Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OctagonAI/skills --skill balance-sheetgit clone --depth 1 https://github.com/OctagonAI/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/octagonai/skills/balance-sheet)<a href="https://agentmods.dev/skills/octagonai/skills/balance-sheet"><img src="https://agentmods.dev/badge/skills/octagonai/skills/balance-sheet/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/octagonai/skills/balance-sheet"><img src="https://agentmods.dev/badge/skills/octagonai/skills/balance-sheet.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.01045 |
| Opus 5 | $0.00023 | $0.00522 |
| Sonnet 5 | $0.00009 | $0.00209 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
balance-sheet scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Balance Sheet
Retrieve detailed balance sheet 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 for installation instructions.
Query Format
Retrieve detailed balance sheet statement data for <TICKER>, limited to <N> records and filtered by period <FY|Q>.
MCP Call:
{
"server": "octagon-mcp",
"toolName": "octagon-agent",
"arguments": {
"prompt": "Retrieve detailed balance sheet 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 | Total Assets (USD) | Total Current Assets (USD) | Total Non-Current Assets (USD) | Total Liabilities (USD) | Total Equity (USD) | Net Debt (USD) |
|---|---|---|---|---|---|---|
| 2025 | 359,241.00 million | 147,957.00 million | 211,284.00 million | 285,508.00 million | 73,733.00 million | 89,749.00 million |
| 2024 | 364,980.00 million | 152,987.00 million | 211,993.00 million | 308,030.00 million | 56,950.00 million | 89,116.00 million |
| 2023 | 352,583.00 million | 143,566.00 million | 209,017.00 million | 290,437.00 million | 62,146.00 million | 93,965.00 million |
| 2022 | 352,755.00 million | 135,405.00 million | 217,350.00 million | 302,083.00 million | 50,672.00 million | 108,834.00 million |
| 2021 | 351,002.00 million | 134,836.00 million | 216,166.00 million | 287,912.00 million | 63,090.00 million | 101,582.00 million |
Data Source: octagon-financials-agent
Key Observations Pattern
After receiving data, generate observations:
- Asset base changes: Track total assets trajectory over time
- Asset composition: Analyze current vs non-current asset mix
- Equity trends: Monitor shareholders' equity changes
- Leverage position: Track net debt levels and direction
- Capital structure: Compare liabilities to equity ratios
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 112 lines · 46 tokens per session scan A df7b12341389
balance-sheet is a skill published in the GitHub repository OctagonAI/skills (127 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 1,045 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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