adr

Extract architectural decisions from conversations into MADR-compliant documents.

Updated Jul 18, 2026
One-click install
npx skills add https://github.com/arthrod/conejo-skills --skill adr-arthrod
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: adr
Source: https://github.com/arthrod/conejo-skills/tree/main/skills/adr
Command: npx skills add https://github.com/arthrod/conejo-skills --skill adr-arthrod

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill solves the problem of fragmented or lost architectural knowledge by providing a structured, repeatable pipeline to extract, format, and verify Architectural Decision Records (ADRs).

Core Features & Use Cases

  • Decision Extraction: Automatically identifies architectural choices, trade-offs, and rationale from raw conversation transcripts or PR discussions.
  • MADR Compliance: Generates formal ADRs using the Markdown Architectural Decision Record (MADR) standard, ensuring consistency across the team.
  • Quality Verification: Enforces the E.C.A.D.R. (Explicit, Comprehensive, Actionable, Documented, Reviewable) Definition of Done to ensure every decision is high-quality and stakeholder-ready.

Quick Start

Use the adr skill to extract architectural decisions from the current conversation and format them into a new MADR document.

Frequently Asked Questions about adr

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

FAQPage Schema
How do I extract architectural decisions from conversation transcripts?

To extract architectural decisions from conversation transcripts, this Skill mines raw conversational context like PR discussions and automatically identifies architectural choices, trade-offs, and rationale for formatting.

What is MADR compliance for architectural decision records?

MADR compliance for architectural decision records uses the Markdown Architectural Decision Record standard to format extracted technical choices. This ensures consistent, structured documentation of trade-offs and rationale across the engineering team.

How do I verify the quality of architectural decision records?

You verify the quality of architectural decision records by enforcing the E.C.A.D.R. Definition of Done. This ensures every generated record is Explicit, Comprehensive, Actionable, Documented, and Reviewable for stakeholders.

Do I need Python to generate MADR documents from PR discussions?

Yes, you need Python installed to generate MADR documents from PR discussions. Python is required specifically for sequence management during the extraction and formatting pipeline of the architectural decision records.

What is the best way to standardize architectural documentation across engineering teams?

The best way to standardize architectural documentation across engineering teams is using a structured pipeline that converts raw discussions into formal MADR-compliant records. This prevents fragmented knowledge and enforces a repeatable formatting standard.

Are there limitations when mining architectural decisions from conversational context?

A limitation when mining architectural decisions from conversational context is the strict dependency on Python for sequence management. Additionally, all generated records must pass the E.C.A.D.R. verification criteria to be considered complete.