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 NeoXider/neoxider-mcp-hub --skill ml-experiment-reviewgit clone --depth 1 https://github.com/NeoXider/neoxider-mcp-hubWrote 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/neoxider/neoxider-mcp-hub/ml-experiment-review)<a href="https://agentmods.dev/skills/neoxider/neoxider-mcp-hub/ml-experiment-review"><img src="https://agentmods.dev/badge/skills/neoxider/neoxider-mcp-hub/ml-experiment-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/skills/neoxider/neoxider-mcp-hub/ml-experiment-review"><img src="https://agentmods.dev/badge/skills/neoxider/neoxider-mcp-hub/ml-experiment-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 | $0.00000 | $0.00142 |
| Opus 5.5 | $0.00000 | $0.00057 |
| Sonnet 5.5 | $0.00000 | $0.00028 |
| Haiku 4.5 | $0.00000 | $0.00014 |
Grade A, and why
ml-experiment-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.
What it actually says
ML Experiment Review
Use this skill when reviewing an ML experiment, benchmark, ablation, or training run.
- State the hypothesis in falsifiable form.
- Identify the baseline, treatment, controlled variables, dataset split, seeds, and stopping rule.
- Separate observed measurements from interpretations.
- Check data leakage, train/eval contamination, metric selection, variance, and cherry-picking risks.
- Record the exact code revision, configuration, environment, artifacts, and commands needed to reproduce the result.
- Report uncertainty and failed runs. Do not turn a promising result into a product-readiness claim.
- End with the smallest next experiment that can discriminate between the leading explanations.
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 · 12 lines · 0 tokens per session scan A b14248fc31ab
ml-experiment-review is a skill published in the GitHub repository NeoXider/neoxider-mcp-hub (1 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 142 tokens. 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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