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todo-dsl-generator

Convert rough proposal text into a structured TODO DSL plan with step metadata and references.

174|17|Updated Jan 22, 2026
Search Tags:#automation#planning#codebase#project-management#todo-dsl#knowledge-references
One-click install
npx skills add https://github.com/CurryTang/Amadeus --skill todo-dsl-generator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: todo-dsl-generator
Source: https://github.com/CurryTang/Amadeus/tree/main/skills/todo-dsl-generator
Command: npx skills add https://github.com/CurryTang/Amadeus --skill todo-dsl-generator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Converting vague ideas, proposals, or chat transcripts into a precise, executable sequence of steps with clear ownership and traceable references.

Core Features & Use Cases

  • Transforms rough input into a structured TODO DSL with step metadata (step_id, title, kind, objective, assumptions, acceptance, commands, checks, depends_on, references).
  • Includes per-step knowledge and codebase references to ensure low-bias orchestration and easy traceability.
  • Supports exporting flattened todoCandidates for project management views and Kanban-like workflows.

Quick Start

Provide a rough idea or proposal text and your project metadata, and request a structured TODO DSL with per-step references.

Frequently Asked Questions about todo-dsl-generator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert rough project ideas into structured executable steps?▼

Converting rough project ideas into structured executable steps requires parsing proposal text into a TODO DSL plan. This format defines ordered steps with objectives, acceptance criteria, and traceable knowledge references to ensure low-bias orchestration.

What is a TODO DSL and when do I need it for feature planning?▼

A TODO DSL is a structured domain-specific language for product planning that outputs an object with ordered steps. You need it when feature proposals require clear, testable steps with per-step knowledge and codebase references for traceability.

How do I structure task dependencies and acceptance criteria from chat transcripts?▼

Structuring task dependencies from chat transcripts involves generating a plan with step_id, depends_on, and acceptance fields. This validates proposal text into ordered steps with explicit assumptions and commands for testable execution.

Can I export structured plans into a flattened array for Kanban workflows?▼

You can export structured plans into a flattened todoCandidates array for Kanban-like workflows. This output transforms the ordered TODO DSL steps into a project management view for easy tracking and execution.

Does this task automation approach validate steps against existing knowledge assets?▼

This task automation approach validates generated steps against existing knowledge assets and the project codebase. It attaches per-step knowledgeReferences and codebaseReferences to ensure low-bias orchestration and easy traceability.

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