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 vasilyu1983/AI-Agents-public --skill ai-architecture-advisorgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-architecture-advisor)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-architecture-advisor"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-architecture-advisor/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/vasilyu1983/ai-agents-public/ai-architecture-advisor"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-architecture-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.06754 |
| Opus 5.5 | $0.00020 | $0.02702 |
| Sonnet 5.5 | $0.00010 | $0.01351 |
| Haiku 4.5 | $0.00005 | $0.00675 |
Grade A, and why
ai-architecture-advisor 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 22d 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 — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Architecture Advisor
The front-door decision skill for "I have problem X — what should I build with?" It owns the choice — which approach fits, when to promote complexity (and when not to), how to transfer knowledge, how to scale — then hands off to the deep skill that owns the depth. It spans the whole modeling space in one place so you can compare options that normally live in separate skills side by side:
tabular GBDT · deep net · Transformer/LLM · RAG · fine-tuning · agents
multimodal/omni · embeddings & retrieval · recsys/ranking · model architecture (dense/MoE/SSM/diffusion)
No theory dumps — decision tables, elimination logic, tradeoffs, and a pointer to the deep skill.
The architect's move is to ask before answering. The amateur hears "build an AI feature" and reaches for the model they know ("we'll fine-tune Kimi"). The architect first asks: what data type? what volume? what task? what's the success metric? do you even need a Transformer? The skill that distinguishes an architect is the willingness to say "for this, CatBoost wins," "here you need a Transformer," or "LoRA is enough here" — and to refuse to name an approach until the problem is classified. Never jump to a model before the Intake questions below are answered.
Intake: Ask Before You Answer
A request like "let's fine-tune model X" is a proposed solution, not a problem statement. Do not accept it at face value. Surface the six questions that decide the architecture, and hold any model name until they're answered. If the user can't answer one, that gap is itself a finding (most often: no success metric, or no labeled data).
| Ask | Why it changes the answer |
|---|---|
| What's the task? classify / rank / generate / extract / retrieve / act | Picks the lane before anything else — tabular-classify and open-ended-generate share no architecture |
| What's the data? type (tabular/text/image/audio), volume, labeled? private? fresh? | Tabular+small → trees; private/fresh knowledge → RAG not fine-tune; no labels → no SFT |
| What's the success metric & bar? accuracy / latency / cost-per-outcome / interpretability | "Make it work" can't be scored; the binding constraint eliminates most options |
| What are the constraints? p99 latency, $/query at volume, on-prem/cloud, no-hallucination | A <50ms or AWS-only or no-hallucination constraint forces the design more than the task does |
| Is there a regulatory/compliance driver? high-risk classification (EU AI Act), data residency, right-to-explanation, audit trail | Can force an inherently-interpretable model (trees, not a black-box net), ban sending data to a third-party API, or mandate human-in-the-loop — overriding an accuracy-only ranking |
| Do you even need an LLM/Transformer? | The cheapest thing that clears the bar wins — often a tree, a regex, or a single API call beats a fine-tune |
What ships with it
8 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.
- 22d ago Changed · -71 lines · -5 tokens per session 329e9df21248
- 1mo ago Changed · +4 lines 8962a47e0024
- 1mo ago First seen · 400 lines · 56 tokens per session scan A 66033e4602cf
ai-architecture-advisor is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (101 stars, last pushed 11d ago), licensed MIT. It adds 51 tokens to every session and 6,754 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 2026-08-30.
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