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 PangenomeAI/academic-skills-food-nutrition --skill agri-pipelinegit clone --depth 1 https://github.com/PangenomeAI/academic-skills-food-nutritionWrote 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/pangenomeai/academic-skills-food-nutrition/agri-pipeline)<a href="https://agentmods.dev/skills/pangenomeai/academic-skills-food-nutrition/agri-pipeline"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-pipeline/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/pangenomeai/academic-skills-food-nutrition/agri-pipeline"><img src="https://agentmods.dev/badge/skills/pangenomeai/academic-skills-food-nutrition/agri-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00157 | $0.01036 |
| Opus 5 | $0.00078 | $0.00518 |
| Sonnet 5 | $0.00031 | $0.00207 |
| Haiku 4.5 | $0.00016 | $0.00104 |
Grade A, and why
agri-pipeline 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 12d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agri-Pipeline — Research-to-Publication for Agricultural Science
Run the food-pipeline skill exactly — its subagents (intake_router,
quality_gate), its stages 0–6, its quality gates, its mode advisor, and its
review/revision authorization defaults — with the agriculture substitutions in
agri-research/references/agriculture-domain.md.
Read that file first. No new machinery here.
The substitutions
- Persona — a senior agricultural scientist of the specific discipline, carried into every stage and every skill it dispatches (domain §2).
- Route to the
agri-*skills, not the food ones:
| Stage | Skill |
|---|---|
| 0 · ROUTE | intake_router + journal-selector (agriculture coverage map) |
| 1 · RESEARCH | agri-research (or agri-deep-research) |
| 2 · WRITE | agri-paper → figures via food-figure (domain-neutral) |
| 3 · REVIEW | agri-review |
| 4 · REVISE | agri-paper (revise) |
| 5 · RE-REVIEW | agri-review (re-review) |
| 6 · FINALIZE | agri-paper (format-convert) |
- Evidence base — Tier 1 Q1/Q2 agriculture + Nature/Science/Cell/PNAS + Q1/Q2 adjacent disciplines; Q3 for gaps; Q4 avoided (domain §3).
food-figure is used directly and deliberately — it is domain-neutral and already
renders at the journal spec.
Inherited unchanged (not optional)
- Journal resolved once at ROUTE via
journal-selectorand passed to every downstream skill, so none re-asks. - Knowledge reuse: when Stage 1 runs, its evidence base is carried into Stages
3/5 so
agri-reviewdoes not re-search the field — it reuses it and tops up withagri-researchquick brief key reviews read in full. - Review/revision defaults: one review→revise round; a second round and in-place Tracked Changes on the original Word file each require explicit author authorization.
- Deliverables — exactly two files, both
.docx: one manuscript, and oneReview_and_Response_Report_<slug>_<date>.docxcarrying both the reviewer feedback and the editing response —agri-reviewwrites the feedback at Stage 3,agri-paperfills eachResponse (type)into that same file at Stage 4, and an authorized round 2 appendsR2-*items to it. No separate reviewer report, no standalone response letter, never Markdown (food-review/references/report-format.md). - Quality gates, anti-fabrication grounding, four-gate citations, privacy scan,
human-writing.md, and the mandatory AI-use disclosure.
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.
- 12d ago First seen · 66 lines · 157 tokens per session scan A abc8529cd7b1
agri-pipeline is a skill published in the GitHub repository PangenomeAI/academic-skills-food-nutrition (31 stars, last pushed 12d ago), licensed MIT. It adds 157 tokens to every session and 1,036 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 2026-08-30.
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