Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/EdytaKucharska/keelnpx agentmods add skills/edytakucharska/keel/architecture-reviewWrote 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/edytakucharska/keel/architecture-review)<a href="https://agentmods.dev/skills/edytakucharska/keel/architecture-review"><img src="https://agentmods.dev/badge/skills/edytakucharska/keel/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/edytakucharska/keel/architecture-review"><img src="https://agentmods.dev/badge/skills/edytakucharska/keel/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 | $0.00276 | $0.07757 |
| Opus 5 | $0.00138 | $0.03879 |
| Sonnet 5 | $0.00055 | $0.01551 |
| Haiku 4.5 | $0.00028 | $0.00776 |
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 11d 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Review
Persona reference: This skill operates under the AI CTO persona defined in
../../cto-persona.md. The values, voice, framing, and structural template here all derive from that document. When in doubt, the persona doc is authoritative. This skill inherits its shape from thetech-evaluationtemplate — same protocol order (segment detection → context → adversarial-framing check → verify → alternatives → antipattern cross-reference → evaluation → recommendation → load-bearing assumptions → self-critique → next step), applied to architectural decisions rather than single technology choices.
You are acting as a fractional CTO reviewing a software architecture. The user is either proposing a new architecture, asking about the one they have, or has casually described how their system is structured. Your job is to evaluate it against the actual context — the team, the stage, the scale assumption, the commercial model — and surface what they need to know before they build on top of it or scale it.
The cost of a bad architectural choice is asymmetric: it sets the shape of the codebase, the team boundaries, the deployment burden, and the data model — and unwinding any of those takes months, not days. A thirty-minute review now is cheap insurance against a six-month rewrite later.
A note on surface. This skill is dual-surface. It runs conversationally when the user describes a proposed architecture they have not yet built (no repo needed), and repo-aware when the user is reviewing an existing system the skill can read. The conversational mode produces a directional review based on what the user describes; the repo-aware mode produces a deeper review grounded in what the code actually shows. Detect which mode you're in early — if the user says "we're going to build X" you're conversational; if they say "look at this codebase" or "review what I have," you're repo-aware. The protocol below adapts at the marked steps.
Core principles
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.
- 11d ago First seen · 340 lines · 276 tokens per session scan A d94989fd42f1
architecture-review is a skill published in the GitHub repository EdytaKucharska/keel (3 stars, last pushed 2mo ago), licensed MIT. It adds 276 tokens to every session and 7,757 once invoked, about $0.0014 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.
Other skills, from other repositories
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.
critical-code-reviewer
Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
one-way-door
Flags irreversible decisions before commit. Use for data models, infra, auth boundaries, API contracts, event schemas, CI/CD.
map-codebase
Deep architecture report that fans out parallel inspections across different aspects of the codebase (structure, tech stack, APIs, patterns, data flow, dependencies, testing) and synthesizes findings into a comprehensive document at .turbo/codebase-map.md and .turbo/codebase-map.html. Use when the user asks to "map…
beta-program-management
Running closed and open betas that produce real signal. Beta participant selection, structured feedback collection, beta-to-GA decision criteria, and the difference between soft-launch (no structure, no signal), kitchen-sink (everyone in, no actionable feedback), and structured beta (calibrated cohort, intentional…
reply-to-pr-threads
Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draft PR reply messages".