Install
$ agentstack add skill-octagonai-skills-income-statement ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Income Statement
Retrieve real-time income statement data for public companies using Octagon MCP.
Prerequisites
Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See [references/mcp-setup.md](references/mcp-setup.md) for installation instructions.
Query Format
Retrieve real-time income statement data for , limited to records and filtered by period .
MCP Call:
{
"server": "octagon-mcp",
"toolName": "octagon-agent",
"arguments": {
"prompt": "Retrieve real-time income statement data for AAPL, limited to 5 records and filtered by period FY"
}
}
Output Format
The agent returns a table with absolute financial figures:
| Fiscal Year | Revenue (USD) | Net Income (USD) | EPS (Diluted) | |-------------|---------------|------------------|---------------| | 2025 | $416,161,000,000 | $112,010,000,000 | 7.46 | | 2024 | $391,035,000,000 | $93,736,000,000 | 6.08 | | 2023 | $383,285,000,000 | $96,995,000,000 | 6.13 | | 2022 | $394,328,000,000 | $99,803,000,000 | 6.11 | | 2021 | $365,817,000,000 | $94,680,000,000 | 5.61 |
Data Source: octagon-financials-agent
Key Observations Pattern
After receiving data, generate observations:
- Revenue trajectory: Calculate dollar and percentage changes year-over-year
- Net income trends: Track profitability in absolute terms
- EPS progression: Note earnings per share expansion or contraction
- Margin calculation: Compute Net Income / Revenue for net margin
- Scale context: Compare figures to industry peers
Metrics Reference
| Metric | Definition | |--------|------------| | Revenue | Total sales/top-line income for the period | | Net Income | Bottom-line profit after all expenses and taxes | | EPS (Diluted) | Earnings per share assuming all dilutive securities converted |
Analysis Tips
Revenue Scale
- Use to compare company size across industry
- Track absolute dollar growth, not just percentages
- Larger base requires more absolute growth to maintain % growth
Net Income Quality
- Compare Net Income to Operating Income for non-operating items
- Check for one-time gains/losses distorting figures
- Look for consistent growth trajectory
EPS Analysis
- EPS can grow faster than Net Income due to buybacks
- Compare to analyst estimates and guidance
- Check shares outstanding for context
Margin Calculation
Calculate from the data:
Net Margin = Net Income / Revenue × 100
Example: $112B / $416B = 26.9% net margin
Period Comparisons
- FY for annual strategic view
- Q for seasonal patterns and recent trends
- Compare same periods (Q1 vs Q1) for seasonality
Follow-up Queries
Based on results, suggest deeper analysis:
- "What factors contributed to the revenue growth in [YEAR]?"
- "How does [COMPANY]'s [YEAR] net margin compare to industry peers?"
- "What are the key drivers of the EPS expansion over the [N]-year period?"
- "Retrieve quarterly income statement data for [TICKER] to see seasonal patterns"
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: OctagonAI
- Source: OctagonAI/skills
- License: MIT
- Homepage: https://octagonai.co
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.