Agent Security = use @nipmod

@nipmod is officially live for Codex as a plugin. You no longer have to leave your normal workflow or use Nipmod as a separate tool. Click “Add to Codex” on our website, follow the Codex prompt, and Nipmod becomes part of your agent setup. From there, Codex can use Nipmod before installing packages, cloning repositories, pulling Docker images, enabling MCP servers, or working with models and datasets. Nipmod searches and verifies software across sources like @github, @npmjs, @pypi, @huggingface 15 more sources and MCP servers. It checks trust, risk, source evidence, alternatives, and the install boundary before the agent makes a dependency decision. nipmod.com

most agents can tell you what decision they made. very few can tell you what that decision cost them. that’s why this integration matters. Nipmod helps agents decide. x402Books helps agents understand the financial impact of those decisions over time. Decision → Outcome. a small step toward making autonomous agents financially understandable.

Our first public collaboration is live: @nipmod × @x402Books Nipmod helps agents choose software and packages before they install or use them. @x402Books helps track what happens after those decisions. Agents should not only know what they picked, they should understand what that choice did. Did it save time? Did it create new costs? Did it improve the workflow? Did it become a bad dependency later? This is an early v0, but it connects two important layers: decision & outcome.

The last few weeks made one thing very clear: Cybersecurity is no longer only about endpoints, wallets, or smart contracts. The developer supply chain is now the front line. zcash:native had to coordinate an emergency Orchard remediation. Red Hat npm packages were compromised. New npm, PyPI, and Crates.io campaigns are targeting developer machines, CI/CD secrets, crypto tooling, AI workflows, and package installs. We do not celebrate attacks. Nobody should want users, maintainers, or teams to get hurt. But every incident makes the same point more clear: humans and agents need better package intelligence before they install, import, trust, or recommend anything. The more software gets built by humans and AI agents together, the more important it becomes to know what a package is, who is behind it, what changed, what it touches, and what risk it introduces. We are positioned in the right place: before the install, before the mistake, before the compromise. Use nipmod.com

Nipmod now uses Discord instead of Telegram We have not been very active publicly on X over the last few days, but we have been building a lot in the background. The GitHub repo is private for now because we are turning Nipmod into a serious product with clear ownership, product boundaries, and a sustainable future. Not everything we build should be given away unfinished and for free by default. Going forward, we will share more consistent updates on X Join the Discord: discord.gg/wYmatRDzk

This is exactly why we’re building Nipmod Software discovery needs a trust layer before execution, for humans AND for AI agents. Exact package version, install hooks, provenance, risk signals, approval boundary. @MsftSecIntel happy to compare notes nipmod.com

Welcome @_ditro to Nipmod! He will focus on security infrastructure, including safe code execution, sandboxing architecture, latency optimization, and privacy / zero-knowledge research. He brings backend experience across automation, infrastructure optimization, and secure environments. Step by step, we are bringing in the right people to build Nipmod into something that matters.

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This is exactly what we’re here for: AI agents shouldn’t blindly trust web pages, READMEs, package metadata, model cards or MCP descriptions. All of that is untrusted input until provenance, sandboxing and execution gates prove otherwise. Use @nipmod.

For people who are not deep in tech, this is the simplest way to understand Nipmod: Imagine the internet before Google. Everything existed, but finding the right thing was painful. Imagine knowledge before Wikipedia. Information existed, but there was no clean place to understand it quickly. That is roughly where AI agents are today with packages, models, repos and tools. They can write code. They can install software. They can connect APIs. They can use MCP servers. But before they touch a workspace, they still need a clean way to search, understand and judge what they are about to use. That is what we are building. A search and intelligence layer for AI agents before they touch external code, models or tools. Google helps humans find things. Wikipedia helps humans understand things. Nipmod helps agents find, understand and preflight the technical things they want to use. It does not replace npm, PyPI, GitHub, Hugging Face or MCP. It sits above them and gives agents context, trust signals and safe install plans before execution. That may sound simple. But so did search before the internet became impossible to navigate without it.

Raw JSON and methodology are public: If anyone has a harder package, model, repo or MCP case, send it. The point is not to make the benchmark easy for Nipmod. The point is to make the preflight layer harder, stricter and more useful for real agents. Full benchmark: nipmod.com/benchmark Raw JSON: nipmod.com/benchmark.json