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Adr Writer

Community
Mathews-Tom
adr-writer

Generates Architecture Decision Records capturing context, rationale, alternatives, and consequences in numbered status-tracked format. Triggers on: "write an ADR", "document this decision", "architecture decision record", "decision record", "design decision", "ADR for".

Overview

PublisherMathews-Tom
Repositoryarmory
Skill nameadr-writer
Stars
318
Forks
47
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Adr Writer 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/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/adr-writer .claude/skills/adr-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Adr Writer 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 Adr Writer 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 Adr Writer 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.

ADR Writer

Captures architecture decisions in a lightweight, structured format that preserves context, rationale, alternatives, and consequences. Produces numbered ADR documents with proper status lifecycle — preventing the "why did we do it this way?" problem when revisiting decisions months later.

Reference Files

FileContentsLoad When
references/adr-template.mdStandard ADR template with field explanations and examplesAlways
references/status-lifecycle.mdStatus transitions, supersession rules, deprecation processADR references existing decisions
references/context-capture.mdTechniques for eliciting and documenting decision contextComplex or multi-stakeholder decision
references/alternatives-analysis.mdFramework for evaluating and documenting rejected alternativesMultiple options being considered

Prerequisites

  • A decisions directory (typically docs/adr/ or docs/decisions/)
  • Understanding of the decision being made (may require clarifying questions)

Workflow

Phase 1: Identify the Decision

  1. What choice was made? — Extract the core architectural decision. If the user describes a problem, help them articulate the decision that resolves it.
  2. Is this decision-worthy? — ADRs are for decisions that:
    • Affect system structure (component boundaries, data flow, API design)
    • Are hard to reverse (technology choice, database schema, protocol)
    • Have non-obvious tradeoffs (multiple viable alternatives)
    • Will be questioned later (the "why" will be forgotten)
  3. What triggered this decision? — New requirement, performance issue, scaling concern, security audit finding, tech debt, team growth.

Phase 2: Capture Context

Document the forces that shaped this decision:

  1. Requirements — What functional or non-functional requirements drive this?
  2. Constraints — What limits the solution space? (budget, timeline, team expertise, existing infrastructure, regulatory requirements)
  3. Current state — What exists today? What is the pain point?
  4. Stakeholders — Who is affected by this decision? Who needs to agree?

Phase 3: Enumerate Alternatives

For each alternative considered:

  1. Name it clearly — "PostgreSQL" not "Option A"
  2. List concrete pros — Specific, measurable benefits
  3. List concrete cons — Specific, measurable drawbacks
  4. State the rejection reason — Why this alternative was not chosen. Be specific: "Does not support our required throughput of 10K ops/sec" not "Too slow."

Phase 4: Document the Decision

State the chosen option and why it was selected given the context and constraints. The decision should follow logically from the context + alternatives analysis.

Phase 5: Project Consequences

Document what this decision makes easier and harder:

  1. Positive consequences — What improves?
  2. Negative consequences — What tradeoffs are accepted? What tech debt is incurred?
  3. Neutral consequences — Side effects that are neither good nor bad.

Phase 6: Assign Metadata

  1. Number — Sequential: ADR-001, ADR-002, etc. Check existing ADRs for the next number.
  2. Status — Initial status is usually "Proposed" or "Accepted"
  3. Date — Date the ADR was written
  4. Author — Who authored this ADR
  5. Supersedes/Superseded-by — Link to related ADRs if this replaces an earlier decision

Output Format

text
# ADR-{NNN}: {Descriptive Title}

**Status:** {Proposed | Accepted | Deprecated | Superseded by ADR-XXX}
**Date:** {YYYY-MM-DD}
**Author:** {name}
**Supersedes:** {ADR-XXX (if applicable)}

## Context

{What situation requires a decision? What constraints exist? What forces are at play?
Write in present tense — describe the situation as it exists at decision time.}

## Decision

{State the decision clearly and concisely. "We will use X for Y because Z."
One to three sentences. The reader should understand the decision without reading
the rest of the document.}

## Alternatives Considered

### {Alternative 1 Name}
- **Pros:** {specific benefits}
- **Cons:** {specific drawbacks}
- **Rejected because:** {concrete, specific reason tied to context}

### {Alternative 2 Name}
- **Pros:** {specific benefits}
- **Cons:** {specific drawbacks}
- **Rejected because:** {concrete, specific reason tied to context}

## Consequences

### Positive
- {Concrete benefit 1}
- {Concrete benefit 2}

### Negative
- {Concrete tradeoff 1 — acknowledged and accepted}
- {Technical debt incurred — with plan to address if applicable}

### Neutral
- {Side effect that is neither positive nor negative}

## References

- {Link to related issue, discussion, document, or prior ADR}

Calibration Rules

  1. Context is king. The Context section is the most important part. A decision without context is just an assertion. Future readers need to understand WHY, not just WHAT.
  2. Specific rejection reasons. "Not suitable" is not a rejection reason. "Does not support transactions across partitions, which we need for order processing" is.
  3. Honest consequences. Every decision has downsides. If the Negative section is empty, the analysis is incomplete. Push the user to articulate tradeoffs.
  4. Present tense for context. Write the Context section in present tense — it captures the world as it was when the decision was made.
  5. One decision per ADR. If multiple decisions are interrelated, write separate ADRs and cross-reference them. Do not bundle unrelated decisions.
  6. Immutable after acceptance. Accepted ADRs are not edited. If a decision changes, write a new ADR that supersedes the old one. This preserves the historical record.

Error Handling

ProblemResolution
User cannot articulate alternativesHelp them brainstorm by asking: "What else could you have done? What did you consider and reject?"
Decision is trivial (no real alternatives)Suggest it doesn't need an ADR. ADRs are for non-obvious decisions with tradeoffs.
Decision already made, no context rememberedReconstruct context from code, PRs, commit history. Note reconstructed context as "best available."
Existing ADR numbering scheme unknownCheck docs/adr/ or docs/decisions/. If no directory exists, suggest creating one and starting at 001.
Decision scope is too broadSplit into multiple focused ADRs. One for the database choice, one for the caching strategy, etc.

When NOT to Write an ADR

Push back if:

  • The decision is easily reversible (library version, code formatting rules) — use a comment or config instead
  • The decision is a standard practice with no alternatives (use HTTPS, validate input) — not decision-worthy
  • The user wants to document implementation details — ADRs are for WHY decisions, not HOW implementations
  • The decision has already been superseded — write the new ADR, not the old one

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Adr Writer AI skill do?

Generates Architecture Decision Records capturing context, rationale, alternatives, and consequences in numbered status-tracked format. Triggers on: "write an ADR", "document this decision", "architecture decision record", "decision record", "design decision", "ADR for".

Why use Adr Writer on TypingMind?

Because you install it once and use it with any model. Adr Writer 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 Adr Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/adr-writer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Adr Writer?

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 Adr Writer?

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

Is the Adr Writer AI skill free?

Yes. It is published on GitHub by Mathews-Tom 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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