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/frostney/pascal-mcp-sdk/domain-modelingnpx skills add frostney/pascal-mcp-sdk --skill domain-modelinggit clone --depth 1 https://github.com/frostney/pascal-mcp-sdkWrote 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/frostney/pascal-mcp-sdk/domain-modeling)<a href="https://agentmods.dev/skills/frostney/pascal-mcp-sdk/domain-modeling"><img src="https://agentmods.dev/badge/skills/frostney/pascal-mcp-sdk/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 | $0.00043 | $0.00776 |
| Opus 5 | $0.00022 | $0.00388 |
| Sonnet 5 | $0.00009 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00078 |
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 5d 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.
This is a copy
91% identical to domain-modeling — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Modeling
Actively build and sharpen the project's domain model as you design. This is the active discipline — challenging terms, inventing edge-case scenarios, and writing the glossary and decisions down the moment they crystallise. (Merely reading CONTEXT.md for vocabulary is not this skill — that's a one-line habit any skill can do. This skill is for when you're changing the model, not just consuming it.)
File structure
Most repos have a single context:
/
├── CONTEXT.md
├── docs/
│ └── adr/
│ ├── 0001-event-sourced-orders.md
│ └── 0002-postgres-for-write-model.md
└── src/
If a CONTEXT-MAP.md exists at the root, the repo has multiple contexts. The map points to where each one lives:
/
├── CONTEXT-MAP.md
├── docs/
│ └── adr/ ← system-wide decisions
├── src/
│ ├── ordering/
│ │ ├── CONTEXT.md
│ │ └── docs/adr/ ← context-specific decisions
│ └── billing/
│ ├── CONTEXT.md
│ └── docs/adr/
Create files lazily — only when you have something to write. If no CONTEXT.md exists, create one when the first term is resolved. If no docs/adr/ exists, create it when the first ADR is needed.
During the session
Challenge against the glossary
When the user uses a term that conflicts with the existing language in CONTEXT.md, call it out immediately. "Your glossary defines 'cancellation' as X, but you seem to mean Y — which is it?"
Sharpen fuzzy language
When the user uses vague or overloaded terms, propose a precise canonical term. "You're saying 'account' — do you mean the Customer or the User? Those are different things."
Discuss concrete scenarios
When domain relationships are being discussed, stress-test them with specific scenarios. Invent scenarios that probe edge cases and force the user to be precise about the boundaries between concepts.
Cross-reference with code
When the user states how something works, check whether the code agrees. If you find a contradiction, surface it: "Your code cancels entire Orders, but you just said partial cancellation is possible — which is right?"
What ships with it
3 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.
- 5d ago First seen · 75 lines · 43 tokens per session scan A 152e2c97239a
domain-modeling is a skill published in the GitHub repository frostney/pascal-mcp-sdk (2 stars, last pushed 10d ago), licensed MIT. It adds 43 tokens to every session and 776 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to domain-modeling, differing in 20 lines, and is treated as a copy.
Other skills, from other repositories
profile-report-review
Review GocciaScript retained VM profile reports from test262 and benchmark main-CI runs. Use when Codex is asked to inspect uploaded test262 or benchmark performance/profile artifacts, compare week-over-week or main-run trends, investigate aggregate/detailed profile data, or turn profiling findings into compiler…
review-pr
Resolves current pull-request review findings in place, validates and pushes fixes, and can autonomously converge and merge an opted-in pull request. Use when the user runs /review-pr or /review-pr automatic-merge.
run-retro
Reviews a completed workstream from conversation, repository, and forge evidence, maps lifecycle and ground-level timings, uses grilling to agree improvements to delivery speed, process, and codebase health, then applies selected documentation edits and follow-up ticket actions. Use when ending a substantial…
implement-idea
Turns an unfiled idea into a confirmed mini-spec, implements and validates the selected approach, reviews it, and opens a draft pull request. Use when the user runs /implement-idea or asks to build something without an existing issue.
implement-issue
Validates and implements a GitHub issue against current repository evidence, runs the project's completion gate, reviews the change, and opens a draft pull request. Use when the user runs /implement-issue with an issue number.
software-engineering-excellence
Applies the user's ambient engineering bar: current evidence, complete in-scope solutions, reuse, real validation, right-sized value, and maintainability. Use for planning, orchestrating, implementing, debugging, reviewing, refactoring, architecture, release delivery, or substantial technical investigation.