Borrowing it
Nothing to install: this file belongs to AlexisBalayre/claude-code-power-config. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AlexisBalayre/claude-code-power-config/main/.claude/skills/domain-modeling/SKILL.mdgit clone --depth 1 https://github.com/AlexisBalayre/claude-code-power-configWrote 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/alexisbalayre/claude-code-power-config/domain-modeling)<a href="https://agentmods.dev/skills/alexisbalayre/claude-code-power-config/domain-modeling"><img src="https://agentmods.dev/badge/skills/alexisbalayre/claude-code-power-config/domain-modeling/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/alexisbalayre/claude-code-power-config/domain-modeling"><img src="https://agentmods.dev/badge/skills/alexisbalayre/claude-code-power-config/domain-modeling.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 | $0.00905 |
| Opus 5.5 | $0.00016 | $0.00362 |
| Sonnet 5.5 | $0.00008 | $0.00181 |
| Haiku 4.5 | $0.00004 | $0.00090 |
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 2mo 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Modeling
Actively sharpen the project's documented language as you design: challenge terms, stress-test them with edge-case scenarios, and update the docs the moment a decision crystallises. Merely reading the docs for vocabulary is not this skill; reach for it when the language is being changed, not just consumed.
Where the documented language lives in this repo
| Source | What it covers |
|---|---|
docs/glossary.md |
Cross-cutting nouns: Organization, Member, Session, Participant, Provider, Channel, etc. |
docs/conventions/core.md |
Role taxonomy and naming stems for modules / classes |
docs/explanation/<topic>.md |
Current narrative for a subsystem (system architecture, security model) |
docs/adr/ |
Dated log of why a hard-to-reverse choice was made |
Before a session, skim the Glossary, the relevant docs/explanation/ doc, and any ADRs already filed for the area. For why/how questions that span multiple services, delegate to the architecture-explainer subagent rather than re-reading docs in the main context.
During the session
Challenge against the existing language
When the user uses a term that conflicts with the Glossary or core.md, call it out. Example: "Glossary defines Session as the live client connection context; you're using it for the engine process that runs it. Which do you mean?"
Sharpen fuzzy language
Propose precise canonical terms; pull from the existing Glossary first, only invent when nothing fits. Common ambiguities here:
- "Session" (the user-facing connection context vs. the engine process running it)
- "Message" (the inbound event vs. the rendered delivery to a Channel)
- "Tenant" vs.
Organization(BetterAuth term wins) - "Member" vs. "Participant" vs. "User"
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.
- 2mo ago First seen · 68 lines · 41 tokens per session scan A b20952abf5fe
domain-modeling is a skill published in the GitHub repository AlexisBalayre/claude-code-power-config (2 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 905 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
vuln-scanner
Audit trending repos for real security vulnerabilities and disclose responsibly - scan and route findings (PVR / dependency PR), re-submit queued advisories, and send armed email disclosures.
fork-fleet
Fork divergence monitor - tracks where the fleet's active forks diverge in CODE (unique commits, new/modified skills) and CONFIG (enable/var/model/schedule vs upstream), gated on real change.
pack-submit
Package one of this agent's own skills as a standalone community pack and submit it to the aeon registry as a PR.
shiplog
Recap of everything shipped since the last run - cross-repo PRs, security fixes, star deltas, and X traction, synthesized into a digest article and a ready-to-post shiplog in your voice.
token-pick
One token recommendation and one prediction market pick - scored, quantified, with a skip branch when signals are weak.
autoresearch
Evolve a skill by generating variations, evaluating them, and updating the best version.