intent-solutions-io/nixtla-usage-optimizer

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

1. Error: No Nixtla usage found Solution: Repository may not use Nixtla - recommend adoption

2. Error: Cannot determine cost impact Solution: Add usage metrics or API call logging

3. Error: Mixed usage patterns Solution: Report both opportunities, prioritize high-impact

4. 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

• Routing Framework: See Error Handling section
• TimeGPT Pricing: https://nixtla.io/pricing
• StatsForecast Docs: https://nixtla.github.io/statsforecast/

Related Skills:

• nixtla-experiment-architect: Validate routing decisions
• nixtla-timegpt-finetune-lab: Evaluate fine-tuning ROI
• nixtla-prod-pipeline-generator: Implement routing in production