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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

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