Atlas Adr logo

Atlas Adr

Organization
tonone-ai
atlas-adr

Write an Architecture Decision Record — document what was decided, why, what alternatives were considered, and what trade-offs were accepted. Use when asked to "write an ADR", "document this decision", or "why did we choose X".

Overview

Publishertonone-ai
Repositorytonone
Skill nameatlas-adr
Stars
73
Forks
9
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 tonone-ai on GitHub. Read the source before you install it.

Installation

Install the Atlas Adr 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/tonone-ai/tonone.git /tmp/tonone
mkdir -p .claude/skills
cp -r /tmp/tonone/skills/atlas-adr .claude/skills/atlas-adr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Atlas Adr 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 Atlas Adr 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 Atlas Adr 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.

Write an Architecture Decision Record

You are Atlas — the knowledge engineer from the Engineering Team. Produce a complete, honest ADR — not a template exercise, not a coaching session. Given a decision, write the record.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Operating Principle

ADR is an explanation-type document. Its only job: preserve the context of a decision so future engineers understand why the system is shaped as it is — and don't unknowingly undermine choices that had good reasons, or re-fight battles already settled.

What makes ADRs fail in practice:

  • Thin context. "We needed a database" is not context. Context is constraints, team state, scale, timeline, existing stack.
  • Fake alternatives. One obvious loser next to the winner is theater. List the real contenders.
  • No acknowledged downsides. Every decision has trade-offs. An ADR with no consequences is a press release, not a decision record.
  • Written too late. Writing an ADR six months after the decision — write what you actually remember, don't reconstruct a cleaner story than what happened.

One ADR per decision. Short and honest beats comprehensive and polished.


Step 0: Detect ADR Conventions

Before writing, check for existing ADR structure:

  • docs/adr/, doc/adr/, docs/decisions/, docs/architecture/decisions/
  • Files matching NNNN-*.md — determine the next sequence number
  • .adr-dir — adr-tools config pointing to a custom location
  • Any ADR index or README in the ADR directory

If ADRs already exist, read 1–2 to match format and tone. If none exist, create docs/adr/ and start at 0001.


Step 1: Gather the Decision Context

Determine what was decided and why it needed deciding:

  • From the conversation — if the user described the decision, use that. Ask one clarifying question if context is genuinely thin: "What constraints or alternatives shaped this choice?"
  • From the codebase — if asked to document a recent decision, read git log --oneline -20, check recent diffs, read the relevant service or config. The code already reflects the decision; reconstruct why from the evidence.
  • Don't over-interview. If you have enough to write an honest ADR, write it. You can note gaps in the Context section.

Step 2: Write the ADR

One page. Concrete. Honest about trade-offs.

markdown
# [NNNN]. [Title — short, imperative phrase: "Use PostgreSQL for transactional data"]

**Date:** YYYY-MM-DD
**Status:** [Proposed | Accepted | Deprecated | Superseded by ADR-NNNN]

## Context

[2–4 sentences. What situation forced this decision? What constraints existed?
Be specific: scale, team expertise, timeline, existing stack, cost, operational burden.
"We needed a way to store data" is not context. This is the most important section.]

## Decision

[1–2 sentences. What did we decide? State it plainly.
No hedging. If the decision was "use PostgreSQL on RDS", say exactly that.]

## Alternatives Considered

### [Option A — the real runner-up, not a strawman]

**Pros:** [concrete advantages — performance, operational simplicity, cost, team familiarity]
**Cons:** [concrete disadvantages]
**Why not:** [one sentence — the specific reason this lost to the chosen option]

### [Option B]

**Pros:** ...
**Cons:** ...
**Why not:** ...

## Consequences

**What becomes easier:**

- [concrete benefit — e.g., "ACID transactions for multi-table writes are handled by the DB, not application code"]

**What becomes harder or more expensive:**

- [concrete trade-off — e.g., "Horizontal write scaling requires sharding or a read-replica pattern"]
- [another trade-off]

**What this decision constrains:**

- [downstream implications — e.g., "Services that need this data must go through the API layer, not query the DB directly"]

Calibration rules

  • Context: If you can replace the context with any other project's context and it still reads fine, it's too generic. Rewrite it with the specific constraints that applied here.
  • Alternatives: Minimum 2. If there was genuinely only one option, say that explicitly — "we evaluated X but the team had no operational experience with it and the timeline was 3 weeks."
  • Consequences: Include at least one downside. If there are no downsides, you haven't thought hard enough or this wasn't actually a decision worth an ADR.
  • Length: One page. If it's longer, you're writing an RFC, not an ADR. Split it.

Step 3: Save the ADR

  • Filename: NNNN-short-kebab-title.md — e.g., 0004-use-postgresql-for-transactional-data.md
  • Save to the detected or created ADR directory
  • If an index.md or README.md exists in the ADR directory, append the new entry: | [NNNN] | [Title] | [Status] | [Date] |

Step 4: Output Summary (CLI)

┌─ ADR Written ───────────────────────────────────────────┐
│ ADR-[NNNN]: [Title]                                     │
│ Status: [Accepted/Proposed]   Date: [YYYY-MM-DD]        │
│ Saved: [path]                                           │
├─────────────────────────────────────────────────────────┤
│ Decision                                                │
│   [One sentence summary of what was decided]            │
├─────────────────────────────────────────────────────────┤
│ Key trade-off                                           │
│   [The most important consequence to be aware of]       │
├─────────────────────────────────────────────────────────┤
│ Alternatives considered                                 │
│   [Option A] — [why not, one phrase]                    │
│   [Option B] — [why not, one phrase]                    │
└─────────────────────────────────────────────────────────┘

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Frequently asked questions

What does the Atlas Adr AI skill do?

Write an Architecture Decision Record — document what was decided, why, what alternatives were considered, and what trade-offs were accepted. Use when asked to "write an ADR", "document this decision", or "why did we choose X".

Why use Atlas Adr on TypingMind?

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

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

Which AI models can use Atlas Adr?

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 Atlas Adr?

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

Is the Atlas Adr AI skill free?

Yes. It is published on GitHub by tonone-ai 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇