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 decide-like-modulegit 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/decide-like-module)<a href="https://agentmods.dev/skills/vladick-pick/business-ontology/decide-like-module"><img src="https://agentmods.dev/badge/skills/vladick-pick/business-ontology/decide-like-module/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/decide-like-module"><img src="https://agentmods.dev/badge/skills/vladick-pick/business-ontology/decide-like-module.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 | $0.00038 | $0.03838 |
| Opus 5 | $0.00019 | $0.01919 |
| Sonnet 5 | $0.00008 | $0.00768 |
| Haiku 4.5 | $0.00004 | $0.00384 |
Grade A, and why
decide-like-module 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decide like module
Purpose
Decisions are the most valuable layer of a business ontology, and the easiest to waste. A module accumulates dozens of rulings over its life — "we never run a campaign without a confirmed opt-in," "refunds over 30 days go to the area lead," "we don't onboard clients below the minimum deal size" — and most of that knowledge sits inert in decision cards that get written once and read never. The point of capturing decisions was never archival; it was so the module could decide consistently next time. This skill is what turns the stored layer into a working one.
The reasoning behind it matters more than the mechanics. When a new case lands, there are three possibilities, and they call for genuinely different behaviour. Either the module has already effectively decided this — there is a decision card or a rule that covers it, and the right move is to apply that ruling with a citation, so the new case is handled the way every prior case was. Or the case is similar but not identical to a prior ruling — and then the honest move is to recommend by analogy while naming the gap, so a human can see the reasoning is a stretch rather than a match. Or the rules genuinely do not cover it, or two of them point opposite ways, or it is a real expert judgement that only the area owner can make — and then inventing an answer is the worst thing the apprentice can do, because a confident fabrication is indistinguishable from a real ruling once it is written down. The skill exists to make the first case fast, the second case visible, and the third case escalate instead of hallucinate.
This is the apprentice stance. An apprentice does not become the decision-maker; it recommends from accepted precedent. The agent proposes, an authorized human decides, and the deterministic controller applies. Marking a new ruling accepted on agent authority remains forbidden.
When to use
Reach for this skill when:
- Someone asks how the module would handle a specific new or borderline case — a pricing exception, a refund past the window, an unusual onboarding, a "can we skip step X this once?" — and wants the module's own answer, not a generic one.
- You are mid-task in a working repo and hit a choice the module has a stance on ("does this client clear our minimum?", "do we need a second approver here?") and want to answer "as the module would" rather than improvise.
- A human asks "what's our policy on …" for a situation that is not literally written down but is close to things that are.
- You need to check whether a proposed action is consistent with the module's prior decisions before it goes ahead.
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 · 134 lines · 38 tokens per session scan A 9d2bff345e53
decide-like-module is a skill published in the GitHub repository Vladick-Pick/business-ontology (2 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 3,838 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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