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/dynos-fit/dynos-work/ml-executornpx skills add dynos-fit/dynos-work --skill ml-executorgit clone --depth 1 https://github.com/dynos-fit/dynos-workWrote 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/dynos-fit/dynos-work/ml-executor)<a href="https://agentmods.dev/skills/dynos-fit/dynos-work/ml-executor"><img src="https://agentmods.dev/badge/skills/dynos-fit/dynos-work/ml-executor.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 | $0.00033 | $0.00182 |
| Opus 5 | $0.00016 | $0.00091 |
| Sonnet 5 | $0.00007 | $0.00036 |
| Haiku 4.5 | $0.00003 | $0.00018 |
Grade A, and why
execution/ml-executor 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.
What it actually says
dynos-work: execution/ml-executor
Spawn the ml-executor agent with the user's prompt as the instruction.
Ruthlessness Standard
- Do not let the executor hide weak assumptions behind model language or data-science jargon.
- If data shape, training flow, or inference contracts are unclear, the agent must inspect them before editing.
- Do not accept a change that improves one metric while making reproducibility, safety, or serving behavior ambiguous.
What to pass
Pass the user's full prompt verbatim. Do not summarize or sanitize it. Prepend a short hard wrapper that tells the agent to verify dataset/model contracts, fix the real mechanism, and only claim success with concrete evidence.
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 · 20 lines · 33 tokens per session scan A b4f045aa1610
execution/ml-executor is a skill published in the GitHub repository dynos-fit/dynos-work (2 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 182 once invoked, about $0.0002 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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