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 Vladick-Pick/business-ontology --skill interpretgit clone --depth 1 https://github.com/Vladick-Pick/business-ontologyWrote 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/vladick-pick/business-ontology/interpret)<a href="https://agentmods.dev/skills/vladick-pick/business-ontology/interpret"><img src="https://agentmods.dev/badge/skills/vladick-pick/business-ontology/interpret/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/vladick-pick/business-ontology/interpret"><img src="https://agentmods.dev/badge/skills/vladick-pick/business-ontology/interpret.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 | $0.00032 | $0.02541 |
| Opus 5 | $0.00016 | $0.01270 |
| Sonnet 5 | $0.00006 | $0.00508 |
| Haiku 4.5 | $0.00003 | $0.00254 |
Grade A, and why
interpret 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 10d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interpret
Answer questions and interpret metrics strictly from the accepted model of reality, so that people and downstream agents get one consistent reading instead of a fresh improvised one each time. This is the read side of the toolkit: you consult what has been committed, you do not change it.
Why this matters: the ontology only earns trust if reading it gives the same answer the model actually holds. If you answer from memory, from raw source data, or from a plausible-sounding guess, you quietly fork the model in the reader's head and erode the single source of truth the whole kit exists to protect. Interpret keeps the answer anchored to a card with an id, a status, and a source, so the reader can verify it and so two readers get the same story.
When to use
Use Interpret when the request is to understand the module as it currently is:
- definition questions: "what counts as a qualified lead here", "what does this module produce", "who owns this production system";
- metric questions: "why did activation drop", "is this CAC computed the way we agreed", "where does this number live";
- relationship questions: "what feeds sales", "what breaks if attraction stops", "what is the interface between lidgen and sales";
- sanity checks against the model: "the dashboard says X, does that match what we decided".
The defining signal is that the answer should already exist in the committed model, and the job is to retrieve and explain it faithfully.
When not to use
- The model does not yet contain the answer and the user wants to capture it. That is authoring: hand off to the
propose-changeskill (and ultimately a human promote), not Interpret. - The user wants to recompute a number from raw data or build an analysis the model does not define. That is an analytics task; do it with normal tools, and only cite the model for the definition.
- The user wants to change a definition, status, or link. Interpret never writes. Route the change to
propose-change. - The question is about how to operate the kit itself, not about the module.
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.
- 10d ago First seen · 132 lines · 32 tokens per session scan A ad110327a058
interpret is a skill published in the GitHub repository Vladick-Pick/business-ontology (2 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 2,541 once invoked, about $0.0002 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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