| name | nixtla-usage-optimizer |
| description | Audits Nixtla library usage and recommends cost-effective routing strategies. Scans TimeGPT, StatsForecast, and MLForecast patterns, identifies cost optimization opportunities, generates comprehensive usage reports, and suggests smart routing between models. Activates when user needs cost optimization, API usage audit, routing strategy design, or Nixtla cost reduction. |
| allowed-tools | Read,Glob,Grep |
| version | 1.0.0 |
| license | MIT |
Nixtla Usage Optimizer
Audit Nixtla library usage and recommend cost-effective routing strategies.
Overview
This skill analyzes and optimizes Nixtla usage:
- Usage scanning: Find all TimeGPT and baseline usage
- Cost analysis: Identify optimization opportunities
- Routing recommendations: Smart model selection
- ROI assessment: Cost vs accuracy trade-offs
Prerequisites
Required:
- Python 3.8+
- Existing Nixtla codebase to audit
No Additional Packages: Uses only Read, Glob, Grep tools
Instructions
Step 1: Scan Repository
Find all Nixtla library usage:
grep -r "NixtlaClient" --include="*.py" .
grep -r "StatsForecast" --include="*.py" .
grep -r "MLForecast" --include="*.py" .
Step 2: Analyze Patterns
Categorize usage by:
- Location (experiments, pipelines, notebooks)
- Frequency (how often called)
- Data characteristics (simple vs complex patterns)
Step 3: Generate Report
Create 000-docs/nixtla_usage_report.md with:
- Executive summary
- Usage analysis
- Recommendations
- ROI assessment
Step 4: Implement Routing
Apply recommendations:
- Replace TimeGPT with baselines for simple patterns
- Add TimeGPT for high-value forecasts
- Implement fallback chains
Output
- 000-docs/nixtla_usage_report.md: Comprehensive usage report
- routing_rules.json: Machine-readable routing logic (optional)
Error Handling
-
Error: No Nixtla usage found
Solution: Repository may not use Nixtla - recommend adoption
-
Error: Cannot determine cost impact
Solution: Add usage metrics or API call logging
-
Error: Mixed usage patterns
Solution: Report both opportunities, prioritize high-impact
-
Error: No baseline models found
Solution: Recommend adding StatsForecast for fallback
Examples
Example 1: Audit Existing Project
Scan results:
Found Nixtla usage:
- TimeGPT: 12 locations
- StatsForecast: 5 locations
- MLForecast: 2 locations
Recommendations:
1. Replace TimeGPT in 4 low-impact areas (save ~40%)
2. Add fallback to StatsForecast baselines
3. Keep TimeGPT for high-value forecasts
Example 2: No TimeGPT Yet
Scan results:
Found Nixtla usage:
- StatsForecast: 8 locations
- TimeGPT: 0 locations
Recommendations:
1. Add TimeGPT for 2 high-value forecasts
2. Keep baselines for simple patterns
3. Implement tiered routing
Resources
Related Skills:
nixtla-experiment-architect: Validate routing decisionsnixtla-timegpt-finetune-lab: Evaluate fine-tuning ROInixtla-prod-pipeline-generator: Implement routing in production
