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 Mathews-Tom/armory --skill architecture-reviewergit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote 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/mathews-tom/armory/architecture-reviewer)<a href="https://agentmods.dev/skills/mathews-tom/armory/architecture-reviewer"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/architecture-reviewer/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/mathews-tom/armory/architecture-reviewer"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/architecture-reviewer.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 | $0.00093 | $0.03960 |
| Opus 5.5 | $0.00037 | $0.01584 |
| Sonnet 5.5 | $0.00019 | $0.00792 |
| Haiku 4.5 | $0.00009 | $0.00396 |
Grade A, and why
architecture-reviewer 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 yesterday.
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 — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Reviewer
Systematic, framework-driven architecture review skill. Acts as a senior staff/principal engineer performing a thorough architecture critique. Not a rubber-stamp — the skill is opinionated, identifies real risks, and challenges assumptions. Every finding is tied to a concrete impact and a concrete recommendation.
Workflow Overview
The review proceeds in 4 phases:
- Input Classification & Context Gathering — Determine review mode, scan inputs, ask clarifying questions (always).
- Dimension-by-Dimension Analysis — Evaluate 7 dimensions, loading each reference as needed.
- Cross-Cutting Analysis — Identify conflicts, coherence issues, and systemic risks.
- Scoring & Report Generation — Compute scores, prioritize recommendations, produce report.
⚠️ CRITICAL: Scoring & Format Quick Reference
These constraints are NON-NEGOTIABLE. Memorize before starting any review.
SCORE SCALE: 1-5 only (NOT 1-10, NOT percentages)
Half-scores (3.5) permitted with justification
SEVERITY LABELS: [S1] Critical — System will fail or is exploitable
[S2] High — Significant risk under realistic conditions
[S3] Medium — Design weakness limiting growth
[S4] Low — Suboptimal but manageable
[S5] Info — Best practice suggestion (also used for strengths)
DIMENSION WEIGHTS:
Structural Integrity: 20% | Performance: 17%
Scalability: 18% | Enterprise Readiness: 15%
Security: 18% | Operational Excellence: 7%
| Data Architecture: 5%
GRADE BOUNDARIES:
A = 90-100% | B = 80-89% | C = 70-79% | D = 60-69% | F = <60%
FORMULA: Overall% = (Σ dimension_score × weight) / 5 × 100
Template compliance is mandatory. See Phase 4 checklist before finalizing any report.
Phase 1: Input Classification & Context Gathering
What ships with it
16 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.
- assets/report-template.md 10 KB
- CHANGELOG.md 289 B
- evals/cases.yaml 3.8 KB
- references/codebase-signals.md 6.5 KB
- references/data-architecture.md 8.7 KB
- references/deep-module-analysis.md 3.4 KB
- references/document-review-guide.md 7.8 KB
- references/enterprise-readiness.md 16 KB
- references/operational-excellence.md 8.8 KB
- references/performance.md 9.2 KB
- references/scalability.md 9.0 KB
- references/scoring-rubric.md 22 KB
- references/security.md 13 KB
- references/structural-integrity.md 11 KB
- references/upstream/LICENSE 1.0 KB
- scripts/scan_codebase.sh 16 KB runs code
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.
- yesterday Changed · +22 tokens per session 7ef131f42a1b
- 1mo ago First seen · 381 lines · 71 tokens per session scan A a2079e6e39bf
architecture-reviewer is a skill published in the GitHub repository Mathews-Tom/armory (337 stars, last pushed 2d ago), licensed MIT. It adds 93 tokens to every session and 3,960 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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