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 volodchenkov/claude-sdlc-agents --skill architecture-review-frameworkgit clone --depth 1 https://github.com/volodchenkov/claude-sdlc-agentsWrote 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/volodchenkov/claude-sdlc-agents/architecture-review-framework)<a href="https://agentmods.dev/skills/volodchenkov/claude-sdlc-agents/architecture-review-framework"><img src="https://agentmods.dev/badge/skills/volodchenkov/claude-sdlc-agents/architecture-review-framework/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/volodchenkov/claude-sdlc-agents/architecture-review-framework"><img src="https://agentmods.dev/badge/skills/volodchenkov/claude-sdlc-agents/architecture-review-framework.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 | $0.00090 | $0.02475 |
| Opus 5 | $0.00045 | $0.01238 |
| Sonnet 5 | $0.00018 | $0.00495 |
| Haiku 4.5 | $0.00009 | $0.00248 |
Grade A, and why
architecture-review-framework 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Review Framework
This skill defines how the architect reviews SPECs and CHANGES. Goal: produce a verifiable, professional ARCH_REVIEW that catches problems early and leaves a paper trail. Inspired by:
- C4 Model review at Context and Container levels
- SOLID principles (Robert C. Martin) — for code review
- DDD bounded context discipline — service boundary checks
- ADR pattern (Michael Nygard) — for capturing and approving architectural decisions
- Code review best practices (Google eng-practices, Smartbear)
Two review modes
Mode A: SPEC review (most common)
Triggered after the system-analyst posts SPEC. The architect reads SPEC + REQUIREMENTS, evaluates against 7 review areas (Area 0 implementation-readiness + 6 technical areas), evaluates each ADR, validates traceability matrix. Output: ARCH_REVIEW comment in SPEC sub-issue + SPEC_APPROVED marker (when all green).
Mode B: CHANGES review (less common — assist the reviewer)
After coders post CHANGES. The architect cross-checks that implementation respects the architectural decisions in SPEC. Output: ARCH_REVIEW iteration comment in coder's sub-issue. Note: the reviewer is the canonical reviewer for CHANGES; the architect intervenes only when architectural drift is suspected.
This skill focuses on Mode A; Mode B uses the same 7-area lens.
The 7 Review Areas
Every ARCH_REVIEW iteration covers all 7 — even if some are N/A for the given SPEC, explicitly state why N/A. Silent skipping is the most common architecture review failure mode.
Area 0: Implementation-readiness (ATAM-style concrete scenarios)
A formally correct SPEC that leaves implementation to guesswork is not APPROVED. For every affected backend service and frontend, verify:
- Every model field has explicit type, constraints, and
on_deletesemantics (coder doesn't extrapolate from "similar models"). - Every endpoint has request shape, response shape, and ALL error codes (not just success).
- Every user-facing screen has explicit loading / empty / error / partial / success states.
- Every business rule (§4 BRs) is a testable invariant with explicit inputs and expected outputs — not an aspiration ("handle X gracefully", "good UX").
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 · 200 lines · 90 tokens per session scan A 34477cc86139
architecture-review-framework is a skill published in the GitHub repository volodchenkov/claude-sdlc-agents (2 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 2,475 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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