aidd-methodology

Document architecture, contracts, and constraints before coding in AI-driven projects.

35|7|Updated Jun 12, 2025
One-click install
npx skills add https://github.com/Bbar0n234/learnflow-ai --skill aidd-methodology
Or copy as Structured Prompt for Agent▼
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Skill: aidd-methodology
Source: https://github.com/Bbar0n234/learnflow-ai/tree/main/.claude/skills/aidd-methodology
Command: npx skills add https://github.com/Bbar0n234/learnflow-ai --skill aidd-methodology

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The AIDD methodology helps teams write architecture, contract, and project documentation for AI-driven projects with LLM agents, ensuring context-first planning before coding.

Core Features & Use Cases

  • Context-first documentation strategy that anchors architecture, contracts, and limitations.
  • ADRs, design briefs, and implementation planning to guide agent-driven development.
  • Clear delineation of roles: developers (architects) versus LLM agents, with architect review before implementation.
  • Use Cases: planning complex multi-service projects, cross-team documentation updates, and iteration planning for agent-based workflows.

Quick Start

Coordinate architecture, contracts and context-first requirements before starting code, then empower an LLM agent to implement with oversight from the architect.

Frequently Asked Questions about aidd-methodology

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

FAQPage Schema
How do I document architecture and contracts for AI-driven development before coding?▼

AI-driven development requires context-first documentation that anchors architecture, contracts, and limitations upfront. You create ADRs and design briefs to establish a single source of truth, ensuring LLM agents receive unambiguous guidance before implementation begins.

What is the best way to plan multi-service projects that involve LLM agents?▼

Planning complex multi-service projects with LLM agents requires upfront architectural guidance and design briefs to coordinate cross-team development. Establishing review checkpoints before implementation ensures the agent operates within defined constraints and architectural boundaries.

How do ADRs and design briefs help coordinate development with LLM agents?▼

ADRs and design briefs provide the context-first requirements and architectural guidance that LLM agents need for implementation. They create a single source of truth that eliminates ambiguity, allowing developers to act as architects who review agent output against documented constraints.

Does context-first documentation work for cross-team iteration planning in agent-based workflows?▼

Context-first documentation supports cross-team iteration planning by maintaining updated architecture and contract documents for agent-based workflows. It ensures all teams reference a single source of truth during documentation updates and subsequent implementation cycles.

Do I need to define developer and LLM agent roles separately for AI-driven development?▼

AI-driven development requires clear delineation of roles where developers act as architects providing oversight, while LLM agents handle implementation. The architect must review context-first documentation and establish checkpoints before empowering the agent to write code.

When should I not use upfront architectural documentation for LLM projects?▼

Upfront architectural documentation for LLM projects is less suited for simple, single-service scripts or rapid prototypes where architectural constraints and cross-team coordination are unnecessary. It targets complex projects requiring strict context-first planning and contract definition.