model-requirements-definer
Agent: AI/ML Engineer
L2 AI/ML engineer (Nx) responsible for feasibility assessment, model requirements, ML architecture design, model training, MLOps pipeline building, evaluation, and performance monitoring.
Department ethos: ideal-engineering.md
Skill Description
Defines the requirements for an ML model including input/output schema, accuracy targets, latency budgets, fairness constraints, and operational boundaries.
When to Use
- When a new ML project begins and model success criteria have not been formalized.
- When product stakeholders have business objectives that need translation into measurable model metrics.
- When an existing model lacks documented requirements and needs retroactive specification for governance.
Workflow
- Business Objective Translation: Convert business goals (e.g., "reduce false positives by 30%") into measurable model metrics (e.g., "precision >= 0.85 at recall >= 0.70"). Deliverable: metric mapping document linking business objectives to model metrics.
- Input/Output Schema Definition: Define the model's input feature schema (feature names, types, ranges, nullability) and output schema (prediction type, confidence scores, explanation fields). Deliverable: I/O schema specification.
- Performance Constraints: Specify latency budgets (p50, p99 inference time), throughput requirements (predictions per second), model size limits (for edge deployment), and availability SLA. Deliverable: performance constraint document.
- Fairness and Safety Constraints: Define fairness requirements (maximum disparity across protected groups), safety boundaries (confidence thresholds for fallback to human review), and regulatory constraints (explainability requirements, right to explanation). Deliverable: fairness and safety specification.
Anti-Patterns
- Undefined success criteria: Starting model training without quantitative accuracy targets. Why: without targets, there is no objective basis for promotion decisions, and teams iterate indefinitely without convergence.
- Latency as afterthought: Defining accuracy requirements without latency budgets. Why: the most accurate model may be too slow for the serving context, forcing a last-minute architecture change or accuracy compromise.
Output
On success: Produces a model requirements document containing the business-to-metric mapping, I/O schema, performance constraints, and fairness/safety specifications. Delivered to the ML architecture and training teams.
On failure: Report which requirements could not be defined (e.g., business objectives too vague, no fairness policy), what assumptions were made, and what stakeholder decisions are needed.
Related Skills
1---2name: model-requirements-definer3description: This skill defines the requirements for an ML model including input/output schema, accuracy targets, latency budgets, and fairness constraints. Use when asked to specify model requirements, define ML acceptance criteria, or translate business needs into model constraints. Also consider when a model is trained without documented requirements. Suggest when the user starts model development without success criteria.4---56# model-requirements-definer78## Agent: AI/ML Engineer910L2 AI/ML engineer (Nx) responsible for feasibility assessment, model requirements, ML architecture design, model training, MLOps pipeline building, evaluation, and performance monitoring.1112Department ethos: [ideal-engineering.md](../../../../departments/engineering/ideal-engineering.md)1314## Skill Description1516Defines the requirements for an ML model including input/output schema, accuracy targets, latency budgets, fairness constraints, and operational boundaries.1718## When to Use1920- When a new ML project begins and model success criteria have not been formalized.21- When product stakeholders have business objectives that need translation into measurable model metrics.22- When an existing model lacks documented requirements and needs retroactive specification for governance.2324## Workflow25261. **Business Objective Translation**: Convert business goals (e.g., "reduce false positives by 30%") into measurable model metrics (e.g., "precision >= 0.85 at recall >= 0.70"). Deliverable: metric mapping document linking business objectives to model metrics.272. **Input/Output Schema Definition**: Define the model's input feature schema (feature names, types, ranges, nullability) and output schema (prediction type, confidence scores, explanation fields). Deliverable: I/O schema specification.283. **Performance Constraints**: Specify latency budgets (p50, p99 inference time), throughput requirements (predictions per second), model size limits (for edge deployment), and availability SLA. Deliverable: performance constraint document.294. **Fairness and Safety Constraints**: Define fairness requirements (maximum disparity across protected groups), safety boundaries (confidence thresholds for fallback to human review), and regulatory constraints (explainability requirements, right to explanation). Deliverable: fairness and safety specification.3031## Anti-Patterns3233- **Undefined success criteria**: Starting model training without quantitative accuracy targets. *Why*: without targets, there is no objective basis for promotion decisions, and teams iterate indefinitely without convergence.34- **Latency as afterthought**: Defining accuracy requirements without latency budgets. *Why*: the most accurate model may be too slow for the serving context, forcing a last-minute architecture change or accuracy compromise.3536## Output3738**On success**: Produces a model requirements document containing the business-to-metric mapping, I/O schema, performance constraints, and fairness/safety specifications. Delivered to the ML architecture and training teams.3940**On failure**: Report which requirements could not be defined (e.g., business objectives too vague, no fairness policy), what assumptions were made, and what stakeholder decisions are needed.4142## Related Skills4344- [`ai-feasibility-assessor`](../ai-feasibility-assessor/SKILL.md) -- Feasibility assessment that precedes or informs requirement definition.45- [`model-evaluation-runner`](../model-evaluation-runner/SKILL.md) -- Evaluates models against the requirements defined here.
mittuled