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 yugasun/aiops --skill domain-modelinggit clone --depth 1 https://github.com/yugasun/aiopsWrote 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/yugasun/aiops/domain-modeling)<a href="https://agentmods.dev/skills/yugasun/aiops/domain-modeling"><img src="https://agentmods.dev/badge/skills/yugasun/aiops/domain-modeling.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 | $0.00033 | $0.00382 |
| Opus 5 | $0.00016 | $0.00191 |
| Sonnet 5 | $0.00007 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
domain-modeling 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
Domain Modeling
Actively sharpen the domain model: challenge terms, stress-test scenarios, write decisions as they crystallise. Reading CONTEXT.md for vocabulary is a one-line habit — this skill is for changing the model.
Where files live
Single context: CONTEXT.md + docs/adr/. Multi-context: CONTEXT-MAP.md points to per-area CONTEXT.md and ADRs. Create files lazily when you have something to write. Formats: CONTEXT-FORMAT.md, ADR-FORMAT.md.
During the session
- Challenge glossary — call out terms that conflict with
CONTEXT.md. - Sharpen fuzzy language — propose precise canonical terms.
- Stress-test with scenarios — edge cases that force boundary decisions.
- Cross-check code — surface contradictions between stated rules and implementation.
- Cross-check with code graph — if
graphify-out/graph.jsonexists, tell the user "用代码图谱验证模块边界假设" then query module boundaries and dependency density to validate concept boundary assumptions. Use/code-graph query modulesfor the overview and/code-graph query deps <module>to verify that proposed domain boundaries align with actual code boundaries. - Update CONTEXT.md inline — one term at a time; glossary only, no implementation detail.
ADRs — offer only when all three hold
- Hard to reverse
- Surprising without context
- Result of a real trade-off with alternatives
Otherwise skip the ADR.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 30 lines · 33 tokens per session scan A b519b496346e
domain-modeling is a skill published in the GitHub repository yugasun/aiops (4 stars, last pushed 19d ago), licensed Apache-2.0. It adds 33 tokens to every session and 382 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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