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 Joncik91/ucai --skill architectgit clone --depth 1 https://github.com/Joncik91/ucaiWrote 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/joncik91/ucai/architect)<a href="https://agentmods.dev/skills/joncik91/ucai/architect"><img src="https://agentmods.dev/badge/skills/joncik91/ucai/architect/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/joncik91/ucai/architect"><img src="https://agentmods.dev/badge/skills/joncik91/ucai/architect.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 | $0.00056 | $0.01456 |
| Opus 5.5 | $0.00022 | $0.00582 |
| Sonnet 5.5 | $0.00011 | $0.00291 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
architect 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 1mo 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architect
System design, architecture decisions, and technical strategy for any scale and any stack.
Step 1: Understand the Context
Before designing or recommending anything, establish:
- Stage: prototype / startup / growth / enterprise?
- Team size: solo / small (2-5) / medium (6-20) / large (20+)?
- Traffic: requests/day? peak load? geographic distribution?
- Data: volume? sensitivity? compliance requirements (GDPR, HIPAA, SOC2)?
- Constraints: cloud provider, existing stack, budget, timeline?
Ask or infer from project files: CLAUDE.md, README.md, package.json, docker-compose.yml, terraform/, .github/workflows/.
Step 2: Research Current Options
For any significant technology decision, search before recommending:
WebSearch: "<decision area> best practices 2025"
WebSearch: "<option A> vs <option B> 2025 comparison"
WebSearch: "<database type> use cases trade-offs 2025"
WebSearch: "<architecture pattern> when to use 2025"
This grounds recommendations in current community consensus, not stale knowledge.
Step 3: Architecture Decision Records (ADRs)
Document every significant decision as a MADR (Markdown Any Decision Record):
# ADR-NNN: <Title>
**Status**: Proposed | Accepted | Deprecated | Superseded by ADR-NNN
## Context
What is the situation forcing this decision?
## Decision
What are we choosing to do?
## Options Considered
| Option | Pros | Cons |
|--------|------|------|
| A | ... | ... |
| B | ... | ... |
## Consequences
What becomes easier? What becomes harder?
## References
- Link to relevant documentation or prior art
Store in docs/decisions/ or adr/.
Step 4: Universal Architecture Principles
Algorithm Audit (apply before every other principle below)
Before adopting any pattern in this section: (1) question whether the requirement driving it is load-bearing or aspirational — can it be dropped? (2) for each new dependency, framework, or abstraction, ask whether ~10 lines of vanilla logic would suffice; (3) only after deletion, optimize. Reason from the fundamentals of the actual data and traffic, not from "this is how serious teams do it." If the only justification is analogy ("everyone uses microservices / Redis / Kafka"), the choice is unjustified.
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.
- 1mo ago First seen · 165 lines · 56 tokens per session scan A e4cd34fa7bba
architect is a skill published in the GitHub repository Joncik91/ucai (28 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,456 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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