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ai-dlc-fundamentals

Explain AI-DLC fundamentals for hat-driven, iteration-based workflows with context resets.

13|1|Updated Apr 6, 2026
Search Tags:#iteration#backpressure#state-persistence#ai-dlc#context-reset#hat-based
One-click install
npx skills add https://github.com/TheBushidoCollective/ai-dlc --skill ai-dlc-fundamentals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-dlc-fundamentals
Source: https://github.com/TheBushidoCollective/ai-dlc/tree/main/skills/ai-dlc-fundamentals
Command: npx skills add https://github.com/TheBushidoCollective/ai-dlc --skill ai-dlc-fundamentals

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI-DLC fundamentals help teams understand and apply the core principles of AI-DLC to structure iterative, hat-based work with clear state persistence and context resets.

Core Features & Use Cases

  • Backpressure-driven iteration guides progress by blocking until criteria are met.
  • Files-as-memory and explicit state files (intent.md, completion-criteria.md, scratchpad.md, blockers.md, iteration.json) preserve learnings across sessions.
  • Hat-based workflows organize work into Elaborator, Planner, Builder, and Reviewer with defined transitions.
  • Use cases include onboarding a new AI-assisted project, enforcing repeatable cycles, and teaching teams how to operate under different modes (HITL, OHOTL, AHOTL).

Quick Start

Outline a simple 3-iteration plan using context resets and persistent state.

Frequently Asked Questions about ai-dlc-fundamentals

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

FAQPage Schema
What is backpressure-driven iteration and how does it work in AI workflows?▼

Backpressure-driven iteration blocks workflow progress until specific completion criteria are met, ensuring AI-assisted product development cycles remain repeatable and explicitly gated across multiple sessions.

How do I preserve AI context and state across multiple iterations?▼

Use files-as-memory by maintaining explicit state files like intent.md, completion-criteria.md, scratchpad.md, blockers.md, and iteration.json to persist context resets and learnings across AI sessions.

How do I structure hat-based workflows for AI-assisted development?▼

Organize AI development tasks into Elaborator, Planner, Builder, and Reviewer hats with defined transitions to manage iteration-based workflows and operate under HITL, OHOTL, or AHOTL modes.

When do I need context resets in AI development workflows?▼

Context resets are needed when AI-assisted projects require external state persistence across multiple iterations, preventing context loss and ensuring progress is guided by backpressure and completion criteria.

How do I start a 3-iteration AI-assisted project plan?▼

Outline a simple 3-iteration plan by applying context resets between cycles and using persistent state files to capture intent, completion criteria, and scratchpad data for continuous progress.

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