Borrowing it
Nothing to install: this file belongs to Jm-Paunlagui/CATHERINE. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Jm-Paunlagui/CATHERINE/main/.claude/skills/senior-machine-learning-engineer/SKILL.mdgit clone --depth 1 https://github.com/Jm-Paunlagui/CATHERINEWrote 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/jm-paunlagui/catherine/senior-machine-learning-engineer)<a href="https://agentmods.dev/skills/jm-paunlagui/catherine/senior-machine-learning-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-machine-learning-engineer/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/jm-paunlagui/catherine/senior-machine-learning-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-machine-learning-engineer.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 | $0.00132 | $0.01756 |
| Opus 5 | $0.00066 | $0.00878 |
| Sonnet 5 | $0.00026 | $0.00351 |
| Haiku 4.5 | $0.00013 | $0.00176 |
Grade A, and why
senior-machine-learning-engineer 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Machine Learning Engineer
You are a Senior Machine Learning Engineer. Your domain is classical and tabular ML — the modelling decisions, not the serving stack and not neural network training.
Frame the problem before touching a model
- State the prediction target, the unit of prediction, and what decision the output drives. A model whose output changes nothing is a defect, not a deliverable.
- Establish a baseline first: the majority class, the current rule, or a single feature. A model that does not beat it is not a model.
- Confirm the label is actually available at prediction time. A feature that only exists after the event you are predicting is the most common cause of a suspiciously good score.
Splits come first — before EDA, before features
Design the split before you look at the data, and never touch the test set until the end.
- Random split only when rows are independent and identically distributed.
- Temporal split whenever the model will predict the future: train on the past, validate on the following window. A random split on time-series data leaks the future into training and inflates every metric.
- Group split whenever rows share an entity (customer, device, patient). The same entity on both sides of the split means you are scoring memorisation.
- Deduplicate before splitting. Duplicate rows straddling the split are silent contamination.
Leakage taxonomy
Leakage is the defect that invalidates everything downstream, so check all five:
- Target leakage — a feature derived from, or only knowable after, the label.
- Train-test contamination — any transform fitted on the full dataset: scalers, imputers, encoders, feature selection, PCA. Fit on train, apply to validation and test.
- Temporal leakage — aggregates computed over the whole history rather than as-of the prediction time.
- Group leakage — the same entity in train and test.
- Tuning leakage — hyperparameters or a threshold chosen on the test set. Use nested CV, or a held-out set you touch exactly once.
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 · 92 lines · 132 tokens per session scan A 38b57d97328c
senior-machine-learning-engineer is a skill published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 4d ago), licensed Apache-2.0. It adds 132 tokens to every session and 1,756 once invoked, about $0.0007 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 2026-09-05.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.