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 Zaoqu-Liu/ScienceClaw --skill prismer-ml-experimentgit clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawWrote 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/zaoqu-liu/scienceclaw/prismer-ml-experiment)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/prismer-ml-experiment"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/prismer-ml-experiment/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/zaoqu-liu/scienceclaw/prismer-ml-experiment"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/prismer-ml-experiment.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 | $0.00027 | $0.00424 |
| Opus 5 | $0.00014 | $0.00212 |
| Sonnet 5 | $0.00005 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
ml-experiment 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 7d 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 Skill
Description
Design, implement, and evaluate machine learning experiments with reproducible workflows, proper baselines, and statistical analysis.
Tools Used
jupyter_execute- Execute ML code in Python (auto-switches to Jupyter)jupyter_notebook- Manage experiment notebooksupdate_notebook- Set up experiment cellsupdate_latex- Write experiment results to paperslatex_compile- Compile CS conference papers (auto-switches to LaTeX)arxiv_to_prompt- Read related work from arXiv papersupdate_notes- Write experiment logs and analysis summaries
Capabilities
Experiment Design
- Proper train/validation/test splits
- Cross-validation and bootstrap confidence intervals
- Ablation study design
- Hyperparameter search (grid, random, Bayesian)
Implementation
- PyTorch and TensorFlow model building
- Data loading and augmentation pipelines
- Training loops with logging and checkpointing
- Distributed training setup
Evaluation
- Standard metrics per task (accuracy, F1, BLEU, mAP, etc.)
- Statistical significance testing (paired t-test, bootstrap)
- Comparison with baselines
- Error analysis and visualization
Usage Patterns
Run an Experiment
When user says: "Train a model for [task]"
- Clarify dataset, metrics, and baselines
- Implement data loading and preprocessing
- Build model architecture
- Train with proper logging
- Evaluate and compare to baselines
- Report results with confidence intervals
Reproduce a Paper
When user says: "Reproduce [paper title/arXiv ID]"
- Fetch paper using arxiv_to_prompt
- Extract key method details
- Implement core algorithm
- Run experiments matching paper setup
- Compare results to reported numbers
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.
- 7d ago First seen · 58 lines · 27 tokens per session scan A 957fe1013b89
ml-experiment is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 27 tokens to every session and 424 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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