# Nixtla Usage Optimizer

> 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.

- Skill: `jeremylongshore/nixtla-usage-optimizer` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add jeremylongshore/nixtla-usage-optimizer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jeremylongshore/nixtla-usage-optimizer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- License: MIT
- Author: jeremylongshore (https://skillmd.com/u/jeremylongshore)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jeremylongshore/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:
```bash
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