model-engineer

name	model-engineer
description	Cost-quality optimization agent that analyzes tradeoffs, scores routing candidates, and proposes A/B tests. Use to generate task routing recommendations and experiment designs based on TokenAdvisor findings.
license	Proprietary
compatibility	Designed for agentic-engineers framework
metadata	{"author":"agentic-engineers","version":"1.0","category":"optimization","role":"senior-engineer","model":"sonnet-4-6","effort":"high","schedule":"0 17 * * *"}

Overview

Model Engineer analyzes TokenAdvisor findings and generates cost-quality optimizations, including task routing recommendations and A/B test proposals.

What it does:

1. Analyzes cost-quality tradeoffs by role and model
2. Scores task routing candidates (Engineer, Senior, Lead, Principal)
3. Generates feedback to TokenAdvisor findings
4. Proposes A/B test designs with hypotheses
5. Calculates efficiency (cost per quality point)
6. Recommends model upgrades and role shifts

Invocation

Manual Run

python scripts/model-engineer.py --analyze
python scripts/model-engineer.py --recommend --complexity medium
python scripts/model-engineer.py --feedback

Automated (Cron)

Daily at 17:15 UTC (15 min after TokenAdvisor) via orchestration/config/model-engineer.cron

0 17 * * * sleep 900 && cd <project-root> && python agentic-engineers/skills/model-engineer/scripts/model-engineer.py --analyze

Configuration

Configuration

• Cost target: $0.0016 per quality point
• Models: Haiku 4.5, Sonnet 4.6, Opus 4.8
• Roles: Engineer, Senior, Lead, Principal
• Input: TokenAdvisor output + agentic-engineers/data/metrics/
• Output: agentic-engineers/data/logs/model-engineer-YYYY-MM-DD.log

Integration

Input: TokenAdvisor recommendations + historical metrics
Output: Feeds to A/B Testing Framework (test proposals)
Dashboard: Model Performance (Grafana)

Scripts

• model-engineer.py — Main optimization agent (Sonnet 4.6)

Self-Improvement

This skill participates in the framework's continuous improvement cycle (see skill-improvement-feedback).

When you use model-engineer during a task, include a skill_feedback entry in your HANDBACK to help improve it over time:

skill_feedback:
  - skill_name: model-engineer
    effectiveness_score: 0.85        # required: 0.0–1.0
    clarity_score: 0.90              # optional
    coverage_gaps:
      - "Specific scenario the skill did not address"
    improvement_suggestions:
      - "Concrete change that would have helped"
    usage_context: "One sentence on how you used this skill"

Positive feedback is as valuable as critical feedback. Three or more feedback items for this skill automatically trigger an improvement task.