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 frontier-ai-next/mgarlbot --skill code_executiongit clone --depth 1 https://github.com/frontier-ai-next/mgarlbotWrote 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/frontier-ai-next/mgarlbot/code_execution)<a href="https://agentmods.dev/skills/frontier-ai-next/mgarlbot/code_execution"><img src="https://agentmods.dev/badge/skills/frontier-ai-next/mgarlbot/code_execution/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/frontier-ai-next/mgarlbot/code_execution"><img src="https://agentmods.dev/badge/skills/frontier-ai-next/mgarlbot/code_execution.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 | $0.00014 | $0.00261 |
| Opus 5.5 | $0.00006 | $0.00104 |
| Sonnet 5.5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
code_execution 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.
What it actually says
Code execution
Use the code interpreter (sandboxed Python).
Simple math
result = 2 ** 10
print(f"2^10 = {result}")
Data handling
import json
data = {"name": "test", "values": [1, 2, 3]}
print(json.dumps(data, indent=2))
Commonly allowed modules (safe mode)
math,statisticsjsonredatetimecollectionsitertools,functoolsrandom
Rules
- Use
code_interpreterfor math and in-sandbox data work. - Use
shellfor filesystem operations on the host. - Imports are restricted — only allowed modules work.
- Always
print()results; otherwise output may be empty. - Prefer one coherent code block for multi-step logic.
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 First seen · 45 lines · 14 tokens per session scan A b76fe8e30d8a
code_execution is a skill published in the GitHub repository frontier-ai-next/mgarlbot (17 stars, last pushed 2mo ago), licensed MIT. It adds 14 tokens to every session and 261 once invoked, about $0.0001 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 2026-08-30.
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