A coordinated review of existing code, system design, visual design, or infrastructure by ten specialist viewpoints, followed by a shared discussion and summary.

Use it to review a codebase, pull request, architecture, design, or infrastructure setup and produce combined findings and decision points for the product owner.

It brings different kinds of problems into one review and helps separate important decisions from individual suggestions. It also checks that the project has documented deployment tiers before reviewing it.

team-review — Skill for Claude Code by MotWakorb/ai-agent-dev-team

Install

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.

Any agent

npx skills add MotWakorb/ai-agent-dev-team --skill team-review

Clone the repo

git clone --depth 1 https://github.com/MotWakorb/ai-agent-dev-team

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/motwakorb/ai-agent-dev-team/team-review/github.svg)](https://agentmods.dev/skills/motwakorb/ai-agent-dev-team/team-review)

Your own site

<a href="https://agentmods.dev/skills/motwakorb/ai-agent-dev-team/team-review"><img src="https://agentmods.dev/badge/skills/motwakorb/ai-agent-dev-team/team-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/motwakorb/ai-agent-dev-team/team-review"><img src="https://agentmods.dev/badge/skills/motwakorb/ai-agent-dev-team/team-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 5,147 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00043	$0.05147
Opus 5.5	$0.00017	$0.02059
Sonnet 5.5	$0.00009	$0.01029
Haiku 4.5	$0.00004	$0.00515

Measured 2mo ago against content hash da1dca7b7c42, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

team-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.

team-review/SKILL.md · 451 lines

How it starts

The opening of the file, as written. The whole thing — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Team Review Session

This skill orchestrates a parallel review session across all ten personas. Each persona reviews the target independently from their domain perspective, then the team comes together to debate findings and produce a unified assessment with decision points for the PO.

Preflight: Verify Onboarding & Effective Tier

Before any other step, verify deployment-tier setup. Defaulting to enterprise rigor across the board is the failure mode this preflight prevents.

Check COMPONENTS.md exists at the repo root. If missing, refuse to run and tell the PO:

This project hasn't been onboarded yet. Run /onboard first — it produces COMPONENTS.md, which records each component's deployment tier (home-lab / small-team / startup / enterprise). Without it, personas calibrate to enterprise rigor across the board. See _shared/deployment-tier.md for the tier model.

Do not proceed.
Identify in-scope components for this run (from the review target — codebase path, PR scope, component under review).
Look up tiers in COMPONENTS.md. If an in-scope component is missing, ask the PO to add it (with reasoning) before proceeding.
Resolve cross-tier conflicts using strictest-wins by default. If applying that across the board produces clearly wasteful review findings (e.g., flagging a home-lab component for missing SOC 2 controls), surface it as a decision per _shared/deployment-tier.md.

Inject tier context into every agent prompt. Every prompt below must additionally include:

Read ~/.claude/skills/_shared/deployment-tier.md.
In-scope components and tiers: [component] ([tier]), ...
Effective tier for this work: [tier]
Calibrate your findings to the effective tier. Do not flag missing enterprise practices on home-lab components. If something would be a finding at a higher tier but isn't at this tier, note it as "at a higher tier this would be a finding" rather than as an actual finding.

Model Selection

Read the full file on GitHub · 451 lines

Changes

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 · 451 lines · 43 tokens per session scan A da1dca7b7c42

Subscribe to this mod's changes

team-review is a skill published in the GitHub repository MotWakorb/ai-agent-dev-team (2 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 5,147 once invoked, about $0.0002 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.

Code review

team-review next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
team-reviewMotWakorb/ai-agent-dev-team	Skill	2	2mo ago	MIT	A	43	Claude Code
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re0-mergeLilMGenius/paperthin	Skill	1.4k	4d ago	MIT	A	70	Claude Code