소스 정보
- 저장소
- ForceInjection/domain-driven-design-skills
- 최근 소스 활동
- 2026년 5월 8일 03:07
- 감지된 SKILL.md 언어
- 영어
- 스타
- 25
- 포크
- 7
설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SOC 직업 분류 기준
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/ForceInjection/domain-driven-design-skills --skill template-generator명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
Conduct deep academic research for philosophy, neuroscience, cognitive science, and theoretical computer science (computability, complexity, AI theory, logic). Use when user asks to: research academic topics, find scholarly papers, conduct literature reviews, analyze citations, synthesize research findings, explore philosophical arguments, investigate consciousness/cognition, study computability/decidability/Turing machines, or analyze academic debates. Triggers on: 'research papers', 'literature review', 'academic sources', 'scholarly articles', 'philosophy of mind', 'computability theory', 'neuroscience studies', 'find papers on', 'what does the research say'.
Create clear action plans with steps, success criteria, and risk awareness. Use before implementing features, making changes, starting projects, or anytime you need a roadmap to success. Triggers on "plan this", "how should we approach", "what's the strategy", "steps to complete", or when facing complex multi-step work.
Add keyboard navigation to a feature using CommandRegistryService. Use when implementing keyboard shortcuts, vim-style navigation, or hotkeys for a page or component.
| name | template-generator |
| description | Generate workflow templates with coherent node graphs and integration tests |
Generate workflow templates: discover nodes, design graphs, wire edges correctly, create tests.
sourceOutput → targetInput (port names must match exactly)Directory structure:
apps/api/src/nodes/
├── input/ # TextInputNode, ImageInputNode, NumberInputNode...
├── preview/ # TextPreviewNode, ImagePreviewNode, NumberPreviewNode...
├── text/ # Summarization, translation, sentiment
├── image/ # Generation, manipulation
├── audio/ # Processing, transcription
├── anthropic/ # Claude models
├── openai/ # GPT models
├── logic/ # ConditionalForkNode, ConditionalJoinNode
└── ... # json/, math/, fetch/, browser/, etc.
Search commands:
Grep pattern="translate" path="apps/api/src/nodes" glob="*.ts"
Glob pattern="apps/api/src/nodes/text/*.ts"
Read node interface - look for nodeType.inputs and nodeType.outputs:
Read file_path="apps/api/src/nodes/text/bart-large-cnn-node.ts"
Key fields: inputs[].name → targetInput, outputs[].name → sourceOutput
The type field defines how the workflow is triggered:
| Type | Description | Entry Node |
|---|---|---|
manual | User-initiated via UI/API | Input nodes (TextInputNode, etc.) |
email_message | Triggered by incoming email | ReceiveEmailNode |
http_request | Triggered by HTTP request (sync) | HttpRequestNode |
http_webhook | Triggered by webhook (async) | HttpRequestNode |
scheduled | Triggered on schedule (cron) | ReceiveScheduledTriggerNode |
queue_message | Triggered by queue message | ReceiveQueueMessageNode |
Finding trigger-compatible nodes: Nodes declare which triggers they work with via the compatibility field in their nodeType. Search for compatible nodes:
Grep pattern="compatibility:.*email_message" path="apps/api/src/nodes" glob="*.ts"
Grep pattern="compatibility:.*http_request" path="apps/api/src/nodes" glob="*.ts"
File: apps/api/src/templates/{template-id}.ts
import type { WorkflowTemplate } from "@dafthunk/types";
import { TextInputNode } from "../nodes/input/text-input-node";
import { BartLargeCnnNode } from "../nodes/text/bart-large-cnn-node";
import { TextPreviewNode } from "../nodes/preview/text-preview-node";
export const myTemplate: WorkflowTemplate = {
id: "my-template",
name: "My Template",
description: "What it does",
icon: "file-text",
type: "manual",
tags: ["text", "ai"],
nodes: [
TextInputNode.create({
id: "text-to-process",
name: "Text to Process",
position: { x: 100, y: 100 },
inputs: { value: "Sample text...", rows: 4 },
}),
BartLargeCnnNode.create({
: ,
: ,
: { : , : },
}),
.({
: ,
: ,
: { : , : },
}),
],
: [
{ : , : , : , : },
{ : , : , : , : },
],
};
Positioning: Inputs at x:100, processing at x:500, outputs at x:900. Stack vertically with 200px spacing.
Naming: IDs are kebab-case (text-to-translate). Names are short Title Case, omit "Preview" for outputs.
ConditionalForkNode - splits flow based on boolean:
condition (boolean), value (any)true, false (only ONE has value)ConditionalJoinNode - merges exclusive branches:
a, b (exactly ONE must have value)result[BooleanInput] ──condition──► [Fork] ──true──► [ProcessorA] ──►┐
[TextInput] ────value──────► ──false─► [ProcessorB] ──►├─► [Join] ──► [Preview]
Register in apps/api/src/templates/index.ts:
import { myTemplate } from "./my-template";
export const workflowTemplates = [..., myTemplate];
Test file {template-id}.integration.ts:
describe("My Template", () => {
it("should have valid structure", () => {
expect(myTemplate.nodes).toHaveLength(3);
expect(myTemplate.edges).toHaveLength(2);
const nodeIds = new Set(myTemplate.nodes.map(n => n.id));
for (const edge of myTemplate.edges) {
expect(nodeIds.has(edge.source)).toBe(true);
expect(nodeIds.has(edge.target)).toBe(true);
}
});
});
Run: pnpm typecheck && pnpm --filter '@dafthunk/api' test {template-id}
| Output | Compatible Inputs |
|---|---|
| string | string, any |
| number | number, any |
| boolean | boolean, any |
| image | image, blob, any |
| audio | audio, blob, any |
| json | json, any |