skills/berba-q/faostat-skills/viz.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 | $0.00140 | $0.02104 |
| Opus 5 | $0.00070 | $0.01052 |
| Sonnet 5 | $0.00028 | $0.00421 |
| Haiku 4.5 | $0.00014 | $0.00210 |
Grade A, and why
faostat-viz 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FAOSTAT Data Visualizer
Generate interactive, standalone HTML charts from FAOSTAT data. Charts use Chart.js via CDN and are designed to be self-contained, responsive, accessible, and properly attributed.
Prerequisites
Before starting, verify that the FAOSTAT MCP tools are available: faostat_search_codes, faostat_get_data. If they are not available and the user has not provided data in the conversation context, inform the user they need the FAOSTAT MCP server configured and stop.
If data is already available in the conversation context (e.g., from a previous query or another skill), skip directly to chart generation.
Chart Type Selection
Choose the chart type based on the data relationship. If the user does not specify a chart type, select the most appropriate one:
| Data Relationship | Chart Type | When to Use |
|---|---|---|
| Values over time | Line chart | Trends, time series, historical data |
| Values across categories | Bar chart | Comparing countries, commodities, or regions |
| Composition breakdown | Stacked bar chart | Shares of a total (e.g., emissions by source, trade by partner) |
| Two-variable relationship | Scatter plot | Correlations (e.g., fertilizer use vs. emissions) |
Do NOT use pie charts. They are difficult to read with more than 3-4 categories and do not convey FAOSTAT data well.
Workflow
Step 1: Determine data source
Check if usable data already exists in the conversation context:
- If YES: extract the relevant data points (values, labels, years, units) and proceed to Step 4.
- If NO: proceed to Step 2 to query FAOSTAT.
Step 2: Query data (if needed)
-
Ask the user what they want to visualize if not clear from context:
- What metric? (production, yield, trade volume, emissions, etc.)
- Which entities? (countries, commodities, regions)
- What time range?
-
Resolve all codes using
faostat_search_codes:- Resolve area codes:
faostat_search_codes(domain_code='<domain>', dimension_id='area', query='<name>') - Resolve item codes:
faostat_search_codes(domain_code='<domain>', dimension_id='item', query='<name>') - Handle
requires_confirmationfor every search -- present options and wait for user choice.
- Resolve area codes:
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 · 170 lines · 140 tokens per session scan A b0df21453f53
faostat-viz is a skill published in the GitHub repository berba-q/faostat-skills (7 stars, last pushed 4mo ago), licensed MIT. It adds 140 tokens to every session and 2,104 once invoked, about $0.0007 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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