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.
git clone --depth 1 https://github.com/zigrivers/scaffoldWrote 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/rules/zigrivers/scaffold/mmr-review)<a href="https://agentmods.dev/rules/zigrivers/scaffold/mmr-review"><img src="https://agentmods.dev/badge/rules/zigrivers/scaffold/mmr-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/rules/zigrivers/scaffold/mmr-review"><img src="https://agentmods.dev/badge/rules/zigrivers/scaffold/mmr-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 | $0.00078 | $0.01476 |
| Opus 5.5 | $0.00031 | $0.00590 |
| Sonnet 5.5 | $0.00016 | $0.00295 |
| Haiku 4.5 | $0.00008 | $0.00148 |
Grade A, and why
mmr-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 1mo 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mmr — Multi-Model Review
Dispatch code reviews across several AI model CLIs (Claude, Codex, Grok and
Antigravity by default; OpenCode opt-in), reconcile the findings, and gate on
severity. Its peer mmr critique does the same fan-out for a design and is
advisory (no gate).
Run a review
Pick the input mode that matches the target. Pass --sync --format json to get
reconciled findings back in a single call:
# GitHub PR (fetches the diff via `gh pr diff`)
mmr review --pr <number> --focus "what to focus on" --sync --format json
# Staged changes (pre-commit)
mmr review --staged --sync --format json
# All tracked uncommitted changes (excludes untracked files)
git diff HEAD | mmr review --diff - --sync --format json
# Branch / ref range
mmr review --base main --head <branch> --sync --format json
# A specific file's current contents (tracked-no-changes, untracked, or new)
(diff -u /dev/null path/to/file.ts || true) | mmr review --diff - --sync --format json
The --diff flag expects diff-format content (a .patch/.diff path, or -
for stdin). It does not read raw file content — wrap the target in a diff first.
The || true guard is required because diff exits 1 when files differ, which
breaks pipelines under set -o pipefail.
Severity gate
The verdict blocks on findings at or above fix_threshold (default P2; lower
severities are advisory). Override per run with --fix-threshold P0|P1|P2|P3.
Proceed only on pass or degraded-pass; fix blocking findings on blocked.
The verdict also reflects how many channels reported: fewer than
defaults.min_completed_channels (default 2) completing yields
needs-user-decision even with zero findings — one reviewer is not multi-model
review. Treat that as "fix the channels" (mmr doctor), not as a pass.
Bounded remediation cycles
Use a maximum of three rounds per review cycle. If round three confirms a reproducible defect within the original acceptance criteria or a required safeguard, make a concrete repair, add focused regression proof, pass the required gate, then start a new bounded cycle on the same PR. Reset to round one and review the new exact head under a new session id. On resume, recover the active cycle and round from the PR disposition ledger and MMR session history; use cycle one only when neither records a prior review.
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.
- 1mo ago Changed · +24 lines 6a86eec59c17
- 1mo ago First seen · 103 lines · 78 tokens per session scan A 5f85db9e4884
mmr-review is a cursor rule published in the GitHub repository zigrivers/scaffold (6 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 1,476 once invoked, about $0.0003 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.
Other cursor rules, from other repositories
code-review-checklist
A code-review checklist for 1C source files, including BSL and OS files. It groups findings by severity and checks issues such as security, performance, errors, access control, duplication, and maintainability.
20-agent-architect
System Architect agent role: pattern validation, Stage 1 PR review, ADR creation, architectural decisions.
language-agnostic-patterns
Language-agnostic programming patterns: SOLID, design patterns, clean code, and architecture. Load when refactoring, designing abstractions, or reviewing structure — not for everyday syntax.
cursor-asdd
ASDD contribution rules for this repository.
neuraforge
NeuraForge AI — Architecture Principles.
debug
Read agents/debug.md for full instructions. Follow AGENTS.md for architecture principles.