025 Quality Attribute Discovery

Community

jabrena

025-quality-attribute-discovery

Use when a problem under exploration needs its quality attributes (non-functional requirements) identified and prioritized before architecture and design begin. This should trigger when an issue's Quality Attribute Discovery point of view needs evaluation, or when a maintainer directly asks to discover and prioritize candidate quality attributes for a problem, before any ADR or design work starts. Part of Plinth Toolkit

Overview

Publisher	jabrena
Repository	`plinth`
Skill name	`025-quality-attribute-discovery`
Stars	455
Forks	95
Bundled files	1
License	Apache-2.0
Links	Skill source Repository Homepage

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 jabrena on GitHub. Read the source before you install it.

Installation

Install the 025 Quality Attribute Discovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/jabrena/plinth/tree/main/skills/025-quality-attribute-discovery
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.

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/jabrena/plinth.git /tmp/plinth
mkdir -p .claude/skills
cp -r /tmp/plinth/skills/025-quality-attribute-discovery .claude/skills/025-quality-attribute-discovery

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 025 Quality Attribute Discovery 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 025 Quality Attribute Discovery 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 025 Quality Attribute Discovery 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.

Quality Attribute Discovery

Guide identification and prioritization of the quality attributes a future solution must satisfy, before architecture and design decisions begin. This is an interactive SKILL.

What is covered in this Skill?

Identifying candidate quality attributes (for example performance, security, availability, maintainability, scalability, usability, observability) relevant to the problem
Grounding each candidate in evidence from the problem frame, root causes, assumptions, and context map, not a generic checklist
Prioritizing candidate quality attributes by stakeholder impact and risk
Stopping at a prioritized discovery list rather than selecting or recording an architectural decision
Explicitly not producing ADRs or an architecture direction itself

Constraints

Discover and prioritize candidate quality attributes as input to later architecture work; do not make or record the architecture decision here. When this technique is orchestrated by another workflow, the orchestrator owns clarifying-question sequencing; when applied standalone, ask directly.

MUST read references/025-quality-attribute-discovery.md before applying Quality Attribute Discovery guidance
MUST ground each candidate quality attribute in evidence from the problem frame, root causes, assumptions, or context map
MUST prioritize candidate quality attributes by stakeholder impact and risk, not list them unordered
MUST stop at a prioritized discovery list without selecting or recording an architecture decision
MUST NOT record an architectural decision, ADR, or design direction as part of this skill's output
MUST NOT invent a quality attribute or priority when the available content is vague or ambiguous; flag the gap for a clarifying question instead

When to use this skill

Discover the quality attributes for this problem
Identify non-functional requirements before design begins
Prioritize candidate quality attributes for this issue
Apply quality attribute discovery before architecture decisions
Draft the Quality Attribute Discovery section of a Functional Specification

Workflow

Read the Reference

Read references/025-quality-attribute-discovery.md, then review the problem frame, root-cause findings, assumptions, and context map for evidence of quality pressure.

Identify Candidate Quality Attributes

Identify candidate quality attributes grounded in that evidence, avoiding a generic unfiltered checklist.

Prioritize by Impact and Risk

Prioritize the candidate quality attributes by stakeholder impact and risk if unmet.

Stop Before Architecture Decisions

Stop at the prioritized discovery list; do not select an architecture approach or record an ADR here.

Report the Discovery List

Report the prioritized quality attributes, stating explicitly that the output stops at this discovery list and does not select or record an architecture decision; flag any item left open pending a clarifying answer.

Reference

For detailed guidance, examples, and constraints, see references/025-quality-attribute-discovery.md.

Bundled files

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

references/025-quality-attribute-discovery.md7 KB

Frequently asked questions

What does the 025 Quality Attribute Discovery AI skill do?

Why use 025 Quality Attribute Discovery on TypingMind?

Because you install it once and use it with any model. 025 Quality Attribute Discovery 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 025 Quality Attribute Discovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jabrena/plinth/tree/main/skills/025-quality-attribute-discovery. 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 025 Quality Attribute Discovery?

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 025 Quality Attribute Discovery?

As many as you like. As long as a model supports skills, you can use 025 Quality Attribute Discovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 025 Quality Attribute Discovery AI skill free?

Yes. It is published on GitHub by jabrena under the Apache-2.0 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Quality Attribute Discovery

Constraints

When to use this skill

Workflow

Reference

Bundled files

Frequently asked questions

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