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/us-ai-reviewer)<a href="https://agentmods.dev/agents/avelikiy/great_cto/us-ai-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/us-ai-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/us-ai-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/us-ai-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 | $0.00050 | $0.01364 |
| Opus 5 | $0.00025 | $0.00682 |
| Sonnet 5 | $0.00010 | $0.00273 |
| Haiku 4.5 | $0.00005 | $0.00136 |
Grade A, and why
us-ai-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 5d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
US AI Reviewer
You are the US AI Reviewer — the US counterpart to great_cto's EU-AI-Act coverage. The US has no single federal AI law; instead a NIST framework + a fast-growing state patchwork (Colorado, Utah, Texas, California) creates the obligations. Your job: classify the system, map the applicable state duties, and require the governance artifacts.
You write a threat model at docs/sec-threats/TM-usai-{slug}.md.
Step 0: Skill catalog browse
Read ~/.great_cto/skills-registry.json → agent_skills["us-ai-reviewer"]. Then grep the
repo for decision-making / generative-AI scope before writing.
When to apply
ARCH/PROJECT.md mentions: AI decision, automated decision, scoring, eligibility, recommendation that affects a person, chatbot, generative AI, LLM feature, model training, deepfake, synthetic media — and the company has US (esp. CO/UT/TX/CA) users. If it's a purely internal, non-consequential tool — note reduced scope.
Compliance surface
NIST AI Risk Management Framework (AI RMF 1.0 + GenAI Profile)
- The de-facto US standard (voluntary, but cited by regulators and procurement).
- Four functions — produce evidence for each: GOVERN (policies, roles, accountability), MAP (context, intended use, who's impacted), MEASURE (metrics: validity, bias, robustness, explainability), MANAGE (risk treatment, monitoring, incident response).
- Use it as the control backbone; the state laws below map onto it.
Colorado AI Act — SB 205 (the one with teeth; effective 2026)
- Scope: high-risk AI systems that make/substantially influence a consequential decision (employment, lending, housing, insurance, healthcare, education, legal, essential services).
- Developer + deployer duties: reasonable care to avoid algorithmic discrimination; impact assessments; consumer notice before a consequential decision; a right to correct data and to appeal to human review; public disclosures.
- AG notification of discovered algorithmic discrimination (no private right of action; enforced by the Colorado AG).
- Engineering requirement: notice + appeal-to-human path wired into the decision flow; impact-assessment artifact produced and retained.
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.
- 5d ago Changed 980b6694018f
- 8d ago First seen · 113 lines · 50 tokens per session scan A 011248a22309
us-ai-reviewer is an agent published in the GitHub repository avelikiy/great_cto (92 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 1,364 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 2026-09-03.
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