clari-local-dev-loop
Set up local development for Clari API integrations with mock data. Use when building forecast dashboards, testing export pipelines, or iterating on Clari data transformations locally. Trigger with phrases like "clari dev setup", "clari local testing", "develop with clari", "clari mock data".
Best use case
clari-local-dev-loop is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Set up local development for Clari API integrations with mock data. Use when building forecast dashboards, testing export pipelines, or iterating on Clari data transformations locally. Trigger with phrases like "clari dev setup", "clari local testing", "develop with clari", "clari mock data".
Teams using clari-local-dev-loop should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/clari-local-dev-loop/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How clari-local-dev-loop Compares
| Feature / Agent | clari-local-dev-loop | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Set up local development for Clari API integrations with mock data. Use when building forecast dashboards, testing export pipelines, or iterating on Clari data transformations locally. Trigger with phrases like "clari dev setup", "clari local testing", "develop with clari", "clari mock data".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Clari Local Dev Loop
## Overview
Local development workflow for Clari integrations: mock forecast data for offline testing, schedule recurring exports, and build data transformation pipelines.
## Prerequisites
- Completed `clari-install-auth` setup
- Python 3.10+ or Node.js 18+
- Local database or data warehouse access for testing
## Instructions
### Step 1: Project Structure
```
clari-integration/
├── src/
│ ├── clari_client.py # API client wrapper
│ ├── export_pipeline.py # Export and transform pipeline
│ ├── models.py # Data models for forecast data
│ └── config.py # Environment config
├── tests/
│ ├── fixtures/
│ │ ├── forecast_export.json # Sample export response
│ │ └── job_status.json # Sample job status
│ └── test_pipeline.py
├── .env.local # Dev credentials (git-ignored)
├── .env.example
└── requirements.txt
```
### Step 2: Mock Forecast Data for Testing
```python
# tests/fixtures/forecast_export.json
MOCK_FORECAST = {
"entries": [
{
"ownerName": "Jane Smith",
"ownerEmail": "jane@example.com",
"forecastAmount": 250000,
"quotaAmount": 300000,
"crmTotal": 180000,
"crmClosed": 120000,
"adjustmentAmount": 15000,
"timePeriod": "2026_Q1"
},
{
"ownerName": "Bob Johnson",
"ownerEmail": "bob@example.com",
"forecastAmount": 180000,
"quotaAmount": 250000,
"crmTotal": 140000,
"crmClosed": 90000,
"adjustmentAmount": 0,
"timePeriod": "2026_Q1"
}
]
}
```
### Step 3: Test Pipeline Without API Calls
```python
# tests/test_pipeline.py
import pytest
from src.export_pipeline import transform_forecast_data
def test_forecast_aggregation():
data = MOCK_FORECAST
result = transform_forecast_data(data)
assert result["total_forecast"] == 430000
assert result["total_quota"] == 550000
assert result["attainment_percent"] == pytest.approx(78.2, rel=0.1)
assert len(result["reps"]) == 2
def test_handles_empty_export():
result = transform_forecast_data({"entries": []})
assert result["total_forecast"] == 0
```
### Step 4: Development Run Script
```bash
#!/bin/bash
# scripts/dev-export.sh
set -euo pipefail
source .env.local
echo "=== Clari Dev Export ==="
python3 src/export_pipeline.py \
--forecast "company_forecast" \
--period "2026_Q1" \
--format json \
--output ./data/latest-export.json
echo "Export saved to ./data/latest-export.json"
echo "Records: $(jq '.entries | length' ./data/latest-export.json)"
```
## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| Import error | Missing dependency | `pip install -r requirements.txt` |
| Empty export | Wrong time period | Use a period with submitted forecasts |
| Mock data stale | Schema changed | Re-download a sample from API |
| `.env.local` not loading | Missing dotenv | `pip install python-dotenv` |
## Resources
- [Clari API Reference](https://developer.clari.com/documentation/external_spec)
- [pytest Documentation](https://docs.pytest.org)
## Next Steps
See `clari-sdk-patterns` for production-ready API wrappers.Related Skills
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