1. Model Card Writer
Generates structured model documentation cards covering intended use, limitations, and evaluation results. Use this whenever you ship or update a model to give stakeholders and downstream teams a clear, standardized reference.
Copy-paste prompt
You are a machine learning engineer. Write a model card for [model name]. Include: model description, intended use cases, out-of-scope uses, training data summary, evaluation metrics ([metric 1]: [value], [metric 2]: [value]), known limitations, and bias/fairness considerations.
2. ML Experiment Log Writer
Documents experiment runs with hyperparameters, results, and key takeaways in a clean, reproducible format. Use this after each experiment run to keep your research log consistent and easy to share with the team.
Copy-paste prompt
You are a machine learning engineer. Write a structured experiment log for the following run: Model architecture: [describe]. Hyperparameters: [list key params]. Dataset: [describe]. Results: [metrics]. Compare to baseline: [baseline metrics]. Summarize what worked, what didn't, and the recommended next experiment.
3. ML System Design Document Writer
Drafts end-to-end ML system design documents covering data pipeline, training, serving, and monitoring. Use this when scoping a new ML system or presenting a design proposal to engineering leadership.
Copy-paste prompt
You are a machine learning engineer. Write a system design document for an ML system that [describe goal, e.g. 'predicts customer churn in real time']. Cover: problem framing, data pipeline design, feature engineering, model training approach, serving infrastructure, monitoring strategy, and known risks.
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4. Model Deprecation Notice Writer
Writes clear internal announcements when retiring a model version, including migration guidance. Use this to communicate model retirements to dependent teams well in advance of the deprecation date.
Copy-paste prompt
You are a machine learning engineer. Write an internal model deprecation notice for [model name], version [X]. Include: deprecation date, reason for retirement, recommended replacement model, migration steps for dependent teams, and a point of contact for questions.
5. ML Incident Post-Mortem Writer
Creates structured post-mortems for ML system failures including model drift, data pipeline issues, or prediction errors. Use this after any ML incident to document what happened, why, and how to prevent recurrence.
Copy-paste prompt
You are a machine learning engineer. Write a post-mortem for the following ML incident: [describe incident — e.g. model accuracy dropped 15% in production on [date]]. Include: incident timeline, root cause analysis, impact assessment, immediate remediation steps taken, and long-term preventive actions.