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Decision

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Tranz007
decision

Recover or preserve consequential UX and architecture rationale. Use when a designer asks why something was designed a certain way, whether a choice deserves an ADR/design decision record, or wants to capture context, evidence, alternatives, rationale, consequences, and status without documenting trivial changes.

Overview

PublisherTranz007
Repositoryux-skills
Skill namedecision
Stars
48
Forks
4
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by Tranz007 on GitHub. Read the source before you install it.

Installation

Install the Decision 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/Tranz007/ux-skills/tree/main/skills/decision
  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/Tranz007/ux-skills.git /tmp/ux-skills
mkdir -p .claude/skills
cp -r /tmp/ux-skills/skills/decision .claude/skills/decision
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Decision 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 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 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

Help future designers and engineers understand why, without creating documentation theater.

Always

  • Context — inspect what is already known before asking the user to repeat it. Use .ux/INTENT.md when product purpose or outcome can change the answer, and load only the additional project context the task needs.
  • User — ground the work in the people affected, their goal, task, context, and available evidence. Do not invent user needs, behaviors, or personas.
  • Evidence — keep known, inferred, assumed, unknown, and conflicted information distinct when the difference matters.
  • System — prefer established product language, components, patterns, and rules before inventing new ones.
  • Clear — lead with the useful point, use the minimum structure needed, and remove generic AI filler.
  • Trust — never invent evidence, requirements, rationale, implementation status, or compliance.
  • Outcome — for substantial multi-step work, keep intent active, use a small .ux/STATE.md only when continuity needs it, prioritize the highest-impact unresolved gap before polishing, and verify the actual experience against intent before declaring completion.

Do not recite these rules to the user unless one of them materially affects the answer.

Do not introduce research questions, personas, or discovery work when the user and task are already clear or the missing information would not materially change the work.

When the user asks why

Search available decision records, research, requirements, issue/PR history, design-system documentation, implementation history, and the smallest relevant .ux/ context.

Separate:

  • documented rationale;
  • rationale strongly supported by artifacts;
  • plausible inference;
  • missing or conflicting rationale.

Current behavior proves what exists, not why it was chosen. Never invent a neat historical explanation just because one sounds plausible.

When a new decision is being made

A durable record is useful when a choice:

  • introduces or changes a shared interaction or system pattern;
  • has meaningful accessibility, technical, policy, privacy, or operational consequences;
  • rejects an obvious alternative for a non-obvious reason;
  • affects multiple teams or journeys;
  • is expensive to reverse;
  • is likely to be questioned later.

Routine layout, copy, and styling changes usually do not need a decision record.

Check intent impact

After a consequential decision, check whether it explicitly changes why the product exists, the intended outcome, primary people affected, scope, non-goals, material constraints, or definition of success.

If it does, INTENT.md should change with it. Preserve the evidence or decision that caused the change. Do not rewrite intent merely because implementation changed, and do not infer a strategy shift from code alone.

Use the team's system

If the project has ADR, RFC, design-decision, or documentation conventions, use them. Do not introduce a competing format.

If none exists, keep it lightweight:

text
Decision
Status
Context and evidence
Options considered
Decision and rationale
Consequences
Open questions
Related artifacts

Preserve what was actually known at decision time. Do not rewrite assumptions as evidence after the fact.

Output

Answer a "why" question directly when that is all the user needs. Create or recommend a durable record only when the decision is consequential enough to justify it.

If the decision materially changes product intent, say so explicitly and include the intent update in the work when the user is asking you to preserve the decision in project context.

Contrast example

Bad:

Create an ADR documenting why the primary button was moved below the form.

Good:

This does not need a durable decision record. It is a local layout change with no shared pattern, architectural consequence, or non-obvious tradeoff. The design or PR history is enough.

Why: the good response preserves decision records for choices future teams are likely to question or need to understand.

Examples

  • "Why was this designed this way?"
  • "Does this deserve an ADR?"
  • "Record why we extended the existing component instead of creating a new 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 Decision AI skill do?

Recover or preserve consequential UX and architecture rationale. Use when a designer asks why something was designed a certain way, whether a choice deserves an ADR/design decision record, or wants to capture context, evidence, alternatives, rationale, consequences, and status without documenting trivial changes.

Why use Decision on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Tranz007/ux-skills/tree/main/skills/decision. 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 Decision?

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?

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

Is the Decision AI skill free?

Yes. It is published on GitHub by Tranz007 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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