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 vijayjoshi24/ea-agent-skills --skill arch-reviewgit clone --depth 1 https://github.com/vijayjoshi24/ea-agent-skillsWrote 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/vijayjoshi24/ea-agent-skills/arch-review)<a href="https://agentmods.dev/skills/vijayjoshi24/ea-agent-skills/arch-review"><img src="https://agentmods.dev/badge/skills/vijayjoshi24/ea-agent-skills/arch-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/vijayjoshi24/ea-agent-skills/arch-review"><img src="https://agentmods.dev/badge/skills/vijayjoshi24/ea-agent-skills/arch-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 | $0.00112 | $0.00928 |
| Opus 5.5 | $0.00045 | $0.00371 |
| Sonnet 5.5 | $0.00022 | $0.00186 |
| Haiku 4.5 | $0.00011 | $0.00093 |
Grade A, and why
arch-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 2mo 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Review
Provide structured, evidence-based architecture review feedback. Reviews should be honest, specific, and actionable — not just validating what the team already decided.
Step 1 — Understand What's Being Reviewed
Identify:
- What: system, service, pattern, ADR, or full solution design
- Stage: early concept · detailed design · pre-ARB · post-implementation
- Audience: peer review · ARB · CTO · external audit
- Constraints: regulatory, legacy, budget, timeline (affect what's realistic to recommend)
Step 2 — Score Across Seven Dimensions
Rate each dimension: ✅ Strong · ⚠️ Needs attention · ❌ Significant issue
| Dimension | What to assess |
|---|---|
| Fitness for purpose | Does the design actually solve the stated problem? Is it over- or under-engineered? |
| Simplicity | Is this the simplest design that would work? Are there unnecessary abstractions? |
| Security | Are controls proportionate to data sensitivity and threat model? Any obvious gaps? |
| Resilience | Single points of failure, retry/circuit-breaker patterns, failover strategy, data durability |
| Operability | Can it be deployed, monitored, scaled, and debugged in production? |
| Standards alignment | Does it follow enterprise technology standards and approved patterns? |
| Decision quality | Are key choices documented? Are trade-offs acknowledged? |
Step 3 — Produce Findings
For each issue found, use this format:
[SEVERITY] {Dimension}: {concise issue title}
Finding: {what specifically is wrong or missing}
Impact: {what happens if this isn't addressed}
Recommendation: {specific action, not just "improve this"}
Effort: S (<1 day) | M (1 week) | L (1 sprint) | XL (>1 sprint)
Severity levels:
- Critical — blocks approval; must be resolved before proceeding
- High — should be resolved before implementation
- Medium — should be addressed in the next iteration
- Low — improvement opportunity, not blocking
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.
- 2mo ago First seen · 113 lines · 112 tokens per session scan A 8702c56fb3df
arch-review is a skill published in the GitHub repository vijayjoshi24/ea-agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 112 tokens to every session and 928 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 2026-08-31.
arch-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| arch-reviewvijayjoshi24/ea-agent-skills | Skill | 5 | 6mo ago | MIT | A | 112 | Claude Code, Codex |
| autoreviewopenclaw/clawhub | Skill | 9.5k | yesterday | MIT | A | 17 | Claude Code, Codex |
| agent-teams-simplify-and-hardenLeoYeAI/openclaw-master-skills | Skill | 2.2k | 6d ago | MIT | A | 115 | Claude Code, Codex |
| omh-code-reviewrlaope/oh-my-hermes | Skill | 5.2k | yesterday | MIT | A | 48 | Claude Code, Codex |
| revdiff-planumputun/revdiff | Skill | 945 | 2d ago | MIT | A | 84 | Claude Code |
| explore-unknownsdzhng/skills | Skill | 1.4k | 2d ago | MIT | A | 162 | Claude Code, Codex |
| re0-mergeLilMGenius/paperthin | Skill | 1.4k | 4d ago | MIT | A | 70 | Claude Code |