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 frankxai/claude-skills-library --skill ai-architect-reviewgit clone --depth 1 https://github.com/frankxai/claude-skills-libraryWrote 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/frankxai/claude-skills-library/ai-architect-review)<a href="https://agentmods.dev/skills/frankxai/claude-skills-library/ai-architect-review"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/ai-architect-review/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/frankxai/claude-skills-library/ai-architect-review"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/ai-architect-review.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 | $0.00088 | $0.02589 |
| Opus 5.5 | $0.00035 | $0.01036 |
| Sonnet 5.5 | $0.00018 | $0.00518 |
| Haiku 4.5 | $0.00009 | $0.00259 |
Grade A, and why
ai-architect-review 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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI architecture review
Most AI system choices are reversible in an afternoon. Four are not. This skill checks those four first, then walks the planes for anything unowned.
Run it against a real codebase. Every check below names something you can grep, measure, or point at. If you cannot produce the evidence, the check fails — an architecture review with no evidence is an opinion.
Step 1 — the four decisions
Work these in order. Each one asks for evidence, not a judgement.
1. Where does the model call go?
The question is not which model. It is whether exactly one place in the system knows a model provider's name.
Evidence: grep for the provider SDK import across the repo.
rg -l "from ['\"](openai|@anthropic-ai/sdk|@google/gen|@aws-sdk/client-bedrock)" --type-add 'src:*.{ts,tsx,js,py,go,rb,java}' -tsrc
- One module → decision made. Note where the seam is.
- More than one → deferred, not made. Every call site is a migration cost that grows monotonically. Say this plainly and give the count.
What a single seam buys, that scattered calls cannot: routing by task, fallback when a provider degrades, a cache with a coherent key, and one line item for spend. A system with no fallback path has no fallback path at every layer, whatever the layers above it do.
2. What shape is the loop?
Four shapes. Three questions pick one.
- Can you name every step before the request arrives? → fixed workflow
- If not: is it one coherent piece of work? → single agent loop
- If not: does the work mutate shared state? → sequential sub-agents, otherwise parallel sub-agents
| Shape | Costs | Fails by |
|---|---|---|
| Fixed workflow | No adaptation when input is not what you planned for | Staying confident, going silently wrong once reality drifts from the graph |
| Single agent loop | Latency and spend grow with loop length | Losing track of its own earlier decisions as the window fills |
| Parallel sub-agents | A merge step you have to design | Duplicating work, then contradicting with no way to adjudicate |
| Sequential sub-agents | Throughput, given up on purpose | Being slow enough that someone parallelises the writes again |
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.
- 1mo ago First seen · 235 lines · 88 tokens per session scan A 24d39ff51bda
ai-architect-review is a skill published in the GitHub repository frankxai/claude-skills-library (47 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 2,589 once invoked, about $0.0004 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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