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 agentmods add skills/hani-q/qstack/qstack-model-the-domainnpx skills add hani-q/qstack --skill qstack-model-the-domaingit clone --depth 1 https://github.com/hani-q/qstackWhat 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 | $0.00061 | $0.00481 |
| Opus 5 | $0.00030 | $0.00241 |
| Sonnet 5 | $0.00012 | $0.00096 |
| Haiku 4.5 | $0.00006 | $0.00048 |
Grade A, and why
qstack-model-the-domain 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 2d 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
Model the domain
Encode the real domain in a structure instead of distributing it across booleans, conditionals, loose parameters, and lifecycle checks.
Find the missing shape
- Trace the states, transitions, invariants, ownership, and dominant access paths in the current code.
- Locate repeated shape assumptions, synchronized booleans, growing branches, ad hoc mutation, and phase-named modules that repeat the same domain rules.
- Choose the smallest structure that removes those problems. Candidates include a state machine, typed model, discriminated union, registry, lookup table, reducer, command/event model, queue, cache, index, graph, normalized collection, or one module that owns a coherent body of domain knowledge.
- Compare the proposed shape with the current one. It must delete branches, duplicated rules, invalid states, or lifecycle risk. Extra indirection alone is not a benefit.
- Leave clear, local, stable code alone when no structure materially improves it. Three explicit statements can be better than a premature abstraction.
State the invariant the model enforces, the access patterns it serves, the invalid states it removes, and the migration boundary. Design work does not authorize implementation beyond the user's request. Assess compatibility and migration before changing a public API or persisted shape. Design alone does not authorize commits, pushes, publishing, deployment, or external messages.
Adapted from Lauren Tan's PStack
principle-model-the-domain
at commit 60c641e4fad674784b30abcf9f8915dea39df38d under the MIT License.
See third-party notices.
What ships with it
1 file 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.
- 2d ago First seen · 42 lines · 61 tokens per session scan A f18c2b7a9264
qstack-model-the-domain is a skill published in the GitHub repository hani-q/qstack (7 stars, last pushed 5d ago), licensed MIT. It adds 61 tokens to every session and 481 once invoked, about $0.0003 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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