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Dodaf

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dodaf

Knowledge base from DoD Architecture Framework (DoDAF) 2.02 — Volume II: Architectural Data and Models. Use for DoDAF viewpoints, DM2 meta-model, capability modeling, operational architecture, systems architecture, services architecture, data/information modeling, standards compliance, project/portfolio integration, and fit-for-purpose model selection. Does not cover DoDAF Volume I (policy/governance) or Volume III (enterprise-level guidance); also does not substitute for the live DISR or DARS registries.

Overview

Publisherjgsystemsconsulting
Repositoryjgs-se-knowledge-packs
Skill namedodaf
Stars
121
Forks
1
Bundled files
15
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.

  • 15 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 Dodaf 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/dodaf
  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/dodaf .claude/skills/dodaf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dodaf 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 Dodaf 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 Dodaf 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 Architecture Framework (DoDAF) 2.02 — Volume II: Architectural Data and Models

Source: US Department of Defense (public domain, Distribution A — approved for public release) | Chapters: 11

When to use

Use this skill when designing, reviewing, or querying DoDAF architectural descriptions, viewpoint models, or the DoDAF Meta-Model (DM2). It is the reference for any question about which DoDAF model to produce for a given decision-maker need, how to correctly structure DM2 data, how performers-activities-resources interrelate, or how DoDAF integrates with DoD acquisition and portfolio processes (JCIDS, DAS, PPBE, SE, OPS, CPM). Use it when selecting models, building traceability chains, specifying interfaces, or aligning architecture with standards.

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: viewpoint summary table, DM2 data groups, and the performer-activity-resource triad.
  • With a topic — ask about capability modeling, operational architecture, service-oriented design, data models, standards compliance, project integration, DOTMLPF analysis, fit-for-purpose selection.
  • With a model code — ask for CV-1, OV-5b, SV-1, DIV-2, PV-3, StdV-1, SvcV-4, etc.
  • With a chapter — ask for ch04 (Capability VP), ch06 (Operational VP), ch10 (Systems VP), etc.

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

Core Frameworks & Mental Models

The DoDAF Meta-Model (DM2)

The DM2 is the formal ontology (based on the IDEAS Foundation) that provides standard types, relationships, and glossary terms for all DoDAF work. It is implemented in Sparx Enterprise Architect with the IDEAS add-in. All DoDAF viewpoint models are presentations over DM2 data — produce the data once in the shared data store, then present it through whichever viewpoint models serve the identified decisions.

Fundamental principle — Fit-for-Purpose: DoDAF does not require architects to produce all models. Models are selected based on identified decision-maker needs. AV-1 and AV-2 are required for every architecture (DARS registration). All other models are optional — justified by the decisions they support.

The Performer-Activity-Resource Triad

The invariant kernel of every DoDAF model:

  • Performer (who) — person in role, organization, system, or service that performs an activity.
  • Activity (what) — transformation that consumes resources to produce resources.
  • Resource (with what / to what) — information, materiel, or performer produced or consumed.
  • Resource Flow Triple — the atomic interoperability unit: (producing activity) → (resource) → (consuming activity). All resource exchanges must be expressed as this triple; direct performer-to-performer transfers are not modeled.

Rules constrain Activities (never Performers directly). Performers are affected by rules only through the activities they perform.

Eight DoDAF Viewpoints

All Viewpoint (AV) — AV-1 Executive Summary (architecture overview, DARS registration) and AV-2 Integrated Dictionary (vocabulary grounded in seven genus terms: activity, resource, project, location, guidance, vision, property).

Capability Viewpoint (CV) — Seven models for capability portfolio managers. Core concept: desired resource state (effect) achieved by activities under conditions using rules and resources. CV-1 (effects/measures) → CV-2 (hierarchy) → CV-3 (schedules) → CV-4 (dependencies) → CV-5 (deployments) → CV-6 (activities) → CV-7 (services). DOTMLPF analysis is grounded here.

Data & Information Viewpoint (DIV) — Three-level stack addressing the same enterprise information at increasing concreteness: DIV-1 (conceptual — what must be understood), DIV-2 (logical — what data is required), DIV-3 (physical — how data is stored/exchanged). DIV-3 must satisfy every DIV-2 requirement; may not introduce new concepts.

Operational Viewpoint (OV) — Nine models describing who needs to do what, technology-independent. Systems/services appear only as constraints. OV-5b is the authoritative operational activity model; OV-5a is its strict subset. OV-6a/6b/6c add rules, state transitions, and sequences.

Project Viewpoint (PV) — Three models integrating architecture with DAS and PPBE. PV-1 (organizations-to-projects, WBS), PV-2 (schedules, Gantt, milestones, happens-in to fiscal years), PV-3 (projects-to-capabilities). PV-1 existence predicate: every modeled project must produce guidance or resources traceable to other viewpoint models.

Services Viewpoint (SvcV) — Twelve models for service-oriented architectures. SvcV-1 is foundational; all others derive from it. Services are performers with self-describing ServiceDescription elements. Includes non-IT services. SvcV-5 maps to OV-5b operational activities.

Standards Viewpoint (StdV) — Two models: StdV-1 (current standards profile, DISR-required for IT) and StdV-2 (standards succession forecast). Standards bind to Activities, not Performers. Conditions may scope when a standard applies to an activity.

Systems Viewpoint (SV) — Twelve models for systems engineers. SV-1 is foundational (system inventory, interfaces); SV-4 for functional decomposition; SV-5a for OV traceability; SV-5b for capability contribution; SV-8/SV-9 for evolution planning; SV-10a/10b/10c for behavioral specification. Services may not appear in SV-1/SV-4 as primary performers.

Eleven DM2 Data Groups

Principle (8): Performers, Resource Flows, Information & Data, Rules, Capabilities, Services, Projects, Organizational Structures.

Supporting (3): Measures (magnitude of attributes), Locations (spatial extents), Pedigrees (origin/history of resources).

Key cross-mapping: all 8 viewpoints draw on some subset of these 11 groups; the exact mapping is in Table 3.3-1 of the source document.

Key Architecture Integration Points

  • CV↔PV integration via PV-3: capabilities (CV) are delivered by projects (PV-3); this is the PPBE-to-operational-need link.
  • OV↔SV integration via SV-5a: operational activities (OV-5b) are supported by system functions (SV-4); traced in SV-5a.
  • OV↔SvcV integration via SvcV-5: operational activities (OV-5b) are supported by service functions; traced in SvcV-5.
  • StdV↔DIV integration: DIV-3 data implementations must comply with StdV-1 metadata standards.
  • SV/SvcV↔StdV integration via StdV-2+SV-8: standards succession timelines drive system evolution planning.

Chapter Index

#FileKey content
1ch01-introduction-and-dodaf-concepts.mdDoDAF purpose, fit-for-purpose principle, IDEAS Foundation, DM2 overview, six DoD core processes
2ch02-dm2-data-groups-overview.mdAll 11 data groups defined, process-to-viewpoint mapping table, performer-activity-resource triad
3ch03-all-viewpoint-av.mdAV-1 Executive Summary, AV-2 Integrated Dictionary, DARS registration, genus-differentia definitions
4ch04-capability-viewpoint-cv.mdCV-1 through CV-7, desired effects, measures of effect/desire, DOTMLPF analysis, capability configurations
5ch05-data-information-viewpoint-div.mdDIV-1/2/3 three-level stack, conceptual/logical/physical abstraction, mesodata/metadata/microdata
6ch06-operational-viewpoint-ov.mdOV-1 through OV-6c, technology-independent operational modeling, OV-5a/5b relationship, mission threads
7ch07-project-viewpoint-pv.mdPV-1/2/3, WBS, program elements, PPBE integration, DAS milestone linkage, happensIn fiscal years
8ch08-services-viewpoint-svcv.mdSvcV-1 through SvcV-10c, service descriptions, business vs. enabling services, SOA traceability
9ch09-standards-viewpoint-stdv.mdStdV-1 current profile, StdV-2 forecast, DISR requirement, standards-to-activity binding
10ch10-systems-viewpoint-sv.mdSV-1 through SV-10c, system composition, SV-5a traceability, SV-8/9 evolution, behavioral spec
11ch11-dm2-data-groups-detail.mdDeep reference for all 8 principle data groups, assignment vs. allocation, resource flow triple mechanics

Topic Index

  • Capability modeling → ch04
  • Capability-to-project traceability → ch04, ch07
  • Configuration items (CI/CSCI) → ch11
  • DARS registration → ch03
  • Data modeling (conceptual/logical/physical) → ch05
  • DISR compliance → ch09
  • DM2 meta-model → ch01, ch02, ch11
  • DOTMLPF analysis → ch04, ch11
  • Earned value management → ch07
  • Fit-for-purpose model selection → ch01, ch02
  • IDEAS Foundation → ch01, ch11
  • Information modeling → ch05
  • Interface identification → ch10, ch08
  • JCIDS → ch04, ch11
  • Measures (MoE, MoD, QoS) → ch02, ch04, ch10
  • Mission threads → ch06
  • Operational architecture → ch06
  • Organizational relationships → ch06, ch11
  • Portfolio management → ch04, ch07
  • PPBE integration → ch07
  • Project scheduling → ch07
  • Publish-subscribe → ch11
  • Resource flow triple → ch02, ch11
  • Service-oriented architecture → ch08
  • Services (non-IT) → ch08
  • Standards compliance → ch09
  • System functions → ch10
  • Systems engineering → ch10, ch11
  • Traceability (OV to SV) → ch10
  • Viewpoint selection → ch01, ch02
  • WBS → ch07
  • Vocabulary / terminology → ch03

Supporting Files

  • glossary.md — ~60 defined terms from the DM2 and DoDAF viewpoints, with precise definitions grounded in the source document
  • patterns.md — 13 When/How/Trade-offs patterns covering fit-for-purpose selection, interface management, DOTMLPF analysis, capability traceability, and standards transition planning
  • cheatsheet.md — Quick-reference tables (viewpoints, data groups, process mapping, model derivation rules, DM2 relationships), decision rules, and tells-and-smells diagnostic guide

Scope & Limits

This knowledge pack covers DoDAF 2.02 Volume II (Architectural Data and Models) only: the DM2 ontology, eight viewpoints with their models, eleven data groups, and their integration with DoD acquisition and portfolio processes. It does not cover DoDAF Volume I (policy, governance, and the six-step architecture development process), Volume III (enterprise-level architecture guidance), the live DISR or DARS registries, or DoDAF-aligned tools (Sparx EA, IBM Rational, etc.). The source document is US Department of Defense (public domain, Distribution A — approved for public release, Chg 1, 31 January 2015); redistribution and synthesis are permitted.

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 Dodaf AI skill do?

Knowledge base from DoD Architecture Framework (DoDAF) 2.02 — Volume II: Architectural Data and Models. Use for DoDAF viewpoints, DM2 meta-model, capability modeling, operational architecture, systems architecture, services architecture, data/information modeling, standards compliance, project/portfolio integration, and fit-for-purpose model selection. Does not cover DoDAF Volume I (policy/governance) or Volume III (enterprise-level guidance); also does not substitute for the live DISR or DARS registries.

Why use Dodaf on TypingMind?

Because you install it once and use it with any model. Dodaf 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 Dodaf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs/tree/main/packs/dodaf. 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 Dodaf?

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 Dodaf?

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

Is the Dodaf 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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