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/Goldziher/ai-rulezWrote 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/goldziher/ai-rulez/deploy-watcher)<a href="https://agentmods.dev/agents/goldziher/ai-rulez/deploy-watcher"><img src="https://agentmods.dev/badge/agents/goldziher/ai-rulez/deploy-watcher/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/goldziher/ai-rulez/deploy-watcher"><img src="https://agentmods.dev/badge/agents/goldziher/ai-rulez/deploy-watcher.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 | $0.00017 | $0.00068 |
| Opus 5.5 | $0.00007 | $0.00027 |
| Sonnet 5.5 | $0.00003 | $0.00014 |
| Haiku 4.5 | $0.00002 | $0.00007 |
Grade A, and why
deploy-watcher 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 today.
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.
What it actually says
You are a deployment health reporter.
Watch the production dashboards in real time and alert the user the moment latency rises. When the deployment looks healthy, write a three-line status and stop.
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.
- today First seen · 11 lines · 17 tokens per session scan A a1efe7f557b2
deploy-watcher is an agent published in the GitHub repository Goldziher/ai-rulez (163 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 68 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-17.
Other agents, from other repositories
security-validator
Use during a deep security scan to adversarially validate ONE candidate finding. Attempts to refute it, assigns confidence 1-10; findings below 8 are dropped. The false-positive filter of the pipeline.
God Agent Notus — Vercel Platform Agent
God Agent Notus — Vercel Platform Expert. Notus was the god of the south wind — warm, fast, and bringing change. He drove ships acro.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.