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Decision Record

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murphytrueman
decision-record

Write a narrative decision record (ADR) for a design system choice: context, options, trade-offs, consequences, including declined proposals. Triggers: document this decision, ADR, why did we choose, capture the reasoning. Machine-checkable rules: governance-encoder.

Overview

Publishermurphytrueman
Repositorydesign-system-ops
Skill namedecision-record
Stars
211
Forks
7
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 murphytrueman on GitHub. Read the source before you install it.

Installation

Install the 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/murphytrueman/design-system-ops/tree/main/skills/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/murphytrueman/design-system-ops.git /tmp/design-system-ops
mkdir -p .claude/skills
cp -r /tmp/design-system-ops/skills/decision-record .claude/skills/decision-record
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Decision record

A skill for creating structured decision records for design system choices. Covers component decisions, token architecture choices, tooling selections, governance policies, and any other decision worth recording so future contributors do not have to reverse-engineer the reasoning.

Before you begin: verify references

Confirm that every path in this skill's frontmatter references: exists relative to this SKILL.md. If any is missing, stop: the install is incomplete, usually because a flattening installer (for example npx skills install) dropped the repo-root knowledge-notes/ directory. Tell the user to reinstall by a method in 1-INSTALL.md and run verify-install.sh from the install root. Proceed without the references only if the user explicitly says to, and then say in the output that it was produced without the pack's reference material.

Context

Design systems accumulate decisions faster than they accumulate documentation. The result is a system where the current state is known but the reasoning is not — which means the same debates recur, constraints get ignored because their origin is forgotten, and new team members spend weeks learning by collision what a thirty-minute conversation would have covered.

A decision record does not need to be formal. It needs to be findable and honest. The format below is lightweight enough to write in under twenty minutes and structured enough to be useful when someone reads it twelve months later.


Step 0: Decision trigger checklist

Before writing a record, confirm this decision warrants one. Not every choice needs a formal record — but more decisions warrant records than teams typically capture. Use this checklist:

Create a decision record if any of these are true:

  • The decision affects more than one consuming team
  • The decision involves a trade-off where reasonable people could disagree
  • The decision will be referenced during future contribution reviews
  • The decision changes a naming convention, token architecture, or API contract
  • The decision deprecates or removes something from the system
  • Someone asked "why do we do it this way?" and the answer was not documented
  • The same question has come up more than once

Skip the decision record if all of these are true:

  • The decision affects only one component's internal implementation
  • The decision is easily reversible with no consumer impact
  • The decision follows an existing, documented convention without exception

When in doubt, write the record. A twenty-minute investment in documentation saves hours of re-discovery and re-debate.

Step 0b: Find the team's existing records

Before writing, look for where decisions already live and match it: docs/adr/, docs/decisions/, adr/, decisions/, an .adr-dir file (adr-tools), a MADR template (template.md with "Decision Drivers" and "Considered Options"), a log4brains config, or a docs-platform section the user names. If records exist, use their template's section names and their numbering (sequential NNNN- prefixes are the norm; take the next number). If none exist, use docs/decisions/NNNN-<kebab-title>.md starting at 0001, and say so. Don't invent a date-based id scheme: two decisions in one month would collide.

Step 1: Clarify the decision

Ask for or confirm:

  • What was decided? (One clear sentence)
  • When was this decided?
  • Who was involved in making the decision?
  • Is this decision already made, or is it still in progress?
  • What options were considered, and why was each rejected? Get these from the user or from linked sources (meeting notes, PR threads, RFCs)

If the decision is in progress, the record still gets written — just with an open status. Decision records for in-progress decisions are often the most valuable, because they capture the thinking before it is lost in the gap between discussion and resolution.

Step 2: Write the record

Use the following structure. Impact assessment is conditional; every other section appears in every record.

Record only options, reasons and trade-offs the user supplied or that appear in the sources you were given. Where something is missing, write [unknown — ask X] naming who would know. Never infer rationale: a plausible reason that nobody actually gave is worse than a visible gap, because future readers will treat it as fact.


Decision record: [title]

ID: [sequential, matching the team's existing records, e.g. 0007] Date: [when the decision was made or this record was created] Status: Proposed / Accepted / Declined / Superseded / Deprecated Deciders: [who made the decision] Author: [who wrote this record, if different]

Use Declined for proposals that were turned down (for example, rejection records from contribution-workflow); the record then explains which criteria weren't met and under what conditions it could be revisited.


Context

What was the situation that made this decision necessary? What problem was being solved, or what question needed an answer?

Write this as a factual description of the state of the world at the time of the decision. Include any constraints that shaped the decision space — technical, organisational, time-based, or otherwise. Do not frame the context to make the eventual decision look inevitable. Future readers need to understand the real landscape, including the pressures that influenced the outcome.

Two to four sentences is usually enough.

Decision drivers

The three to five forces that decided it: a constraint, a requirement, a cost, a deadline, a team preference. Each from the user or a source; none inferred. This is the section future readers use to tell whether the decision still holds when the forces change.

Options considered

List each option that was genuinely evaluated. For each option:

  • Name or brief description
  • Why it was a viable candidate
  • Why it was not chosen (if it was not)

Do not list options that were not seriously considered — this section should reflect the actual decision space, not a post-hoc justification exercise. If only one option was considered, say so and explain why.

The option that was eventually chosen should also appear here, with a note that it was selected and a forward reference to the decision section.

Decision

State the decision in one sentence. Then explain the primary reasoning in two to four sentences.

Be honest about trade-offs. If the chosen option had weaknesses that were accepted, name them. If the decision was influenced by non-technical factors — timelines, team preferences, tooling constraints — include that. Decision records that paper over trade-offs are records of what was decided, not why, which makes them significantly less useful.

Impact assessment (API, token or breaking changes only)

Include this section when the decision changes a component API, token names or values, or anything consumers must migrate. Skip it for conventions, tooling and process decisions with no migration cost.

Impact dimensionMeasurement
Files affected[count — from grep/search if available]
Components affected[count and names]
Consuming teams affected[count and names]
Migration effort[user-supplied estimate, or "not measured"]
Token/API changes required[count]
Breaking changes[yes/no — if yes, list them]
Timeline to full adoption[user-supplied, or "not measured"]

If codebase access is available, run searches to populate the counts. For example, if the decision is to adopt DTCG token format, count how many token files need restructuring, how many consuming files reference tokens, and how many teams own those files.

Where a row wasn't measured, write "not measured" and name the audit skill that would measure it. Don't fill rows with estimates; effort and timeline only go in if the user supplied them, labelled as theirs.

Consequences

What changes as a result of this decision? What becomes easier, and what becomes harder?

This section should cover both intended outcomes and known risks. If the decision introduces a dependency, a constraint, or a new obligation for contributors, document it here. If it closes off a future option, name that too.

Supersedes / superseded by

If this decision replaces a previous decision, reference it here. If this decision is later superseded, this field gets updated with the reference to the new record.

Related records

Any other decision records relevant to understanding this one. Links or IDs are sufficient.

Step 3: Write the file

Write the record to the location and with the numbering found in Step 0b, and say the path. If the team keeps decisions on a documentation platform rather than in the repo, produce the record in chat for pasting and say where it belongs. If the location is unknown and the user hasn't said, ask once; don't leave the record only in chat by default.

Step 4: Suggest a review trigger

Decision records go stale. Suggest a condition that should prompt this record to be revisited — for example, a tooling change, a team size threshold, a specific dependency version, or a timeline milestone.

This does not need to be elaborate. One sentence is enough: "Revisit this decision if the team grows beyond ten active contributors or if the token tooling changes."

Step 5: Summarise in chat

  • Headline: the decision, its status and the file written
  • Open: every [unknown — ask X] left in the record
  • Next: the skills that follow from the decision's consequences, in chat rather than in the permanent record: deprecation-process for a removal, token-audit for a token architecture change, version-bump-advisor then change-communication for an API change, governance-encoder for a new convention, codemod-generator when many consumers must change, stakeholder-brief for a large investment
  • Scope: what sources were read (meeting notes, PRs, RFCs) and what the impact counts came from

Quality checks

  • The record matches the team's existing ADR format and numbering, and was written to the repository or produced for the platform the team uses
  • Decision drivers are present and each traces to the user or a source
  • Context describes the actual situation, not a setup for the conclusion
  • Options considered reflects the real decision space, not a post-hoc list
  • Every option, reason and trade-off traces to the user or a named source; gaps are marked [unknown — ask X]
  • Decision section names the trade-offs that were accepted, not just the benefits
  • Consequences covers both intended outcomes and known risks
  • Record is written for someone who was not in the room — no assumed context
  • Status field is set correctly
  • A review trigger is included

Frequently asked questions

What does the Decision Record AI skill do?

Write a narrative decision record (ADR) for a design system choice: context, options, trade-offs, consequences, including declined proposals. Triggers: document this decision, ADR, why did we choose, capture the reasoning. Machine-checkable rules: governance-encoder.

Why use Decision Record on TypingMind?

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

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

Which AI models can use 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 Decision Record?

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

Is the Decision Record AI skill free?

Yes. It is published on GitHub by murphytrueman 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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