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Architecture Decision Record

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mohitagw15856
architecture-decision-record

Create an Architecture Decision Record (ADR) for any technical decision. Use when asked to document a technical decision, write an ADR, record an architecture choice, or capture why a technology or approach was selected. Produces a structured ADR with context, decision, consequences, and tradeoffs.

Overview

Publishermohitagw15856
Repositorypm-claude-skills
Skill namearchitecture-decision-record
Stars
1.4K
Forks
240
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 mohitagw15856 on GitHub. Read the source before you install it.

Installation

Install the Architecture 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.
  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/mohitagw15856/pm-claude-skills.git /tmp/pm-claude-skills
mkdir -p .claude/skills
cp -r /tmp/pm-claude-skills/exports/openclaw/architecture-decision-record .claude/skills/architecture-decision-record
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architecture 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 Architecture 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 Architecture 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.

Architecture Decision Record (ADR) Skill

This skill produces a complete Architecture Decision Record (ADR) following the Nygard format — the most widely adopted standard. ADRs document the reasoning behind significant technical decisions so future team members understand not just what was decided, but why.

Required Inputs

Ask the user for these if not provided:

  • ADR number (sequential number in your ADR registry — e.g. 012; or "next available" if unknown)
  • Decision title (brief, e.g. "Use PostgreSQL as primary datastore")
  • Context (what situation led to this decision needing to be made?)
  • Options considered (at least 2; if only 1 is given, prompt for alternatives that were considered or ruled out)
  • Decision made (which option was chosen)
  • Reason for choice
  • Status (Proposed / Accepted / Deprecated / Superseded)
  • Author and date
  • Team context (optional — team size, relevant experience, org constraints; helps calibrate formality and depth of the Context section)

Output Format


ADR-[NNN]: [Decision Title]

Date: [YYYY-MM-DD] Status: [Proposed / Accepted / Deprecated / Superseded by ADR-NNN] Author(s): [Name(s)] Deciders: [Who had final say — individual or team]


Context

[3–6 sentences. Describe the situation, constraints, and forces at play that made this decision necessary. Include: the problem being solved, relevant system state, team constraints, timeline pressures, or non-negotiable requirements. Write as if explaining to someone joining the team 18 months from now who has no prior context.]

Key constraints:

  • [Constraint 1: e.g. "Must be deployable on-premise for enterprise customers"]
  • [Constraint 2: e.g. "Team has no prior Go experience"]
  • [Add as many as are relevant]

Options Considered

For each option, produce:

Option [N]: [Name]

Description: [What this option is — 1–3 sentences]

Pros:

  • [Pro 1]
  • [Pro 2]

Cons:

  • [Con 1]
  • [Con 2]

Why this was ruled out (if not chosen): [Honest reason]


Decision

We will [chosen option].

[2–4 sentences explaining the decision in plain language. This should be readable in isolation — someone should understand the decision from this paragraph alone without reading the full document.]


Consequences

Positive Consequences

  • [What this decision enables or improves]
  • [What risk it mitigates]

Negative Consequences / Accepted Tradeoffs

  • [What we're giving up or taking on as a result of this decision]
  • [Technical debt or limitations introduced]
  • [What must now be true for this decision to remain valid]

Risks

  • [What could cause this decision to be wrong in hindsight]
  • [What would trigger us to revisit this decision]

Implementation Notes

[Include if the decision has non-obvious implementation gotchas, or if there are related tickets/RFCs implementers will need. Skip only if the decision is purely tooling selection with no implementation ambiguity.]


Review Date

[Include unless the decision is permanent or self-evidently final. State a specific trigger condition — e.g. "Review if team grows beyond 20 engineers or traffic exceeds 10M requests/day" — not just "should be reviewed periodically".]


Scoring Rubric (0–40)

Score any output of this skill before handing it over; 32+ is ship-quality.

Dimension0510
Context reconstructionAssumes the reader already knows the problem and the pressuresProblem stated, but the forces (team constraints, scale, deadline, forcing event) are missingA reader two years later could guess the decision from the Context section alone
Options honestyOnly the chosen option, or rejected options written as strawmen≥2 options, but rejection reasons are circular ("didn't meet requirements")Every rejected option has genuine strengths and a specific losing constraint tied to a context pressure
Consequence balanceAll consequences are positiveToken negatives with no operational specificsNegatives name real debts, new operational burdens, and what must stay true for the decision to hold
Revisit triggersNo risks or review conditions stated"Review periodically" — no measurable conditionSpecific, measurable trigger conditions that would invalidate the decision, with an owner

Quality Checks

  • Context explains the why — not just the what
  • At least 2 options are documented (including the rejected ones)
  • Rejected options include honest reasons for rejection
  • Consequences include negative consequences — no decision is consequence-free
  • Decision is stated in plain language in the Decision section
  • Risks section identifies what would invalidate this decision
  • Context section states the problem explicitly in its first 1–2 sentences (does not assume the reader knows what problem the team was solving)
  • Each rejected option's "Why ruled out" explanation names a specific constraint or trade-off (not a circular statement like "didn't meet our requirements")

Anti-Patterns

  • Do not write an ADR after the decision has already been fully implemented and the team has moved on — ADRs written retrospectively often omit the real reasons and alternatives
  • Do not list only the chosen option — rejected options with honest reasons are the most valuable part of an ADR for future readers
  • Do not write consequences that are all positive — every architectural decision involves trade-offs; an ADR with no negative consequences was not scrutinised honestly
  • Do not leave the status as "Proposed" indefinitely — an ADR that no one has approved is not guiding anyone's decisions
  • Do not write context that assumes the reader already knows what problem was being solved — the context section exists precisely for readers who lack that background

Usage Examples

  • "Write an ADR for using [technology]"
  • "Document our decision to [architectural choice]"
  • "Create an architecture decision record for [topic]"
  • "Help me write up why we chose [option] over [alternative]"

Frequently asked questions

What does the Architecture Decision Record AI skill do?

Create an Architecture Decision Record (ADR) for any technical decision. Use when asked to document a technical decision, write an ADR, record an architecture choice, or capture why a technology or approach was selected. Produces a structured ADR with context, decision, consequences, and tradeoffs.

Why use Architecture Decision Record on TypingMind?

Because you install it once and use it with any model. Architecture 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 Architecture Decision Record in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mohitagw15856/pm-claude-skills/tree/main/exports/openclaw/architecture-decision-record. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Architecture 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 Architecture Decision Record?

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

Is the Architecture Decision Record AI skill free?

Yes. It is published on GitHub by mohitagw15856 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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