Automatically applies when choosing LLM models and providers. Ensures proper model comparison, provider selection, cost optimization, fallback patterns, and multi-model strategies.
77
54%
Does it follow best practices?
Impact
93%
1.25xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/ai-llm/model-selection/SKILL.mdModel registry and task-based routing
ModelRegistry class
62%
100%
Full model metadata
100%
100%
Capabilities fields
100%
100%
Quality tier values
0%
100%
Pricing in MTok units
100%
100%
ModelRouter class
100%
100%
Named rules with priority
100%
100%
Rule condition callables
100%
100%
Default fallback model
0%
0%
No hardcoded model IDs at call sites
100%
100%
Routing logic documented
100%
100%
Three traffic patterns routed
100%
100%
Cost estimation and batch cost analysis
MTok pricing unit
100%
100%
estimate_cost function
80%
100%
Batch analysis: total_cost_usd
100%
100%
Batch analysis: avg_cost_per_request
100%
100%
Batch analysis: total_tokens
25%
100%
Batch analysis: cost_per_1k_tokens
0%
100%
At least 3 models compared
100%
100%
find_cheapest_model logic
100%
100%
Model registry used
100%
100%
cost_report.txt produced
100%
100%
Reads requests.csv
100%
100%
Cost logged per request
100%
0%
Fallback chain and provider reliability
FallbackChain class
30%
100%
Ordered fallback attempt
100%
100%
model_used in result
100%
100%
fallback_occurred in result
0%
100%
response in result
37%
100%
Warning logged on failure
100%
100%
Exception on all failures
100%
100%
Exception message format
37%
50%
ModelRegistry integration
0%
100%
Demo runs without API keys
100%
100%
Demo shows fallback_occurred=True
20%
100%
632759f
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