santa-loop

santa-loop is a command for coding agents from mturac/everything-openai-codex. It costs 19 tokens per session (1,504 once invoked), scanned A, original, MIT.

A review process that sends the same work to two independent reviewers and requires both to approve it before it can ship. If either reviewer finds a problem, the process asks for fixes and runs the review again.

In plain words
What is it for?
Use it to review changed files or a task output before pushing the code, with repeated review rounds after fixes.
Why use it?
It reduces the chance that one reviewer misses bugs, security issues, weak error handling, or other problems in the final code.

Command

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.

agentmods
npx agentmods add commands/mturac/everything-openai-codex/santa-loop
Clone the repo
git clone --depth 1 https://github.com/mturac/everything-openai-codex

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.

agentmods badge for santa-loop

README.md
[![agentmods](https://agentmods.dev/badge/commands/mturac/everything-openai-codex/santa-loop.svg)](https://agentmods.dev/commands/mturac/everything-openai-codex/santa-loop)
Your own site
<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/santa-loop"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/santa-loop.svg" alt="Measured on agentmods" height="20"></a>
Per session 19 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,504 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00019 $0.01504
Opus 5 $0.00010 $0.00752
Sonnet 5 $0.00004 $0.00301
Haiku 4.5 $0.00002 $0.00150

Measured yesterday against content hash 6345a129ed3b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

santa-loop 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 yesterday.

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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

commands/santa-loop.md · 176 lines

How it starts

The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Santa Loop

Adversarial dual-review convergence loop using the santa-method skill. Two independent reviewers — different models, no shared context — must both return NICE before code ships.

Purpose

Run two independent reviewers (Codex Deep + an external model) against the current task output. Both must return NICE before the code is pushed. If either returns NAUGHTY, fix all flagged issues, commit, and re-run fresh reviewers — up to 3 rounds.

Usage

/santa-loop [file-or-glob | description]

Workflow

Step 1: Identify What to Review

Determine the scope from $ARGUMENTS or fall back to uncommitted changes:

git diff --name-only HEAD

Read all changed files to build the full review context. If $ARGUMENTS specifies a path, file, or description, use that as the scope instead.

Step 2: Build the Rubric

Construct a rubric appropriate to the file types under review. Every criterion must have an objective PASS/FAIL condition. Include at minimum:

Criterion Pass Condition
Correctness Logic is sound, no bugs, handles edge cases
Security No secrets, injection, XSS, or OWASP Top 10 issues
Error handling Errors handled explicitly, no silent swallowing
Completeness All requirements addressed, no missing cases
Internal consistency No contradictions between files or sections
No regressions Changes don't break existing behavior

Add domain-specific criteria based on file types (e.g., type safety for TS, memory safety for Rust, migration safety for SQL).

Step 3: Dual Independent Review

Launch two reviewers in parallel using the Agent tool (both in a single message for concurrent execution). Both must complete before proceeding to the verdict gate.

Each reviewer evaluates every rubric criterion as PASS or FAIL, then returns structured JSON:

{
  "verdict": "PASS" | "FAIL",
  "checks": [
    {"criterion": "...", "result": "PASS|FAIL", "detail": "..."}
  ],
  "critical_issues": ["..."],
  "suggestions": ["..."]
}

Read the full file on GitHub · 176 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.

  1. yesterday First seen · 176 lines · 19 tokens per session scan A 6345a129ed3b

Subscribe to this mod's changes

santa-loop is a command published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 11d ago), licensed MIT. It adds 19 tokens to every session and 1,504 once invoked, about $0.0001 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 2026-09-03.