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Financial Report Automator

CLAWHUB · Unverified

Automated quarterly financial report generation with dividend-adjusted total return calculations. Use when generating stock performance reports, quarterly ea... Skill: Financial Report Automator Owner: terrycarter1985 Summary: Automated quarterly financial report generation with dividend-adjusted total return calculations. Use when generating stock performance reports, quarterly ea... Tags: latest:1.2.1 Version history: v1.2.1 | 2026-05-12T00:12:59.267Z | user Fix sort order bug: ensure data is always sorted newest-first; handle both 'dividend' and 'dividends' column names f

62
Rank
84
Safety
995
Downloads

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 995 downloads reported by the source. Last updated 10/11/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Install and run

Setup complexity: low.

clawhub skill install s17brwfrqyjhbjgadkvar20h8x8492g8:financial-report-automator
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-terrycarter1985-financial-report-automator/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

19,954 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: financial-report-automator
description: Automated quarterly financial report generation with dividend-adjusted total return calculations. Use when generating stock performance reports, quarterly earnings summaries, or return analytics for any ticker with CSV price data.
version: 1.2.1
tags: ['finance', 'reporting', 'quarterly', 'returns', 'dividends']
---

# Financial Report Automator

Generates quarterly stock performance reports with **dividend-adjusted total return**.

## What It Does

- Parses CSV price data (Yahoo Finance export format)
- Computes **price return** (raw close) and **total return** (adjusted close with dividends)
- Produces structured quarterly reports: start/end price, returns, high/low, avg volume, dividends paid

## Key Fix (v1.0.1)

Previous versions used raw close for return calculations, which **understated returns for dividend-paying stocks** like AAPL. v1.0.1 introduces:

1. **Column name normalization** — handles mixed-case CSV headers (`Date` → `date`)
2. **Adjusted close computation** — backward-adjusts prices for dividends so total return reflects reinvested dividends
3. **Separate price_return vs total_return** — both are reported for transparency
4. **Dividend tracking** — `dividend` column is optional; defaults to 0 if absent

## Usage

### As a Python module

```python
from scripts.stock_analyzer import StockAnalyzer

analyzer = StockAnalyzer(data_path="finance/AAPL.csv")

# 30-day total return (dividend-adjusted)
print(analyzer.calculate_returns(30))

# 30-day price return only (no dividends)
print(analyzer.calculate_returns(30, use_adj=False))

# Full quarterly report
report = analyzer.generate_quarterly_report(quarter=2, year=2026)
print(report)
```

### CLI

```bash
python3 scripts/stock_analyzer.py
```

## CSV Format Expected

```
Date,Close,Volume,Open,High,Low
01/02/2026,$269.77,21755774,$272.05,$277.82,$269.11
```

Optional columns:
- `dividend` — per-share dividend on ex-div date
- `adj_close` — pre-computed adjusted close (overrides internal calculation)

## Report Output

```json
{
  "quarter": "Q2 2026",
  "start_date": "2026-04-01",
  "end_date": "2026-06-30",
  "start_price": 223.0,
  "end_price": 235.5,
  "price_return_pct": 5.61,
  "total_return_pct": 6.12,
  "dividends_per_share": 0.25,
  "highest_price": 240.0,
  "lowest_price": 218.5,
  "average_volume": 35000000
}
```

## Requirements

- Python 3.10+
- pandas

_meta.json

{
  "ownerId": "kn72jmd20ws94jr0p8b3p24wyn82abgj",
  "slug": "financial-report-automator",
  "version": "1.2.1",
  "publishedAt": 1778544779267
}

skill-card.md

## Description:

Automated quarterly financial report generation with dividend-adjusted total return calculations for stock performance reports, quarterly earnings summaries, and return analytics from CSV price data.

This skill is ready for commercial/non-commercial use.

## Publisher:

[terrycarter1985](https://clawhub.ai/user/terrycarter1985)

### License/Terms of Use:

MIT-0

## Use Case:

External users, developers, and analysts use this skill to generate stock performance reports, quarterly earnings summaries, and return analytics from local CSV price data.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Generated stock reports may be mistaken for financial advice or live market data.

Mitigation: Treat generated reports as analytical output to verify against source CSV data and qualified financial review before acting on them.

Risk: The included script processes local stock CSV files with pandas, so results depend on the quality and freshness of the input data.

Mitigation: Use trusted CSV exports, verify expected columns and date ranges, and review calculations before relying on the report.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/terrycarter1985/skills/financial-report-automator)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, guidance]

**Output Format:** [Markdown guidance with Python code examples, shell commands, and JSON report structures]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Produces local CSV-based financial analysis guidance and structured quarterly report data for agent use.]

## Skill Version(s):

1.2.1 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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