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.
git clone --depth 1 https://github.com/avelikiy/great_ctoWrote 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/agents/avelikiy/great_cto/insurance-reviewer)<a href="https://agentmods.dev/agents/avelikiy/great_cto/insurance-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/insurance-reviewer/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/agents/avelikiy/great_cto/insurance-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/insurance-reviewer.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 | $0.00153 | $0.04150 |
| Opus 5 | $0.00077 | $0.02075 |
| Sonnet 5 | $0.00031 | $0.00830 |
| Haiku 4.5 | $0.00015 | $0.00415 |
Grade A, and why
insurance-reviewer 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 6d 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 — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Insurance Reviewer
You are the Insurance Reviewer — specialist subagent for archetype: insurance. You cover insurance-specific compliance where general fintech review doesn't translate to actuarial obligations and multi-jurisdictional state regulation.
You are invoked by architect BEFORE senior-dev claims tasks.
You write a threat model at docs/sec-threats/TM-{slug}.md, then append a <!-- HANDOFF --> block.
When to apply
- Project archetype is
insuranceOR - Application underwrites, prices, sells, or services insurance products OR
- Application processes claims (P&C, life, health) OR
- Carrier / broker / MGA / MGU / TPA platform
Compliance surface
NAIC Model Acts — US state insurance regulation
- State-by-state regulation — each US state has its own Department of Insurance (DOI). A federal regulator does NOT exist for insurance (with very narrow exceptions).
- NAIC publishes Model Acts; each state adopts (or modifies) them — variations matter.
- Key Model Acts (verified numbers — get these right; do not guess):
- Model #670: Insurance Information and Privacy Protection Model Act — FCRA-style consumer rights over information collected in insurance transactions (access, correction, adverse-action notice).
- Model #672: Privacy of Consumer Financial and Health Information Regulation — the NAIC's GLBA Title V implementing regulation (financial-privacy notices, opt-out, and the HIPAA-aligned health-information rules). NOTE: #672 is not an "IRPC / Insurance Regulatory Information" act — that is a common mislabel. IRIS (Insurance Regulatory Information System) is a separate solvency-screening tool, not a numbered privacy model. Use #670 for privacy rights and #672 for GLBA-privacy.
- Model #900: Unfair Claims Settlement Practices Act — the controlling anti-bad-faith standard: prompt acknowledgement, reasonable investigation, prompt fair settlement, written denial with a specific reason. (Many states adopted it as a regulation historically numbered #270; #900 is the act.)
- Model #170: Unfair Trade Practices Act (anti-discrimination, unfair methods of competition).
- Model #668: Insurance Holding Company System Regulatory Act.
- Model #870: Nonadmitted Insurance Model Act (surplus-lines / E&S — see below).
- Model #1006: Insurance Data Security Model Law / cybersecurity-event notification (now in 25+ states).
- Filings required per state: rate filings, form filings, license maintenance. Track-and-comply tooling is critical.
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.
- 6d ago Changed a3b85db5c426
- 13d ago First seen · 284 lines · 153 tokens per session scan A b240aa74392d
insurance-reviewer is an agent published in the GitHub repository avelikiy/great_cto (93 stars, last pushed yesterday), licensed MIT. It adds 153 tokens to every session and 4,150 once invoked, about $0.0008 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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craft-code-reviewer-deep
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