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 ObneyAI/grain --skill event-model-distillgit clone --depth 1 https://github.com/ObneyAI/grainWrote 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/obneyai/grain/event-model-distill)<a href="https://agentmods.dev/skills/obneyai/grain/event-model-distill"><img src="https://agentmods.dev/badge/skills/obneyai/grain/event-model-distill.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 5.1 | $0.00063 | $0.00199 |
| Opus 5 | $0.00032 | $0.00100 |
| Sonnet 5 | $0.00013 | $0.00040 |
| Haiku 4.5 | $0.00006 | $0.00020 |
Grade A, and why
event-model-distill 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 7d 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.
What it actually says
Event Model Distill
Run Allium distill to capture intentional observable behaviour. Independently
inspect Grain def* sites and, when available, the live catalog to construct
the service-area topology. Treat registries and schemas as authoritative facts;
infer design-only screens and flows from boundaries and data movement.
Link each command to its matching distilled Allium rule and each screen to its surface. Mark uncertain topology as an open question instead of inventing it. Validate structurally, then run the composition gate. Review intended versus accidental behavior in Allium before treating the result as durable context.
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.
- 7d ago First seen · 18 lines · 63 tokens per session scan A 746db343c0a1
event-model-distill is a skill published in the GitHub repository ObneyAI/grain (118 stars, last pushed 3d ago), licensed MIT. It adds 63 tokens to every session and 199 once invoked, about $0.0003 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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