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 storygit 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/story)<a href="https://agentmods.dev/skills/berba-q/faostat-skills/story"><img src="https://agentmods.dev/badge/skills/berba-q/faostat-skills/story/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/story"><img src="https://agentmods.dev/badge/skills/berba-q/faostat-skills/story.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 | $0.00153 | $0.02623 |
| Opus 5 | $0.00077 | $0.01311 |
| Sonnet 5 | $0.00031 | $0.00525 |
| Haiku 4.5 | $0.00015 | $0.00262 |
Grade A, and why
faostat-story 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Storyteller
Build data-driven narratives for journalists, researchers, and communicators using FAOSTAT data, with embedded interactive charts and properly sourced statistics.
Prerequisites
Before starting, confirm the FAOSTAT MCP tools are available by checking that tools faostat_get_data, faostat_search_codes, faostat_list_groups, faostat_list_domains, 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.
Important Rules
Element and item code resolution. Never use a hardcoded numeric element or item code as the primary value in a faostat_get_data call. Always resolve at runtime: faostat_search_codes(domain_code='<dom>', dimension_id='element', query='<metric name>') for elements; faostat_search_codes(domain_code='<dom>', dimension_id='item', query='<item name>') for items. Numeric codes shown in reference tables and code examples are verified hints — use them to validate the search result, not as the authoritative source. Domain letter-codes (QCL, TCL, GT, EM, FBS, FS…) are stable and may be used directly.
Workflow
Step 1 — Understand the Story Angle
Ask the user for their research question or story angle. Examples:
- "The global avocado boom"
- "Africa's fertilizer gap"
- "Wheat after the Ukraine crisis"
- "Who feeds the world's growing cities?"
- "The rise of quinoa"
If the user provides the angle in their initial message, proceed without re-asking.
Identify:
- Subject — what commodity, country, or theme?
- Tension — what's surprising, changing, or at stake?
- Scope — global, regional, or country-level?
- Time frame — recent years, historical arc, or a specific event window?
Step 2 — Identify Relevant FAOSTAT Domains
Based on the story angle, determine which FAOSTAT domains contain relevant data. Use faostat_list_groups and faostat_list_domains to confirm domain availability.
Common domain mappings:
- Production stories: QCL (Crops and Livestock Products)
- Trade stories — aggregate flows (total imports/exports for a country-commodity): TCL (Crops and Livestock Trade, country-level)
- Trade stories — partner / bilateral flows (who ships to whom): TM (Detailed Trade Matrix)
- Food security stories: FS (Food Security), FBS (Food Balance Sheets)
- Climate/emissions stories: GT (Emissions Totals), ET (Temperature Change), GF (Forests)
- Input stories: RFN/RFM/RFB (Fertilizers), RP (Pesticides)
- Land use stories: RL (Land Use)
- Producer prices: PP
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 · 176 lines · 153 tokens per session scan A 8382124b13fb
faostat-story is a skill published in the GitHub repository berba-q/faostat-skills (7 stars, last pushed 4mo ago), licensed MIT. It adds 153 tokens to every session and 2,623 once invoked, about $0.0008 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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