ASIMP :: Security Platform
ANSIBLE SYSTEM INTEGRITY MANAGEMENT PLATFORM // baselines & hardening
MODE:

Documentation

Home Gitbook & dsom summary index Asimp for ai agents: deep state of mind (dsom) protocol integration Ansible best practices & fqcn standards Ansible playbook and document architecture map Architecture & design Configuration & variables Review & adoption of dsom ansible configuration guide (v3.6.2) Diátaxis documentation framework Deep state of mind (dsom) governance framework System architecture & security posture Operational recipes index Execute tool workflows Legal notice & disclaimer Asimp local testing matrix & telemetry spec Lynis auditing & playbook analysis Openscap integration & playbook analysis Rootless podman 5+ & quadlet orchestration Asimp standard footer patcher reference Google okf frontmatter patcher reference Ansible config parameters reference Component & tool index Sovereign feedback collector reference Llm xml context compiler reference Role lynis ansible reference Mock scanning utility reference Openwiki emulator specification Sovereign os role patcher reference System core playbooks reference Jekyll pre processor reference Role reporting asimp reference Suse/sled sysctl hardening role reference Role update ubuntu asimp reference Sitemap & link integrity validator reference Local knowledge first & metadata discovery Troubleshooting & fallback behaviors Quickstart onboarding guide

Reports & SPA

Output of ASIMP Output of Lynis Output of OpenSCAP SPA Requirement Checklist

About ASIMP

ASIMP is an automated framework built with Ansible to assess host compliance, configure OS parameters, secure SSH, and verify integrity.

Diátaxis Documentation Framework

Ansible >= 9.0.0 OpenSCAP: CIS L2 Lynis: Enabled debsums: Enabled

Diátaxis Documentation Framework

ASIMP documents are organized using the Diátaxis Structuring Framework, which separates knowledge into four distinct quadrants based on the reader’s intent and context. This structural predictability benefits both human administrators and autonomous AI agents.

🧭 The Four Quadrants of Diátaxis

                          PRACTICAL
                              ^
                              |
             TUTORIALS        |       HOW-TO GUIDES
         (Learning-Oriented)  |    (Problem-Oriented)
                              |
                              +---------------------> USER WORK
                              |
             EXPLANATION      |       REFERENCE
         (Understanding-      |    (Information-
            Oriented)         |       Oriented)
                              v
                         THEORETICAL

1. Tutorials (Learning-Oriented)

  • Goal: Helping the newcomer start successfully.
  • Form: Guided, step-by-step onboarding walkthroughs.
  • Tone: Friendly, hands-on, educational.

2. How-To Guides (Task-Oriented)

  • Goal: Helping experienced users solve specific, practical problems.
  • Form: Copy-pasteable recipes, operational scripts, execution blocks.
  • Tone: Concise, direct, recipe-style.

3. Reference Material (Information-Oriented)

  • Goal: Providing precise, objective descriptions of the system.
  • Form: API tables, configuration variable lists, file paths, CLI flags, exit states.
  • Tone: Absolute technical precision. Zero hand-holding.

4. Explanation (Understanding-Oriented)

  • Goal: Explaining architectural choices, governance models, and design trade-offs.
  • Form: Conceptual descriptions, Mermaid graphs, and system topologies.
  • Tone: Strategic, explanatory, analytical.

🧠 Strategic Merging with DSOM Protocol

By matching DSOM’s Progressive Disclosure to Diátaxis quadrants:

  • L1-Overview corresponds to Explanation.
  • L2-Operational corresponds to Tutorials and How-To Guides.
  • L3-TechnicalReference corresponds to Reference Material.

This allows autonomous crawlers to index and consume only the necessary quadrant depending on their immediate execution phase, saving critical context bandwidth.