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Dod Digital Engineering

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jgsystemsconsulting
dod-digital-engineering

Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).

Overview

Publisherjgsystemsconsulting
Repositoryjgs-se-knowledge-packs
Skill namedod-digital-engineering
Stars
121
Forks
1
Bundled files
11
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jgsystemsconsulting on GitHub. Read the source before you install it.

Installation

Install the Dod Digital Engineering AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs/tree/main/packs/dod-digital-engineering
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs.git /tmp/jgs-se-knowledge-packs
mkdir -p .claude/skills
cp -r /tmp/jgs-se-knowledge-packs/packs/dod-digital-engineering .claude/skills/dod-digital-engineering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dod Digital Engineering in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dod Digital Engineering on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dod Digital Engineering is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DoD Digital Engineering Strategy (2018)

Source: OUSD R&E / ODASD(SE) (US Government work, public domain) | Chapters: 7

When to use

Use this skill when you need to reason about the Department of Defense Digital Engineering Strategy (2018, OUSD/ODASD(SE)) and its five goals: (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; and (5) transform the culture and workforce. This pack also covers model formalisms, provenance, governance and access control of the authoritative source of truth, and the coordinate-plan-pilot-sustain rollout sequence. It is a vision-and-policy source, not a how-to method or tool tutorial.

Prerequisites: none, plain Markdown; no MCP server, API key, or licence tier needed at runtime.

How to Use This Skill

  • Without arguments — load the Core Frameworks below: the definition of digital engineering, the five goals, and the central mental-model shifts.
  • With a topic — ask about a goal or focus area (e.g. "authoritative source of truth", "model formalisms", "Goal 4 infrastructure", "culture and workforce"), a shift (document-to-model, model-analyze-build), or a cited example (USS Ford, A-10, ERS, NAVAIR, Army LPDM/ePDM).
  • With a chapter — ch01 (intro/vision + the five goals), ch02–ch06 (Goals 1–5), ch07 (next steps + Appendix 1 summary).
  • Read it as intent, not procedure — the Strategy is a non-prescriptive "living document"; the concrete "how" lives in DoD Component implementation plans, which this pack does not contain.

Supporting files: glossary.md, patterns.md, cheatsheet.md.

Prerequisites: none — plain Markdown; no MCP server, API key, or licence tier needed at runtime.

Core Frameworks & Mental Models

What digital engineering is

Digital engineering is an integrated digital approach that uses authoritative sources of system data and models as a continuum across disciplines to support lifecycle activities from concept through disposal. The 2018 Digital Engineering Strategy — authored by ODASD(SE) with government, industry, and academia stakeholders — is the governing document. It is deliberately a vision and a compass, not a checklist: explicitly non-prescriptive and meant to evolve, it sets shared direction while the concrete execution lives in DoD Component / Service implementation plans.

The driver is mission urgency: deliver capability to the warfighter faster amid exponential technology change, rising complexity, tight budgets, and compressed schedules — problems the legacy linear, document-heavy, stove-piped process handles poorly. The system of interest is scoped broadly: systems of systems, systems, processes, equipment, products, and parts.

The Five Digital Engineering Goals

The entire Strategy is organized around five goals (Section IV summary; each expanded into focus areas in Section V):

  1. Formalize the development, integration, and use of models — making modeling deliberate practice that informs enterprise- and program-level decisions. → ch02
  2. Provide an enduring, authoritative source of truth — move the primary means of communication from documents to digital models and data. → ch03
  3. Incorporate technological innovation to improve the engineering practice (beyond traditional model-based methods). → ch04
  4. Establish a supporting infrastructure and environments so stakeholders can perform activities, collaborate, and communicate. → ch05
  5. Transform the culture and workforce — equipping the Department to adopt digital engineering, and sustain it, across the lifecycle. → ch06

Read goals 1–4 as building the machine (models, a trusted data backbone, infused technology, the environment to run them in) and goal 5 as building the people who run it. Adoption is the gating risk.

Goal → focus-area structure

Each goal decomposes into numbered focus areas (Appendix 1 is the full map):

  • Goal 1: 1.1 plan for models · 1.2 develop/integrate/curate models · 1.3 use models in decisions.
  • Goal 2: 2.1 define the authoritative source of truth · 2.2 govern it (access, controls, governance) · 2.3 use it across the lifecycle.
  • Goal 3: 3.1 establish an end-to-end digital engineering enterprise · 3.2 use technological innovations to improve the practice.
  • Goal 4: 4.1 IT infrastructures · 4.2 methodologies · 4.3 secure infrastructure & protect IP.
  • Goal 5: 5.1 improve the knowledge base · 5.2 lead & support transformation · 5.3 build & prepare the workforce.

Key constructs

  • Authoritative source of truth (AST) — the single, trusted central reference for models and data; captures the current state and history of the technical baseline, gives traceability as the system evolves, and propagates changes downstream. Its authority comes from governance: governance → data quality → stakeholder confidence → data-driven decisions.
  • Model formalisms — the quality rules (syntax, semantics, lexicons, standards) that let independently authored models combine into one coherent digital representation.
  • Provenance and pedigree — recorded model origin and lineage that, with V&V-based reviews, make a model trustworthy and reusable. Trust is engineered, not assumed.
  • Continuum of models — the same authoritative models carried and reused concept→disposal, not rebuilt at each phase boundary.

The mental-model shifts

  • Documents → models/data as the primary means of communication; documents become generated views, and stakeholders shift from accepting documents to accepting models.
  • Design-build-test → model-analyze-build — analyze and prove decisions in a virtual environment before physical build and fielding (virtual-first).
  • Many copies → one source of truth — ask "is this traced from the AST?" not "which copy is current?"
  • Stove-piped IT → a consolidated, collaborative, trusted environment (Goal 4's diagnosis and target).
  • Lock-in → standards & interfaces — bet on the seams between tools (standards, data, formats, interfaces), not a vendor product.
  • One-time rollout → sustained socio-technical change — culture is the operating system (Goal 5).

Rollout: coordinate → plan → pilot → sustain

The Strategy sequences four next steps: coordinate (ODASD(SE) convenes a summit and runs the standing Digital Engineering Working Group), develop implementation plans (owned by the DoD Components), implement pilot programs (learn/measure/optimize before scaling into major programs), and sustain (policy, guidance, training, continuous improvement). Ownership is distributed (Components own plans); coordination is central (ODASD(SE) is a gap-closer, not a gatekeeper).


Chapter Index

#SectionKey content
ch01Introduction, Purpose & VisionWhat digital engineering is; why now; the five goals introduced; the non-prescriptive "living document"; design-build-test→model-analyze-build; system of interest
ch02Goal 1 — Formalize ModelsPlan/develop/use models (1.1–1.3); model formalisms; provenance & pedigree; model-based reviews; the authoritative source of truth foundation; USS Ford example
ch03Goal 2 — Authoritative Source of TruthDefine/govern/use the AST (2.1–2.3); governance, access & controls; technical baseline; document-to-model acceptance shift; Army LPDM/ePDM example
ch04Goal 3 — Technological InnovationEnd-to-end digital enterprise (3.1); use of data, human-machine interaction, technology insertion (3.2); evolving digital representation; A-10 digital thread
ch05Goal 4 — Infrastructure & EnvironmentsIT infrastructure (4.1); methodologies (4.2); cybersecurity & IP protection (4.3); standards-over-tools; modular/cloud; ERS example
ch06Goal 5 — Culture & WorkforceCulture as shared values/behaviors; knowledge base & standards gap (5.1); transformation as change management (5.2); preparing the workforce (5.3); enablers; NAVAIR example
ch07Next Steps & Appendix 1Coordinate→plan→pilot→sustain; ODASD(SE) coordination & DoD Components; DEWG; the goal/focus-area summary table

Topic Index

  • A-10 example / digital thread → ch04
  • Access and controls → ch03
  • Appendix 1 (goal/focus-area summary) → ch07
  • Authoritative source of truth (AST) → ch03, ch02
  • Continuum of models → ch02, ch01
  • Culture (shared values and behaviors) → ch06
  • Cybersecurity (in digital engineering) → ch05
  • Define / govern / use the AST (2.1–2.3) → ch03
  • Design-build-test → model-analyze-build → ch01
  • Digital artifacts → ch03
  • Digital engineering (definition) → ch01
  • Digital Engineering Strategy (2018) → ch01, ch07
  • Digital Engineering Working Group (DEWG) → ch07
  • Document-to-model shift → ch03
  • End-to-end digital enterprise → ch04, ch01
  • Engineered Resilient Systems (ERS) → ch05
  • Evolving digital representation → ch04
  • Five goals (summary) → ch01, ch07
  • Focus areas (structure) → ch07
  • Formalize models (Goal 1) → ch02
  • Governance (of the AST) → ch03
  • Human-machine interaction → ch04
  • Infrastructure and environments (Goal 4) → ch05
  • Intellectual property protection → ch05
  • LPDM / ePDM (Army example) → ch03
  • Methodologies (Goal 4.2) → ch05
  • Model formalisms (syntax/semantics/lexicons) → ch02
  • Model provenance and pedigree → ch02
  • Model-based reviews and audits → ch02
  • NAVAIR SE Transformation → ch06
  • Next steps (coordinate/plan/pilot/sustain) → ch07
  • ODASD(SE) / DoD Components → ch07, ch01
  • Pilot programs → ch07
  • Standards and interfaces (over tools) → ch05
  • System of interest → ch01
  • Technical baseline → ch03
  • Technological innovation (Goal 3) → ch04
  • Technology insertion / infusion → ch04
  • Transformation as change management → ch06
  • USS Ford (CVN-78) example → ch02
  • Workforce (build and prepare) → ch06

Supporting Files

  • glossary.md — key terms from the Strategy, alphabetical, with chapter references
  • patterns.md — practitioner patterns (plan models first, engineer model trust, define→govern→use the AST, open-but-guarded access, standards-over-tools, managed technology infusion, transform the people, pilot before scaling) with When/How/Trade-offs
  • cheatsheet.md — decision rules, the five goals, goal→focus-area map, the mental-model shifts, the four next steps, cited examples, tells & smells

Scope & Limits

Covers: the DoD Digital Engineering Strategy (2018) in full — the definition and vision of digital engineering; the five goals and their focus areas; the document-to-model and design-build-test→model-analyze-build shifts; model formalisms, provenance/pedigree, and model-based reviews; the authoritative source of truth (define/govern/use, governance, access control, technical baseline); the end-to-end digital enterprise, data-driven decisions, human-machine interaction, and technology insertion; supporting infrastructure, methodologies, cybersecurity, and IP protection; the culture/workforce transformation and its enablers; and the coordinate→plan→pilot→sustain rollout with its cited Service examples (USS Ford, Army LPDM/ePDM, A-10, ERS, NAVAIR).

Does not cover: this is a vision-and-policy strategy, deliberately non-prescriptive — it provides no step-by-step method, tool tutorials, or implementation procedure. It is thin on quantitative metrics, detailed acquisition-policy mechanics, and the contents of DoD Component implementation plans (which it points to but does not contain). For MBSE/SysML mechanics use the sebok pack and SysML v2 / Cameo tooling; for SE process detail use dau-se-guidebook; for systems-security-engineering depth use nist-sse; for open/modular architecture see dod-mosa. The CAC-gated Digital Engineering Body of Knowledge (DEBoK) is intentionally excluded (not public).

Source version: DoD Digital Engineering Strategy, OUSD R&E / ODASD(SE), June 2018 (Distribution Statement A — approved for public release; distribution unlimited).

Jurisdiction: US Government public-domain work (17 U.S.C. 105). Freely reproducible, including commercially; attribution to the DoD is a courtesy, not an obligation, and the DoD does not endorse this pack.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dod Digital Engineering AI skill do?

Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of t...

Why use Dod Digital Engineering on TypingMind?

Because you install it once and use it with any model. Dod Digital Engineering is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dod Digital Engineering in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs/tree/main/packs/dod-digital-engineering. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dod Digital Engineering?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dod Digital Engineering?

As many as you like. As long as a model supports skills, you can use Dod Digital Engineering with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dod Digital Engineering AI skill free?

It is published on GitHub by jgsystemsconsulting. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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