---
name: jeremylongshore/finta-local-dev-loop
source: https://app.decimal.ai/s/jeremylongshore-finta-local-dev-loop@1/SKILL.md
source_sha256: bcd1969eba9c
---

# Finta Local Dev Loop

## Overview

Finta is primarily UI-driven without a public API. For local automation, use CSV exports from Finta combined with Python scripts for analysis, reporting, and integration with other tools.

## Instructions

### Export Pipeline Data

1. In Finta, go to **Pipeline** > **Export** > **CSV**
2. Save as `pipeline-export.csv`

### Analyze Fundraise Pipeline

```python
import pandas as pd
from datetime import datetime

# Load Finta export
df = pd.read_csv("pipeline-export.csv")

# Pipeline summary
summary = df.groupby("Stage").agg(
    count=("Name", "count"),
    avg_check=("Check Size", "mean"),
).reset_index()

print("Pipeline Summary:")
print(summary.to_string(index=False))

# Conversion rates
stages = ["Researching", "Reaching Out", "Intro Meeting", "Follow-up", "Due Diligence", "Term Sheet", "Closed"]
for i in range(len(stages) - 1):
    current = len(df[df["Stage"] == stages[i]])
    next_stage = len(df[df["Stage"] == stages[i+1]])
    rate = (next_stage / current * 100) if current > 0 else 0
    print(f"  {stages[i]} -> {stages[i+1]}: {rate:.0f}%")
```

### Weekly Pipeline Report

```python
def generate_weekly_report(df: pd.DataFrame) -> str:
    total = len(df)
    active = len(df[df["Stage"].isin(["Intro Meeting", "Follow-up", "Due Diligence"])])
    term_sheets = len(df[df["Stage"] == "Term Sheet"])
    closed = len(df[df["Stage"] == "Closed"])

    return f"""
Fundraise Pipeline Report ({datetime.now().strftime('%Y-%m-%d')})
==================================================
Total investors: {total}
Active conversations: {active}
Term sheets: {term_sheets}
Closed: {closed}
"""
```

## Resources

- [Finta Website](https://www.trustfinta.com)

## Next Steps

See `finta-sdk-patterns` for integration patterns.