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 agentmods add skills/mayank-io/mstack/design-review-iterativenpx skills add mayank-io/mstack --skill design-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/design-review-iterative)<a href="https://agentmods.dev/skills/mayank-io/mstack/design-review-iterative"><img src="https://agentmods.dev/badge/skills/mayank-io/mstack/design-review-iterative.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.01316 |
| Opus 5 | $0.00025 | $0.00658 |
| Sonnet 5 | $0.00010 | $0.00263 |
| Haiku 4.5 | $0.00005 | $0.00132 |
Grade A, and why
design-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 4d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iterative Design Review
Run up to 4 review-fix iterations with 3 parallel reviewer agents (PE, Sr SDE, Domain Expert). Each iteration raises the bar for what gets fixed, converging quickly to a solid design.
Review Scope
By default, review the most recently written or edited design doc in docs/. If the user specifies a file, use that instead.
Process
Step 0: Initialize
Set iteration = 1. Identify the design doc to review. Read it fully before launching reviewers.
Step 1: Launch 3 Reviewer Agents in Parallel
Dispatch 3 agents simultaneously, each reviewing the same design doc 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 design document as a Principal Engineer. Focus on: architectural soundness, scalability, extensibility, abstraction boundaries, coupling/cohesion, performance implications, operational complexity, security posture, and whether the approach will hold up under real-world load and evolution. Flag missing trade-off analysis, unclear decision rationale, and unaddressed failure modes. Categorize every finding as BLOCKER, HIGH, MEDIUM, LOW, or NIT.
Stay in your lane: You own AST/IR representation, grammar ambiguity analysis, parser/compiler architecture, and performance. Do NOT rule on whether a domain concept is semantically valid — that is the Domain Expert's call. If you think a domain assumption is wrong, flag it as a question for the Domain Expert, not as a finding.
Agent 2 — Senior SDE:
Review this design document as a Senior Software Engineer who will implement this. Focus on: implementation feasibility, missing technical details, gaps between design and codebase reality, unclear interfaces or contracts, missing error handling strategy, ambiguous naming, incomplete data flow, missing edge cases, and whether the milestones/tasks are actually achievable as scoped. Cross-reference with existing code patterns in the repo. Categorize every finding as BLOCKER, HIGH, MEDIUM, LOW, or NIT.
Stay in your lane: You own implementation feasibility, code-level concerns, and codebase consistency. Do NOT rule on domain semantics or make assumptions about what is valid in the problem domain. If a design decision seems wrong but is labeled as domain-driven, flag it as a question for the Domain Expert rather than overriding it.
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.
- 4d ago First seen · 100 lines · 50 tokens per session scan A 717aa139c25c
design-review-iterative is a skill published in the GitHub repository mayank-io/mstack (5 stars, last pushed 10d ago), licensed MIT. It adds 50 tokens to every session and 1,316 once invoked, about $0.0003 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 2026-08-31.
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