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 Jacoby6000/Smithplates --skill design-target-architecturegit clone --depth 1 https://github.com/Jacoby6000/SmithplatesWrote 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/jacoby6000/smithplates/design-target-architecture)<a href="https://agentmods.dev/skills/jacoby6000/smithplates/design-target-architecture"><img src="https://agentmods.dev/badge/skills/jacoby6000/smithplates/design-target-architecture/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/jacoby6000/smithplates/design-target-architecture"><img src="https://agentmods.dev/badge/skills/jacoby6000/smithplates/design-target-architecture.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 | $0.00073 | $0.01370 |
| Opus 5.5 | $0.00029 | $0.00548 |
| Sonnet 5.5 | $0.00015 | $0.00274 |
| Haiku 4.5 | $0.00007 | $0.00137 |
Grade A, and why
design-target-architecture 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Target Architecture
Produce a conceptual architecture — the interfaces, domain models, concrete implementations, and relationships needed to realize a goal — from two inputs:
- A high-level conceptual explanation of what the project should do.
- A high-level architecture (layers, components, boundaries, constraints).
The output is a Conceptual Architecture Document (CAD): the shared,
machine-and-human-readable contract used across this skill family. Read the full
schema at .ai-doc-reference/cad-contract.md (repo root) before emitting one. Producing a
valid CAD is what lets analyze-codebase-architecture and
plan-architecture-refactor consume this output later.
This is a design skill, not an implementation skill. Stay at the level of signatures, responsibilities, and relationships. Do not write method bodies or production logic.
Inputs required (do not guess)
Before designing, confirm you have:
- Concept: the problem, primary use cases, and core behaviors.
- Architecture: intended layers/components, boundaries, and any hard constraints (runtime, persistence, external systems, performance).
- Target language(s): the language for interface signatures.
If any input is missing or ambiguous, ask the user — do not invent requirements or assume "sane" defaults. Missing decisions become entries in the CAD's Open Questions section, not silent assumptions.
Workflow
Copy this checklist and track progress:
- [ ] 1. Confirm inputs; list ambiguities to resolve
- [ ] 2. Extract the domain model (entities, value objects, invariants)
- [ ] 3. Define capabilities as interfaces (responsibility + signatures)
- [ ] 4. Specify implementations and hand-rolled test doubles
- [ ] 5. Map relationships (dependency direction, composition, data flow)
- [ ] 6. Capture open questions, risks, and trade-offs
- [ ] 7. Emit the CAD; offer to save it
1. Confirm inputs
Restate the concept and architecture in your own words in 3-5 bullets so the user can correct misunderstandings early. Surface every ambiguity now. Ask about anything that would otherwise force an assumption.
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 · 147 lines · 73 tokens per session scan A 3d60f5911a39
design-target-architecture is a skill published in the GitHub repository Jacoby6000/Smithplates (2 stars, last pushed 20d ago), licensed MIT. It adds 73 tokens to every session and 1,370 once invoked, about $0.0003 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.
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