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 okeefeco/pyeye-mcp --skill architecture-reviewgit clone --depth 1 https://github.com/okeefeco/pyeye-mcpWrote 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/okeefeco/pyeye-mcp/architecture-review)<a href="https://agentmods.dev/skills/okeefeco/pyeye-mcp/architecture-review"><img src="https://agentmods.dev/badge/skills/okeefeco/pyeye-mcp/architecture-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/okeefeco/pyeye-mcp/architecture-review"><img src="https://agentmods.dev/badge/skills/okeefeco/pyeye-mcp/architecture-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 | $0.00096 | $0.03468 |
| Opus 5 | $0.00048 | $0.01734 |
| Sonnet 5 | $0.00019 | $0.00694 |
| Haiku 4.5 | $0.00010 | $0.00347 |
Grade A, and why
architecture-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 10d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Review
Surface and prioritise how a Python codebase's architectural conventions diverge from each other, with cited evidence and honest grades — so a human can decide which convention to keep. This skill is the single source of truth for how to run the audit; it orchestrates a fresh-context auditor over a supplied scope and drives the human review loop.
What This Is (and Is NOT) — Read First
Honest expectation. This tool produces a ranked queue of "here are N ways your code does X — you decide." Many entries will carry no recommendation by design (see the honesty invariants). It makes unknown divergences visible and ordered by impact — it does not remove the work, and it is emphatically not "the AI cleans up its own mess." You still make the architectural calls; this just stops you from making them blind.
Hard scope caveat — divergence, NOT duplication. This audits convention divergence ("your code does X seven different ways"). It does NOT detect semantic duplication ("the same code copy-pasted in many places"). Duplication is a content-similarity problem this skill structurally cannot do; it is carved out to issue #495. Do not run this expecting duplication detection — you will not get it.
Scope is caller-supplied and REQUIRED. You must hand this a scope — a package, module, or directory. Bounded-scope auto-iteration over a whole large repo is deferred; this skill audits exactly the scope it is given.
The Seven Seed Axes
The auditor sweeps (at minimum) these seven convention axes. The keys are the
single source of truth in pyeye.architecture_review.taxonomy.SEED_AXES; the
auditor may also surface other axes it finds.
| Axis key | What it covers |
|---|---|
layering |
Layering / dependency direction: which layers/packages may import which. |
module_boundaries |
Module & placement boundaries: where a kind of thing lives. |
dependency_acquisition |
Dependency acquisition: constructor-injection vs import vs global/singleton. |
error_handling |
Error handling: raise vs return-sentinel vs result-type; exception types; where caught. |
validation_placement |
Validation placement: at the boundary vs in core vs scattered. |
naming_api_shape |
Naming & API shape: naming patterns, return-type conventions, sync/async split. |
cross_cutting |
Cross-cutting access: logging, config, path handling. |
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.
- 10d ago First seen · 260 lines · 96 tokens per session scan A 5d5d2c9f3b93
architecture-review is a skill published in the GitHub repository okeefeco/pyeye-mcp (0 stars, last pushed 9d ago), licensed MIT. It adds 96 tokens to every session and 3,468 once invoked, about $0.0005 per session on Opus 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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