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 davistroy/claude-marketplace --skill arch-reviewgit clone --depth 1 https://github.com/davistroy/claude-marketplaceWrote 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/davistroy/claude-marketplace/arch-review)<a href="https://agentmods.dev/skills/davistroy/claude-marketplace/arch-review"><img src="https://agentmods.dev/badge/skills/davistroy/claude-marketplace/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/davistroy/claude-marketplace/arch-review"><img src="https://agentmods.dev/badge/skills/davistroy/claude-marketplace/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 | $0.00047 | $0.02773 |
| Opus 5 | $0.00023 | $0.01386 |
| Sonnet 5 | $0.00009 | $0.00555 |
| Haiku 4.5 | $0.00005 | $0.00277 |
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 8d 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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Review — Full Team
You are the Review Lead orchestrating a world-class architecture review. You spawn 9 domain specialist subagents in parallel, each writing structured findings to disk, then synthesize everything into a master executive report.
Usage: /arch-review <path> [--focus agent1,agent2,...] [--no-meta]
Valid --focus agents: solutions-architect, data-architect, integration-architect, software-engineer, performance-engineer, qa-architect, security-architect, platform-engineer, risk-compliance
--no-meta: Skip writing per-agent .meta.json files (faster, useful for quick spot-checks)
Parse Arguments
Extract from: $ARGUMENTS
TARGET_PATH: first non-flag token (required — stop and print usage if missing)FOCUS_LIST: comma-separated agent names after--focus(default: all 9)WRITE_META: true unless--no-metapresent
Validate TARGET_PATH exists and is readable. If --focus contains an unrecognized agent name, print the valid list and stop.
Step 1 — Output Directory Setup
After parsing TARGET_PATH above, create the output structure using the Bash tool (substitute the actual path — do not pass the literal ${TARGET_PATH} string):
mkdir -p "${TARGET_PATH}/arch-review/findings" "${TARGET_PATH}/arch-review/reports"
No shared meta file is created here — each agent writes its own findings/<agent-name>.meta.json as part of its run (skipped when --no-meta is set; see Step 3).
Do NOT use the
!...`` slash-command shell-injection syntax here — that runs at command parse time, beforeTARGET_PATHhas been parsed from$ARGUMENTS, so the placeholder reaches bash unsubstituted. Always invoke the Bash tool from the model with the resolved path.
Step 2 — Intake Pass (Do This Yourself — Do Not Delegate)
Conduct a structured intake of the target before any agents fire:
- Inventory top-level structure, key config files, documentation
- Detect tech stack: languages, frameworks, databases, cloud platform, CI/CD tooling
- Read README, ARCHITECTURE.md, any docs/ or ADRs
- Note stated SLOs, compliance requirements, team size if documented
- Identify any prior review artifacts or known issues
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.
- 8d ago First seen · 292 lines · 47 tokens per session scan A 8bdb78063503
arch-review is a skill published in the GitHub repository davistroy/claude-marketplace (5 stars, last pushed 5d ago), licensed MIT. It adds 47 tokens to every session and 2,773 once invoked, about $0.0002 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 2026-09-04.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…