Keyboard shortcuts

Press ← or → to navigate between chapters

Press S or / to search in the book

Press ? to show this help

Press Esc to hide this help

Pretorin Developer & Agent Docs

Beta — Pretorin is currently in closed beta. Framework and control browsing works for authenticated users. Platform write features (evidence, narratives, monitoring) require a beta code. Sign up for early access.

Pretorin gives developers and AI agents direct access to compliance data, implementation context, and evidence workflows. The primary surfaces are the CLI, the MCP server, and skill-driven agent workflows. It supports 26 compliance frameworks and profiles, including NIST 800-53, NIST 800-171, FedRAMP, and CMMC.

The CLI and MCP tooling in this repository are open source under Apache-2.0. Access to Pretorin-hosted platform services, APIs, and account-scoped data is authenticated and governed separately by the applicable platform terms.

Start Here

Choose the path that matches how you work:

  • CLI-first — Use the Pretorin CLI directly for framework browsing, evidence workflows, reviews, scans, and agent execution.
  • AI-agent-first — Connect pretorin mcp-serve to Claude Code, Codex CLI, Cursor, or another MCP-compatible tool.
  • Hosted agent runtime — Use pretorin agent run when you want Pretorin-managed model execution with built-in skills.

Start with Installation, Authentication, and Quick Start if this is your first time here.

Core Paths

Pretorin is usually used in one of these modes:

  1. Bring-your-own-agent mode — Run pretorin mcp-serve and connect the MCP server to your existing AI tool (Claude Code, Codex CLI, Cursor, Windsurf, etc.). Your agent gets compliance tools without changing your workflow. Pair with pretorin skill install to give your agent explicit guidance for using pretorin (see “Bundled skill” below).

  2. Pretorin-hosted agent mode — Run pretorin agent run to use Pretorin’s built-in agent runtime when you don’t have your own local agent. Pretorin manages the AI runtime; you supply prompts.

  3. Direct CLI mode — Use pretorin subcommands directly for browsing frameworks, managing context, authoring evidence, updating narratives, and running scans. No agent involved.

Important architectural detail. The vast majority of the CLI and the entire MCP server are thin wrappers over the platform API — no LLM runs in pretorin in those paths. When you use mode 1 (your own agent via MCP), your local agent does all the reasoning. Pretorin is the tool surface. The only place pretorin runs its own LLM is pretorin agent run (mode 2), provided as a fallback for users without a local agent.

Bundled skill (pretorin skill install)

pretorin skill install copies a bundled skill into ~/.claude/skills/pretorin/ (or ~/.codex/skills/pretorin/). The skill is markdown + scripts that tell your agent how to use pretorin’s MCP tools effectively — which to call first, how to scope by system + framework, how to handle evidence and narrative writes. Highly recommended when using mode 1.

What You Can Do

  • Browse compliance frameworks — Query controls, families, metadata, and AI guidance from authoritative sources
  • Manage implementation context — Set an active system and framework, then track progress across controls
  • Create and manage evidence — Generate local evidence files, push them to the platform, and link them to controls
  • Write implementation narratives — Draft and push auditor-ready narratives for each control
  • Run AI-powered compliance tasks — Use the built-in Codex agent with bundled skills (gap-analysis, narrative-generation, evidence-collection, security-review, stig-scan, cci-assessment)
  • Run compliance recipes — Author or invoke recipe playbooks (markdown + scripts) that the calling agent executes for evidence capture, baseline scanning, and other procedures
  • Review code against controls — Analyze your codebase for control coverage
  • Track monitoring events — Record security scans, access reviews, configuration changes, and compliance checks
  • Generate compliance artifacts — Produce structured JSON artifacts documenting control implementations
  • Browse STIGs and CCIs — Look up STIG benchmarks, rules, and trace CCIs through the full control hierarchy
  • Manage vendors — Track third-party vendors, link evidence to vendor assessments, and upload vendor documents
  • Complete policy and scope questionnaires — Answer org-policy and scope questions through a guided workflow with AI-assisted generation and review

Architecture

Pretorin CLI is three things, with one shared API client at the bottom:

┌──────────────────────────────────────────────────────────────────────┐
│                            Developer                                  │
│                                                                       │
│   ┌──────────┐       ┌──────────────────┐      ┌──────────────────┐  │
│   │ Terminal │       │ Local AI Agent   │      │ pretorin agent   │  │
│   │ (direct  │       │ (Claude Code,    │      │ run              │  │
│   │  pretorin│       │  Codex CLI,      │      │ (Pretorin's own  │  │
│   │  cmds)   │       │  Cursor, ...)    │      │  CodexAgent — for│  │
│   │          │       │                  │      │  users w/o local │  │
│   │          │       │ + bundled        │      │  agent)          │  │
│   │          │       │   pretorin       │      │                  │  │
│   │          │       │   skill          │      │                  │  │
│   │          │       │   (optional)     │      │                  │  │
│   └────┬─────┘       └────────┬─────────┘      └────────┬─────────┘  │
│        │                      │                          │            │
│        │                      │ stdio                    │            │
│        │             ┌────────┴─────────┐                │            │
│        │             │  MCP Server      │                │            │
│        │             │  pretorin        │                │            │
│        │             │  mcp-serve       │                │            │
│        │             │                  │                │            │
│        │             │  Tool surface    │                │            │
│        │             │  (no LLM here)   │                │            │
│        │             └────────┬─────────┘                │            │
│        │                      │                          │            │
│        └──────────────┬───────┴──────────┬───────────────┘            │
│                       │                  │                            │
│              ┌────────┴──────────────────┴─────────┐                  │
│              │  Pretorin API Client                │                  │
│              │  (shared — only place that talks    │                  │
│              │   to the platform)                  │                  │
│              └────────────────┬────────────────────┘                  │
└───────────────────────────────┼───────────────────────────────────────┘
                                │
                       ┌────────┴─────────┐
                       │  Pretorin        │
                       │  Platform API    │
                       └──────────────────┘

Three things, one client:

  1. Direct CLI — pretorin <command> runs synchronously, talks to the platform via the shared client. No LLM.
  2. MCP server — pretorin mcp-serve exposes the same platform features as MCP tools. The local agent does all the reasoning; pretorin is the tool surface. No LLM runs in pretorin in this path.
  3. pretorin agent run — Pretorin’s own LLM, used when the user doesn’t have a local agent. Calls the same platform-API tools as the MCP server, just over a Python in-process boundary.

The bundled skill (pretorin skill install) is content delivered to the local agent’s skill directory; it’s not a fourth path, it’s a way to give path 2 better instructions.