model-selection

name	model-selection
description	Model Selection Optimization (COST-003) — recommends optimal AI models for tasks given budget constraints, quality targets, and latency requirements. Computes the Pareto cost-quality frontier and supports mixed-model routing simulation.
version	1.0.0
dependencies	[{"skill":"cost-budgeting","optional":false},{"skill":"cost-aggregation","optional":true}]
entry_points	["scripts/model_selector.py"]
tests	["tests/test_model_selector.py"]
coverage_minimum	85

COST-003: Model Selection Optimization

Recommends the optimal AI model for a task given cost, quality, and latency constraints.

API

ModelSelector

from scripts.model_selector import ModelSelector

selector = ModelSelector()

recommend_model(task_type, input_tokens, output_tokens, constraints=None)

Recommend the single best model for a task.

Parameters:

• task_type (str): Task category (code_review, documentation, security_audit, general, etc.)
• input_tokens (int): Expected input token count
• output_tokens (int): Expected output token count
• constraints (dict, optional):
  • max_cost (float): Maximum cost per task in USD
  • quality_target (float): Minimum quality score [0.0, 1.0]
  • max_latency_sec (float): Maximum acceptable latency in seconds
  • provider_preference (list[str]): Ordered list of preferred providers

Returns: dict with keys model, provider, estimated_cost, estimated_quality, estimated_latency_sec, reasoning, _selection_time_ms

recommend_batch(tasks)

Recommend models for multiple tasks, tracking cumulative cost.

cost_quality_frontier(task_type, input_tokens, output_tokens, providers=None)

Compute the Pareto-optimal cost/quality frontier.

simulate_model_mix(mix, daily_tasks, avg_tokens, task_type="general")

Predict daily cost and quality for a hypothetical routing mix.

Integration

• COST-001 (CostBudgeter): uses same rate structure from src/config/models.yaml
• COST-002 (CostAggregator): optional, pass as cost_aggregator= parameter for richer cost estimates

Quality Tiers

Tier	Score Range	Example Models
mini	0.52–0.67	gpt-4o-mini
haiku	0.55–0.70	claude-haiku-4.5, gemini-flash
sonnet	0.82–0.90	claude-sonnet-4.5/4.6, gpt-4o
opus	0.95–0.98	claude-opus-4.8

Self-Improvement

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

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

skill_feedback:
  - skill_name: model-selection
    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.