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 mayank-io/mstack --skill code-review-iterativegit clone --depth 1 https://github.com/mayank-io/mstackWrote 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/mayank-io/mstack/code-review-iterative)<a href="https://agentmods.dev/skills/mayank-io/mstack/code-review-iterative"><img src="https://agentmods.dev/badge/skills/mayank-io/mstack/code-review-iterative/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/mayank-io/mstack/code-review-iterative"><img src="https://agentmods.dev/badge/skills/mayank-io/mstack/code-review-iterative.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 | $0.01162 |
| Opus 5.5 | $0.00019 | $0.00465 |
| Sonnet 5.5 | $0.00009 | $0.00232 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
code-review-iterative 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iterative Code Review
Run up to 4 review-fix iterations with 3 parallel reviewer agents (PE, Sr SDE, QA). Each iteration raises the bar for what gets fixed, converging quickly to clean code.
Review Scope
By default, review unstaged changes from git diff. If the user specifies files, a branch, or a PR, use that scope instead.
Process
Step 0: Initialize
Set iteration = 1. Determine the review scope (git diff, specific files, or user-specified).
Step 1: Launch 3 Reviewer Agents in Parallel
Dispatch 3 agents simultaneously, each reviewing the same code from a different perspective. Every agent MUST categorize each finding as exactly one of: BLOCKER, HIGH, MEDIUM, LOW, NIT.
Agent 1 — Principal Engineer (PE):
Review this code as a Principal Engineer. Focus on: architecture, design patterns, abstraction quality, API contracts, performance implications, scalability concerns, and whether the approach is fundamentally sound. Categorize every finding as BLOCKER, HIGH, MEDIUM, LOW, or NIT. Include file path and line number for each.
Stay in your lane: You own architecture, AST/IR design, parser/compiler structure, and performance. Do NOT rule on whether a domain concept is semantically valid or whether a condition should accept certain inputs from a domain perspective — that is the Domain Expert's call. If you think a domain assumption in the code is wrong, flag it as a question rather than a finding.
Agent 2 — Senior SDE:
Review this code as a Senior Software Engineer. Focus on: correctness, edge cases, error handling, null/undefined safety, race conditions, resource leaks, naming, readability, DRY violations, and adherence to project conventions (check CLAUDE.md). Categorize every finding as BLOCKER, HIGH, MEDIUM, LOW, or NIT. Include file path and line number for each.
Stay in your lane: You own implementation correctness, code quality, and codebase consistency. Do NOT rule on domain semantics or override domain-driven design decisions. If a pattern seems wrong but is documented as a domain requirement, flag it as a question rather than a finding.
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 · 97 lines · 47 tokens per session scan A ddbba1d14a11
code-review-iterative is a skill published in the GitHub repository mayank-io/mstack (6 stars, last pushed 3d ago), licensed MIT. It adds 47 tokens to every session and 1,162 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.
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