Domain Modeling
fossasia/eventyay-interpretation
Build and sharpen a project's domain model. An agent skill from fossasia/eventyay-interpretation.
Explore and evaluate domain models collaboratively before implementation.
$ npx skills add NTCoding/living-architecture --skill ddd -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NTCoding/living-architecture ddd --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NTCoding/living-architecture.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/ddd .claude/skills/ddd && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ddd" agent skill from https://github.com/NTCoding/living-architecture/tree/main/.agents/skills/ddd into .claude/skills/ddd/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ddd", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NTCoding/living-architecture/tree/main/.agents/skills/dddType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NTCoding/living-architecture --skill ddd -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NTCoding/living-architecture ddd --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NTCoding/living-architecture.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/ddd .agents/skills/ddd && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ddd" agent skill from https://github.com/NTCoding/living-architecture/tree/main/.agents/skills/ddd into .agents/skills/ddd/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ddd", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NTCoding/living-architecture --skill ddd -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NTCoding/living-architecture ddd --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NTCoding/living-architecture.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/ddd .cursor/skills/ddd && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ddd" agent skill from https://github.com/NTCoding/living-architecture/tree/main/.agents/skills/ddd into .cursor/skills/ddd/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ddd", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NTCoding/living-architecture.git --path .agents/skills/ddd--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NTCoding/living-architecture --skill ddd -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NTCoding/living-architecture ddd --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NTCoding/living-architecture.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/ddd .gemini/skills/ddd && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ddd" agent skill from https://github.com/NTCoding/living-architecture/tree/main/.agents/skills/ddd into .gemini/skills/ddd/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ddd", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NTCoding/living-architecture dddInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NTCoding/living-architecture --skill ddd -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NTCoding/living-architecture.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/ddd .github/skills/ddd && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ddd" agent skill from https://github.com/NTCoding/living-architecture/tree/main/.agents/skills/ddd into .github/skills/ddd/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ddd", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NTCoding/living-architecture --skill ddd -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NTCoding/living-architecture ddd --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NTCoding/living-architecture.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/ddd .opencode/skills/ddd && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ddd" agent skill from https://github.com/NTCoding/living-architecture/tree/main/.agents/skills/ddd into .opencode/skills/ddd/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ddd", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
dddExplore and evaluate domain models collaboratively before implementation.
Ddd is an agent skill from NTCoding/living-architecture. Explore and evaluate domain models collaboratively before implementation. Use for DDD modelling, domain concepts, aggregates, lifecycles, invariants, ownership, subdomain boundaries, or Rivière role questions where domain expertise and repository evidence must shape the model.
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Development, covering Domain-driven design. The repository describes itself as: Extra software architecture from your code as living documentation. AI-assisted. The licence is Apache-2.0.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit a24bc9f. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Ddd loads about 4k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 2,013 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NTCoding/living-architecture at commit a24bc9f, republished under its Apache-2.0 licence (© NTCoding). 2,013 words, ~3,980 tokens.
.claude/skills/ddd/SKILL.md (or your agent's skills folder).Act as an exploratory domain modeller. Build a shared understanding with the user before converging on a model. Do not optimise for the design that is quickest to implement.
Before presenting the first problem interpretation:
The guide is a map of the implemented model. It is not proof that the current model is correct. Do not regenerate it during a modelling discussion.
Consult
docs/architecture/domain-terminology/contextive/definitions.glossary.yml for
the domain language encountered during the investigation. Claim a glossary
match only when the identifier exactly matches a glossary name. Do not change
case, split identifiers, infer synonyms, or use semantic similarity. Record
<no glossary match> when there is no exact match. Do not change the glossary
unless the user separately asks for that change.
Explain what you currently think the domain problem is before introducing a model. Give the user something concrete to correct.
Separate:
Use domain language. Do not frame the problem as a class, interface, role, or other implementation task. Ask the user to correct what is wrong, incomplete, or framed at the wrong level.
Remain in exploration until the user explicitly asks to recommend, converge, or choose a model. Treat candidate models as probes, not proposals.
Explore existing concepts and standard DDD building blocks before introducing new roles or unusual structures. Start with the current aggregate and its owned state, then test existing entities and value objects, followed by the standard aggregate, entity, value object, and domain service tests. Leave new roles and more exotic structures until the end.
Do not skip a familiar model because it appears likely to fail. Validate it against the fixed constraints so the discussion has concrete proof of why it works or does not work. If it fails, show it explicitly as a ruled-out baseline, not as one of the admitted valid options.
For a useful candidate:
Renaming the same design does not create a different option. Explore genuinely different domain interpretations or ownership of behaviour.
Use one important modelling question per turn by default. Each turn should advance the shared understanding by surfacing one tension, bringing relevant evidence, exploring a different angle, and asking one focused question. Do not front load a questionnaire or race several decisions ahead of the user.
When exploring a new role, begin by presenting multiple genuinely different configurations. Do not lead with a preferred configuration or define precise implementation details before the structural alternatives are visible.
Start each option with this format:
Option: <configuration name>RISKS FOR ABUSE section that answers whether the proposed role or
configuration could become an escape hatch, how it could be abused, and
which enforceable constraints limit that abuse. State plainly when the risk
cannot be constrained safely.Every diagram must:
new role candidate; if a proposed concept
does not yet have a defined role, the option is not ready to present;For example:
***** Option: aggregate with owned value objects *****
One aggregate owns workflow state, event application, and its immutable registry.
KEY IDEA: Start from the state and invariants before considering services or new roles.
CONCRETE ROLES:
aggregate: owns mutable workflow state and enforces workflow invariants.
value-object: represents the immutable registry and each immutable workflow state definition.
RISKS FOR ABUSE: The aggregate could become a large file or construct its own
collaborators. Keep each value object in the file for its domain concept and
inject the registry through the aggregate constructor.
FIGURE: Aggregate ownership FIGURE: Application construction
┌─────────────────────────┐ ┌─────────────────────────┐
│ MaintainerWorkflow │ │ ConfigureWorkflow │
│ role: aggregate │ │ role: command-use-case │
└─────────────────────────┘ └─────────────────────────┘
│ │
│ owns │ parses and injects
▼ ▼
┌─────────────────────────┐ ┌─────────────────────────┐
│ WorkflowRegistry │ │ WorkflowRegistry │
│ role: value-object │ │ role: value-object │
└─────────────────────────┘ └─────────────────────────┘
│
│ contains
▼
┌─────────────────────────┐
│ ImplementingState │
│ role: value-object │
└─────────────────────────┘Only discuss detailed trade-offs or identify a leading option after the user can compare the structural configurations.
Explore the candidate space until further candidates only repeat an existing structure, break an agreed constraint, or add no meaningful trade-off. Reject invalid candidates privately. Let the number of visible options follow from the strong, distinct candidates that remain; never target an arbitrary or user-mentioned count. Never pad the visible options with renamed versions of the same structure or with designs that break an agreed constraint.
Validate every candidate concept name against the represented data and
behaviour before presenting it. Treat existing code names as evidence, not as
proof of a domain concept. Inspect what the code accepts, what it returns, what
state it materialises, and which invariants it protects. Distinguish the source
evidence, the process that interprets it, and the domain result. Do not propose
a value object named after an algorithm or intermediate mechanism when the code
does not materialise that value. For example, a function called
buildCallGraph that returns detected architectural links does not establish a
CallGraph value object unless it actually produces and protects a graph of
code calls.
Apply the basic domain model tests first:
An identifier passed separately from the objects linked to that identifier is a basic entity signal. Before preserving a map, tuple, or parallel parameters with that shape, test whether they are a flattened entity that should own the identity and the relationship.
Before presenting an option, verify all of these points:
never check or an exhaustive satisfies Record<Union, ...>;If any check fails, do not show the option. If a required fact is unknown, stop and inspect the code or ask the user instead of filling the gap with a sketch.
Treat an unexplained primitive or broad verb as missing domain language. Ask
what a number, boolean, or string represents and what an operation such as
compare means before approving the API. For example, compare(other): number
hides both the ordering relationship and the meaning of -1, 0, and 1.
Prefer an API such as
positionRelativeTo(other): 'before' | 'same' | 'after', then translate that
meaning into the numeric Array.sort protocol only at the technical boundary.
Apply this test to every domain model decision. A type can be technically correct while still concealing the concept that a reader needs to understand, use, and change the model safely.
Challenge every candidate with these questions:
When no domain expert is present, identify the assumptions that require domain expert confirmation. Plausible code vocabulary is not confirmed domain language.
Test candidates against credible changes, such as a different product, market, interface, rule, workflow, integration, or source of information.
For each relevant change, ask what stays stable, what changes, where the change lands, and whether the domain language and invariants remain honest. Prefer a model when likely changes stay local and its concepts retain their meaning. Challenge a model when change spreads across unrelated responsibilities, weakens invariants, stretches an aggregate, or requires exceptions.
Future scenarios test a model. They do not justify speculative abstractions. State why a scenario is credible before allowing it to influence the model.
Rivière roles express responsibility and protect architectural quality. They do not exist to provide somewhere convenient for code to go.
When role classification matters, read .riviere/role-selection-guide.md and
the complete definitions for every role under consideration. Inspect the end
to end flow before assigning a role. Responsibility determines the role; the
role does not create the responsibility.
When code does not fit, re-examine the model. Do not invent generic helpers,
managers, orchestrators, services, roles, folders, exemptions, or other escape
hatches. A new role needs a distinct, durable architectural responsibility.
Changes inside .riviere and new aggregate classifications require explicit
user approval.
Prefer the strongest constraints supported by current evidence when introducing a role. Loosen them only when a concrete valid case shows that a constraint rejects legitimate code.
When the user asks to converge, identify the model that currently appears most suitable and explain why it leads. Base the recommendation on domain expertise, repository evidence, credible future changes, and role constraints.
Keep the leading model open to challenge. State its important weaknesses, assumptions, and unresolved questions. Compare it with the strongest remaining alternative when that distinction is useful. New evidence can return the discussion to exploration.
Only the user decides when convergence is complete. Do not infer completion from confidence, silence, or implementation convenience. Agreement on a model does not authorise planning or implementation.
After the user declares convergence complete, draft a concise model summary for their approval. It should cover:
The summary is not an implementation plan. After the user approves it, store it
at
docs/architecture/ddd/explorations/<stable-descriptive-slug>/model-summary.md.
Use lowercase words separated by hyphens, with no date prefix. Update the same
summary when the approved model evolves.
© NTCoding, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/ddd of NTCoding/living-architecture.
Open the folder on GitHubat commit a24bc9f
Ddd next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ddd this skillNTCoding/living-architecture | 139 | — | ~4k | Automated safety check: Pass | Apache-2.0 | |
| Domain Modelingfossasia/eventyay-interpretation | 1.6k | 29 repos | ~821 | Automated safety check: Pass | Apache-2.0 | |
| Architecture Governancezai-org/ZCode | 7.5k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Evolutionary Modular Architecturetech-leads-club/agent-skills | 7k | — | ~3.7k | Automated safety check: Pass | CC-BY-4.0 | |
| Domain Modelingbrim-borium/spotify_sdk | 166 | 5 repos | ~806 | Automated safety check: Pass | Apache-2.0 | |
| Domain Modeling and Glossarywindmill-labs/windmill | 18k | — | ~622 | Automated safety check: Pass | Custom licence |
fossasia/eventyay-interpretation
Build and sharpen a project's domain model. An agent skill from fossasia/eventyay-interpretation.
zai-org/ZCode
Apply the repository's architecture policy to code changes by generating a bounded context package, checking module and layer boundaries, and reporting baseline-aware violations.
tech-leads-club/agent-skills
Guides design of modular-monolith platforms with DDD, flat-by-aggregate modules, anti-corruption layers, outbox events and resilience, plus an architecture document with SVG diagrams.
brim-borium/spotify_sdk
Build and sharpen a project's domain model. An agent skill from brim-borium/spotify_sdk.
windmill-labs/windmill
Actively challenges vague or conflicting terminology as you design, and keeps a living domain glossary file up to date in real time.
swamp-club/swamp
Domain Driven Design guidance for TypeScript/Deno codebases.
NTCoding/living-architecture
Start implementation for a GitHub issue. An agent skill from NTCoding/living-architecture.
NTCoding/living-architecture
List unresolved review threads for the pull request recorded in workflow state.
NTCoding/living-architecture
Execute a low-level dev-workflow-v2 state operation. An agent skill from NTCoding/living-architecture.
Categories
Explore and evaluate domain models collaboratively before implementation. Ddd is an agent skill from NTCoding/living-architecture. Explore and evaluate domain models collaboratively before implementation.
Ddd fits situations like: domain concepts; subdomain boundaries; rivière role questions where domain expertise and repository evidence must shape the model.
Run `npx skills add NTCoding/living-architecture --skill ddd -a claude-code`. Or copy the skill folder (.agents/skills/ddd in NTCoding/living-architecture) into .claude/skills/ddd in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NTCoding/living-architecture --skill ddd -a codex`. Or copy the skill folder (.agents/skills/ddd in NTCoding/living-architecture) into .agents/skills/ddd in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NTCoding/living-architecture --skill ddd -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ddd, .gemini/skills/ddd, .github/skills/ddd and .opencode/skills/ddd in your project.
SKILL.md names no scripts, command-line tools or credentials: Ddd is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Ddd is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Ddd: Domain Modeling (fossasia/eventyay-interpretation, 1.6k stars), Architecture Governance (zai-org/ZCode, 7.5k stars), Evolutionary Modular Architecture (tech-leads-club/agent-skills, 7k stars) and Domain Modeling (brim-borium/spotify_sdk, 166 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NTCoding (a GitHub user) maintains it in NTCoding/living-architecture, which has 139 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on October 5, 2026.
Source: NTCoding/living-architecture on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.