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 commands/mturac/everything-openai-codex/santa-loopgit clone --depth 1 https://github.com/mturac/everything-openai-codexWrote 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/commands/mturac/everything-openai-codex/santa-loop)<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>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 | $0.00019 | $0.01504 |
| Opus 5 | $0.00010 | $0.00752 |
| Sonnet 5 | $0.00004 | $0.00301 |
| Haiku 4.5 | $0.00002 | $0.00150 |
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- santa-loop — 95% identical, 20 lines differ
- santa-loop — 88% identical, 32 lines differ
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": ["..."]
}
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.
- yesterday First seen · 176 lines · 19 tokens per session scan A 6345a129ed3b
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.
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