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/pegasi-ai/reinsWrote 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/plugins/pegasi-ai/reins/plugin)<a href="https://agentmods.dev/plugins/pegasi-ai/reins/plugin"><img src="https://agentmods.dev/badge/plugins/pegasi-ai/reins/plugin/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/plugins/pegasi-ai/reins/plugin"><img src="https://agentmods.dev/badge/plugins/pegasi-ai/reins/plugin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
reins 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 1mo 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.
What it actually says
{
"name": "reins",
"description": "Runtime security for AI agents. Blocks destructive actions before execution, routes high-risk operations through human approval, and maintains an immutable audit trail. Covers OWASP MCP Top 10, ASI Top 10, and Agentic Skills Top 10.",
"version": "0.0.23",
"author": {
"name": "Pegasi"
},
"homepage": "https://app.pegasi.ai",
"repository": "https://github.com/pegasi-ai/reins",
"license": "Apache-2.0",
"skills": "./skills",
"hooks": "./hooks/hooks.json",
"keywords": [
"security",
"ai-agents",
"human-in-the-loop",
"audit",
"governance",
"owasp",
"policy-enforcement"
]
}
What it installs
The manifest is a name and a version. 1 skill travel with it, and installing the plugin installs all of them — 58 tokens a session between them. Each is measured on its own page, and each can be installed alone.
What ships with it
1 file beside plugin.json 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.
- 1mo ago First seen · 23 lines scan A b95952596d7b
reins is a plugin published in the GitHub repository pegasi-ai/reins (392 stars, last pushed 5d ago), licensed Apache-2.0. Its token cost is not measured: this kind of file is read by the harness, not the model. 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.
Other plugins, from other repositories
signet
Cryptographic signing for every AI agent tool call — Ed25519 receipts + hash-chained audit log.
ethicore-guardian
Runtime AI-threat screening for Claude: analyze prompts, scan tool calls and outputs, and vet documents before you act, with BLOCK / CHALLENGE / ALLOW verdicts. Paid feature — needs a Pro-plan-or-higher Guardian API key.
aria-knowledge
ARIA — Applied Reasoning and Insight Architecture. Persistent human-governed knowledge for Claude Code via a five-phase lifecycle: capture → govern → promote → apply → refresh. Stages session insights/decisions/feedback into review backlogs, promotes approved items to a tag-indexed markdown base, and applies them via…
sentinel-ai
Real-time safety scanning for LLM interactions. Detects prompt injection, PII leaks, harmful content, toxicity, obfuscation, secrets, and dangerous tool calls — 600-case benchmark at 100% accuracy with sub-millisecond latency.
humanizer
Rewrite AI-sounding text so it reads like the writer without changing what it says.
nextjs
Official Next.js skills: adopt and optimize Cache Components, adopt Partial Prefetching, and verify runtime behavior against a running dev server.