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Create Architectural Decision Record

OrganizationPopular
github
create-architectural-decision-record

Create an Architectural Decision Record (ADR) document for AI-optimized decision documentation.

Overview

Publishergithub
Repositoryawesome-copilot
Skill namecreate-architectural-decision-record
Stars
39.6K
Forks
5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by github on GitHub. Read the source before you install it.

Installation

Install the Create Architectural Decision Record AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/github/awesome-copilot/tree/main/skills/create-architectural-decision-record
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/github/awesome-copilot.git /tmp/awesome-copilot
mkdir -p .claude/skills
cp -r /tmp/awesome-copilot/skills/create-architectural-decision-record .claude/skills/create-architectural-decision-record
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Architectural Decision Record in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Create Architectural Decision Record on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Create Architectural Decision Record is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Create Architectural Decision Record

Create an ADR document for ${input:DecisionTitle} using structured formatting optimized for AI consumption and human readability.

Inputs

  • Context: ${input:Context}
  • Decision: ${input:Decision}
  • Alternatives: ${input:Alternatives}
  • Stakeholders: ${input:Stakeholders}

Input Validation

If any of the required inputs are not provided or cannot be determined from the conversation history, ask the user to provide the missing information before proceeding with ADR generation.

Requirements

  • Use precise, unambiguous language
  • Follow standardized ADR format with front matter
  • Include both positive and negative consequences
  • Document alternatives with rejection rationale
  • Structure for machine parsing and human reference
  • Use coded bullet points (3-4 letter codes + 3-digit numbers) for multi-item sections

The ADR must be saved in the /docs/adr/ directory using the naming convention: adr-NNNN-[title-slug].md, where NNNN is the next sequential 4-digit number (e.g., adr-0001-database-selection.md).

Required Documentation Structure

The documentation file must follow the template below, ensuring that all sections are filled out appropriately. The front matter for the markdown should be structured correctly as per the example following:

md
---
title: "ADR-NNNN: [Decision Title]"
status: "Proposed"
date: "YYYY-MM-DD"
authors: "[Stakeholder Names/Roles]"
tags: ["architecture", "decision"]
supersedes: ""
superseded_by: ""
---

# ADR-NNNN: [Decision Title]

## Status

**Proposed** | Accepted | Rejected | Superseded | Deprecated

## Context

[Problem statement, technical constraints, business requirements, and environmental factors requiring this decision.]

## Decision

[Chosen solution with clear rationale for selection.]

## Consequences

### Positive

- **POS-001**: [Beneficial outcomes and advantages]
- **POS-002**: [Performance, maintainability, scalability improvements]
- **POS-003**: [Alignment with architectural principles]

### Negative

- **NEG-001**: [Trade-offs, limitations, drawbacks]
- **NEG-002**: [Technical debt or complexity introduced]
- **NEG-003**: [Risks and future challenges]

## Alternatives Considered

### [Alternative 1 Name]

- **ALT-001**: **Description**: [Brief technical description]
- **ALT-002**: **Rejection Reason**: [Why this option was not selected]

### [Alternative 2 Name]

- **ALT-003**: **Description**: [Brief technical description]
- **ALT-004**: **Rejection Reason**: [Why this option was not selected]

## Implementation Notes

- **IMP-001**: [Key implementation considerations]
- **IMP-002**: [Migration or rollout strategy if applicable]
- **IMP-003**: [Monitoring and success criteria]

## References

- **REF-001**: [Related ADRs]
- **REF-002**: [External documentation]
- **REF-003**: [Standards or frameworks referenced]

Frequently asked questions

What does the Create Architectural Decision Record AI skill do?

Create an Architectural Decision Record (ADR) document for AI-optimized decision documentation.

Why use Create Architectural Decision Record on TypingMind?

Because you install it once and use it with any model. Create Architectural Decision Record is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Create Architectural Decision Record in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/github/awesome-copilot/tree/main/skills/create-architectural-decision-record. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Create Architectural Decision Record?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Create Architectural Decision Record?

As many as you like. As long as a model supports skills, you can use Create Architectural Decision Record with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Create Architectural Decision Record AI skill free?

Yes. It is published on GitHub by github under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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