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 sec-s1-analysisgit 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/sec-s1-analysis)<a href="https://agentmods.dev/skills/octagonai/skills/sec-s1-analysis"><img src="https://agentmods.dev/badge/skills/octagonai/skills/sec-s1-analysis/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/sec-s1-analysis"><img src="https://agentmods.dev/badge/skills/octagonai/skills/sec-s1-analysis.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 | $0.00050 | $0.01813 |
| Opus 5 | $0.00025 | $0.00907 |
| Sonnet 5 | $0.00010 | $0.00363 |
| Haiku 4.5 | $0.00005 | $0.00181 |
Grade A, and why
sec-s1-analysis 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 12d 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEC S-1 Analysis
Analyze S-1 registration statements (IPO filings) for companies going public using the Octagon MCP server.
Prerequisites
Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See references/mcp-setup.md for installation instructions.
Workflow
1. Identify Analysis Parameters
Determine the following before querying:
- Company Name: Name of the company filing for IPO
- Focus Area (optional): Risks, opportunities, financials, capitalization
- Specific Topics (optional): Use of proceeds, shareholders, governance
2. Execute Query via Octagon MCP
Use the octagon-agent tool with a natural language prompt:
Analyze the S-1 registration statement for <COMPANY> and extract key business risks and opportunities.
MCP Call Format:
{
"server": "octagon-mcp",
"toolName": "octagon-agent",
"arguments": {
"prompt": "Analyze the S-1 registration statement for Figma and extract key business risks and opportunities."
}
}
3. Expected Output
The agent returns structured S-1 analysis including:
Key Business Risks:
- Customer retention and growth challenges
- Technological and market competition
- Regulatory and operational risks
- Internal management and control concerns
Key Opportunities:
- Product innovation and platform enhancements
- Customer expansion and conversion
- International growth
- Strategic acquisitions
Data Sources: octagon-sec-agent
4. Interpret Results
See references/interpreting-results.md for guidance on:
- Understanding S-1 structure
- Evaluating IPO risks
- Assessing business model viability
- Analyzing capitalization and ownership
Example Queries
Full S-1 Analysis:
Analyze the S-1 registration statement for Figma and extract key business risks and opportunities.
Business Model:
Extract the business model and revenue streams from Stripe's S-1 filing.
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.
- 12d ago First seen · 288 lines · 50 tokens per session scan A 97a27ad3b58b
sec-s1-analysis is a skill published in the GitHub repository OctagonAI/skills (127 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 1,813 once invoked, about $0.0003 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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