ddd-refiner

ddd-refiner is a skill for Claude Code from techygarg/lattice. It costs 86 tokens per session (2,689 once invoked), scanned A, original, MIT.

A guided conversation that defines a project's Domain-Driven Design rules—guidelines for modeling business concepts in code—and saves them in a document.

In plain words
What is it for?
Use it to establish rules for aggregates, entities, value objects, domain services, and other tactical domain patterns.
Why use it?
It gives future domain modeling work consistent rules instead of leaving important design choices to guesswork.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the lattice plugin — 33 skills, 2 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/techygarg/lattice/ddd-refiner
Any agent
npx skills add techygarg/lattice --skill ddd-refiner
Clone the repo
git clone --depth 1 https://github.com/techygarg/lattice

Made for: Claude Code.

Or install lattice, the plugin that ships this one along with the rest of its 33 skills, 2 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ddd-refiner

README.md
[![agentmods](https://agentmods.dev/badge/skills/techygarg/lattice/ddd-refiner.svg)](https://agentmods.dev/skills/techygarg/lattice/ddd-refiner)
Your own site
<a href="https://agentmods.dev/skills/techygarg/lattice/ddd-refiner"><img src="https://agentmods.dev/badge/skills/techygarg/lattice/ddd-refiner.svg" alt="Measured on agentmods" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,689 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00086 $0.02689
Opus 5 $0.00043 $0.01345
Sonnet 5 $0.00017 $0.00538
Haiku 4.5 $0.00009 $0.00269

Measured 6d ago against content hash cd5d9dd8f5c9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

ddd-refiner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ddd-refiner/SKILL.md · 218 lines

How it starts

The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DDD Refiner

What This Produces

  • Output: .lattice/standards/ddd-principles.md (or custom path from .lattice/config.yamlpaths.ddd_principles)
  • Two modes:
    • Overlay (mode: overlay): A slim document containing only sections that differ from the defaults. The domain-driven-design atom reads its embedded defaults first, then applies this document's sections on top. This is the expected common case.
    • Override (mode: override): A comprehensive standalone document that fully replaces the atom's embedded defaults. For teams with fundamentally different domain modeling principles.
  • Default mode: Overlay -- produces only what the user wants to change
  • Config key: paths.ddd_principles in .lattice/config.yaml
  • Template: Read ./assets/template.md for the full document structure, default content, and interview guidance comments

Scope Clarification

This skill defines the rules of domain crafting, not the domain model itself. The domain model evolves through features; this document defines the guardrails. It covers DDD tactical patterns only -- not strategic DDD (no context mapping, no microservice topology, no bounded context integration).

Before You Begin

Check for existing documents

Before starting the interview, check whether a custom document already exists:

  1. Read .lattice/config.yaml -- does paths.ddd_principles point to a file?
  2. If yes, read that file. Ask the user:
    • "You already have a custom DDD principles document. Would you like to revise it (update specific sections), start fresh (new interview), or add to it (add new sections)?"
    • Revise: Load the existing document, walk through only the sections the user wants to change, and update in place.
    • Start fresh: Proceed with the full interview flow below.
    • Add to it: Skip to the "New Sections" part of the interview.
  3. If no config or no existing document, proceed with the full interview flow.

Scan the repository

Read the full file on GitHub · 218 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 218 lines · 86 tokens per session scan A cd5d9dd8f5c9

Subscribe to this mod's changes

ddd-refiner is a skill published in the GitHub repository techygarg/lattice (185 stars, last pushed yesterday), licensed MIT. It adds 86 tokens to every session and 2,689 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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