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 onboard-contourgit 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/onboard-contour)<a href="https://agentmods.dev/skills/vladick-pick/business-ontology/onboard-contour"><img src="https://agentmods.dev/badge/skills/vladick-pick/business-ontology/onboard-contour/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/onboard-contour"><img src="https://agentmods.dev/badge/skills/vladick-pick/business-ontology/onboard-contour.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 | $0.00041 | $0.00923 |
| Opus 5 | $0.00020 | $0.00462 |
| Sonnet 5 | $0.00008 | $0.00185 |
| Haiku 4.5 | $0.00004 | $0.00092 |
Grade A, and why
onboard-contour 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboard contour
Purpose
Use this skill at the start of the first session. The goal is a usable contour, not a complete ontology. The owner spends 10 minutes giving enough shape for the agent to start reading sources. The contour includes the company model language: the language used for human-facing model text. It is not inferred from chat language.
When to use
Use this skill when:
- a new resident agent starts with an owner;
- the current model has no agreed business boundary;
- the owner wants to reset the starting contour.
Do not use it for a deep modeling workshop. If the owner wants to model a
process live for 60-90 minutes, use the capture loop in the primary
business-ontology skill.
Procedure
Start by inviting voice input:
You can answer by voice if it is easier. I can work from the transcript, and
voice usually carries more context. I will not store raw audio in the model.
Ask one question at a time:
- What does the company do, in one paragraph?
- What do you produce or sell, and to whom?
- What directions, businesses, or product lines are inside it?
- What hurts most right now?
- Recommend the starting area yourself: "I will start with because <pain/source>. OK?"
- What mainly flows through this area?
- Where does the truth about that flow live?
- Who are the key roles in this area?
- Which metric says this area is working well?
- Which language should I use for the company model text? Recommend the language in which the owner and team make decisions. Technical ids stay stable and language-independent.
The recommendation in step 5 is the agent's job. Use answers 3 and 4 plus any available source readiness. Do not ask the owner to choose from a blank slate when you can make a defensible recommendation.
Before sending each unanswered setup question, record a human_request with
kind=setup. When the owner answers, close that request and continue the
ladder. Questions answered in the same incoming message may be recorded and
closed immediately so the ledger still explains why no setup ask remains open.
If the company model language is unanswered, keep it as
pending-owner-selection, leave the human_request open, and do not mark
onboarding complete.
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 · 111 lines · 41 tokens per session scan A 2c75db4d3e40
onboard-contour is a skill published in the GitHub repository Vladick-Pick/business-ontology (2 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 923 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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