Machine Learning Engineer Agent Skill

Machine Learning Engineer

name: evaluation-report--machine-learning-engineer

description: Expert skill for Evaluation Report — machine-learning-engineer

license: MIT

metadata:

author: theNeoAI <[email protected]>

Skill Summary

| Field | Value |

|-------|-------|

| Name | machine-learning-engineer |

| Version | 5.0.0 |

| Quality Tier | Exemplary ⭐⭐ |

| Rubric Score | 9.2/10 |

| Line Count | 494 |

6-Dimension Rubric Scores

| Dimension | Score | Weight | Weighted | Tier |

|-----------|-------|--------|----------|------|

| System Prompt Depth | 9.0 | 20% | 1.80 | Exemplary |

| Domain Knowledge Density | 9.5 | 25% | 2.375 | Exemplary |

| Workflow Actionability | 9.0 | 15% | 1.35 | Exemplary |

| Risk Documentation | 8.5 | 10% | 0.85 | Expert |

| Example Quality | 9.0 | 20% | 1.80 | Exemplary |

| Metadata Completeness | 9.5 | 10% | 0.95 | Exemplary |

Strengths

§1 System Prompt — Exemplary

• Principal engineer identity at Google/Meta/Netflix scale (billions of predictions daily)
• Professional DNA table (4 attributes: Feature Engineer, Model Architect, Scale Optimizer, Production Focused)
• Core Competencies table (5 domains: Frameworks, Training, Features, Deployment, Optimization) with scale evidence
• Decision Framework: 5-gate hierarchy matching the rubric dimensions
• 5 Thinking Patterns: Baseline-First, Feature-Centric, Training-Serving Skew Prevention, Reproducible Experiments, Production-First Design
• Each pattern includes specific practices
• Verdict: Exemplary

§2 What This Skill Does

• 5 capabilities: Feature Engineering, Model Development, Distributed Training, Model Optimization, Production ML Systems
• Measurable outcomes

§3 Risk Documentation — Strong

• 6 risks (3 🔴 Critical, 2 🟠 High, 1 🟡 Medium)
• Critical risks: overfitting, training-serving skew, data leakage
• Specific mitigations

§4 Core Philosophy

• ML System Architecture (6-layer ASCII diagram)
• 5 guiding principles

§5 Professional Toolkit

• 7 categories with specific tools (PyTorch, TensorFlow, JAX, XGBoost, Horovod, MLflow, TorchServe, TensorRT, Feast)
• Clear use case for each

§6 Domain Knowledge

• Model Selection Guide (5 problem types)
• Distributed Training Methods (4 methods with scaling)
• Inference Optimization (5 techniques with speedup ratios)
• Verdict: High density, specific metrics

§7 Standard Workflow

• 4 phases (Problem Definition, Feature Engineering, Model Development, Production Deployment) over 25 days
• [✓ Done]/[✗ FAIL] criteria

§8 Scenario Examples

• 5 full scenarios: Recommendation System, Fraud Detection, CV Model, NLP Sentiment, Time Series Forecasting
• Each with Features → Model → Optimization → Results structure
• Specific metrics (20% watch time increase, 10ms p99 latency, 87% top-1 accuracy, 92% F1)
• Diverse coverage across ML domains

§9 Common Pitfalls

• 6 anti-patterns (over-engineering, data leakage, class imbalance, no validation, feature overfitting, neglecting inference cost)
• Specific to ML engineering

§10 Scope & Limitations

• Clear ✓/✗ with specific skill references

Weaknesses

❌ Missing §5 Platform Support (Severity: High)

• No platform installation section

❌ Missing Quality Verification Section

• §11 References exist pointing to 4 references/ files
• These files likely don't exist

❌ References Point to Non-Existent Files

• Same issue as ai-product-manager

❌ Risk Documentation Slightly Below Exemplary

• Could quantify more risks with specific dollar/metric impacts

Anti-Patterns Detected

| # | Anti-Pattern | Severity | Location |

|---|-------------|----------|----------|

| #9 | Platform Coverage Miss — §5 Platform Support absent | 🔴 High | Missing section |

| — | References to non-existent files | 🟡 Medium | §11 |

Token Budget Analysis

| Metric | Current | Target | Status |

|--------|---------|--------|--------|

| SKILL.md lines | 494 | ≤500 | ✅ Within budget |

| Room for platform section | ~6-10 lines | — | Need to trim elsewhere |

Recommendation

Tier: Exemplary ⭐⭐ (9.2/10)

Identical quality tier as ai-product-manager. The 11-section structure is the right choice for this domain. 5 diverse, quantified scenario examples with specific ML metrics. Same single blocking issue: missing platform support section.

Immediate actions required:

• Add §5 Platform Support table (~10 lines)
• Trim ~10 lines from existing content to stay under 500
• Verify/create the 4 references/ files

After fixes: Estimated score → 9.3/10 Exemplary ⭐⭐

One of the two best AI-ML skills in this batch. Platform support addition is the only blocker.

Workflow

Phase 1: Requirements

• Gather functional and non-functional requirements
• Clarify acceptance criteria
• Document technical constraints

Done: Requirements doc approved, team alignment achieved

Fail: Ambiguous requirements, scope creep, missing constraints

Phase 2: Design

• Create system architecture and design docs
• Review with stakeholders
• Finalize technical approach

Done: Design approved, technical decisions documented

Fail: Design flaws, stakeholder objections, technical blockers

Phase 3: Implementation

• Write code following standards
• Perform code review
• Write unit tests

Done: Code complete, reviewed, tests passing

Fail: Code review failures, test failures, standard violations

Phase 4: Testing & Deploy

• Execute integration and system testing
• Deploy to staging environment
• Deploy to production with monitoring

Done: All tests passing, successful deployment, monitoring active

Fail: Test failures, deployment issues, production incidents

Examples

Example 1: Standard Scenario

| Done | All steps complete |

| Fail | Steps incomplete |

Input: Design and implement a machine learning engineer solution for a production system

Output: Requirements Analysis → Architecture Design → Implementation → Testing → Deployment → Monitoring

Key considerations for machine-learning-engineer:

• Scalability requirements
• Performance benchmarks
• Error handling and recovery
• Security considerations

Example 2: Edge Case

| Done | All steps complete |

| Fail | Steps incomplete |

Input: Optimize existing machine learning engineer implementation to improve performance by 40%

Output: Current State Analysis:

• Profiling results identifying bottlenecks
• Baseline metrics documented

Optimization Plan:

• Algorithm improvement
• Caching strategy
• Parallelization

Expected improvement: 40-60% performance gain