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 agentmods add skills/ma-compbio-lab/skillfoundry/yield-forecasting-starternpx skills add ma-compbio-lab/SkillFoundry --skill yield-forecasting-startergit clone --depth 1 https://github.com/ma-compbio-lab/SkillFoundryWrote 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/ma-compbio-lab/skillfoundry/yield-forecasting-starter)<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/yield-forecasting-starter"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/yield-forecasting-starter.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00148 |
| Opus 5 | $0.00000 | $0.00074 |
| Sonnet 5 | $0.00000 | $0.00030 |
| Haiku 4.5 | $0.00000 | $0.00015 |
Grade A, and why
yield-forecasting-starter 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 5d 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.
This is a copy
86% identical to agronomic-experiment-design-starter — 6 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.
What it actually says
Yield forecasting Starter
Use this starter when a task lands in the Yield forecasting frontier leaf and the repository has curated resources but no dedicated runtime implementation yet.
What this starter does
- Summarizes the local resource anchors for the leaf.
- Emits a machine-readable starter plan with promotion steps.
- Gives the agent a stable local entry point before a full runtime skill exists.
How to use it
Run python3 skills/agriculture-food-and-plant-science/yield-forecasting-starter/scripts/run_frontier_starter.py --out scratch/frontier/yield-forecasting-starter.json.
Then inspect refs.md and examples/resource_context.json to promote the starter into a concrete executable workflow.
What ships with it
7 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.
- 5d ago First seen · 17 lines · 0 tokens per session scan A 15f327a0af75
yield-forecasting-starter is a skill published in the GitHub repository ma-compbio-lab/SkillFoundry (38 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 148 tokens. A static security scan graded it A with 0 findings. It is 86% identical to agronomic-experiment-design-starter, differing in 6 lines, and is treated as a copy.
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