Quality Metrics

Q: Why use Quality Metrics on TypingMind?

Because you install it once and use it with any model. Quality Metrics 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.

Q: How do I install Quality Metrics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/quality-metrics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Q: Which AI models can use Quality Metrics?

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.

Q: How many AI models can I use with Quality Metrics?

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

Q: Is the Quality Metrics AI skill free?

Yes. It is published on GitHub by proffesor-for-testing under the MIT license. You only pay your own AI provider for the tokens you use.

Q: 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.

Q: 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.

Community

proffesor-for-testing

quality-metrics

Tracks quality metrics including defect density, test effectiveness ratio, DORA metrics, and mean time to detection. Use when establishing quality dashboards, defining KPIs, evaluating test suite effectiveness, or reporting quality trends to stakeholders.

Overview

Publisher	proffesor-for-testing
Repository	`agentic-qe`
Skill name	`quality-metrics`
Stars	480
Forks	92
Bundled files	3
License	MIT
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.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by proffesor-for-testing on GitHub. Read the source before you install it.

Installation

Install the Quality Metrics 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.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/quality-metrics

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/proffesor-for-testing/agentic-qe.git /tmp/agentic-qe
mkdir -p .claude/skills
cp -r /tmp/agentic-qe/assets/skills/quality-metrics .claude/skills/quality-metrics

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

Use it in TypingMind

Enable Quality Metrics 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 Quality Metrics 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 Quality Metrics 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 Metrics

<default_to_action> When measuring quality or building dashboards:

MEASURE outcomes (bug escape rate, MTTD) not activities (test count)
AVOID vanity metrics: 100% coverage means nothing if tests don't catch bugs
SET thresholds that drive behavior (quality gates block bad code)
TREND over time: Direction matters more than absolute numbers </default_to_action>

Quick Reference Card

When to Use

Building quality dashboards
Defining quality gates
Evaluating testing effectiveness
Justifying quality investments

Quality Gate Thresholds

Metric	Blocking Threshold	Warning
Test pass rate	100%	-
Critical coverage	> 80%	> 70%
Security critical	0	-
Performance p95	< 200ms	< 500ms
Flaky tests	< 2%	< 5%

Dashboard Design

typescript
// Agent generates quality dashboard
await Task("Generate Dashboard", {
  metrics: {
    delivery: ['deployment-frequency', 'lead-time', 'change-failure-rate'],
    quality: ['bug-escape-rate', 'test-effectiveness', 'defect-density'],
    stability: ['mttd', 'mttr', 'availability'],
    process: ['code-review-time', 'flaky-test-rate', 'coverage-trend']
  },
  visualization: 'grafana',
  alerts: {
    critical: { bug_escape_rate: '>20%', mttr: '>24h' },
    warning: { coverage: '<70%', flaky_rate: '>5%' }
  }
}, "qe-quality-analyzer");

Quality Gate Configuration

json
{
  "qualityGates": {
    "commit": {
      "coverage": { "min": 80, "blocking": true },
      "lint": { "errors": 0, "blocking": true }
    },
    "pr": {
      "tests": { "pass": "100%", "blocking": true },
      "security": { "critical": 0, "blocking": true },
      "coverage_delta": { "min": 0, "blocking": false }
    },
    "release": {
      "e2e": { "pass": "100%", "blocking": true },
      "performance_p95": { "max_ms": 200, "blocking": true },
      "bug_escape_rate": { "max": "10%", "blocking": false }
    }
  }
}

Agent-Assisted Metrics

typescript
// Calculate quality trends
await Task("Quality Trend Analysis", {
  timeframe: '90d',
  metrics: ['bug-escape-rate', 'mttd', 'test-effectiveness'],
  compare: 'previous-90d',
  predictNext: '30d'
}, "qe-quality-analyzer");

// Evaluate quality gate
await Task("Quality Gate Evaluation", {
  buildId: 'build-123',
  environment: 'staging',
  metrics: currentMetrics,
  policy: qualityPolicy
}, "qe-quality-gate");

Agent Coordination Hints

Memory Namespace


aqe/quality-metrics/
├── dashboards/*         - Dashboard configurations
├── trends/*             - Historical metric data
├── gates/*              - Gate evaluation results
└── alerts/*             - Triggered alerts

Fleet Coordination

typescript
const metricsFleet = await FleetManager.coordinate({
  strategy: 'quality-metrics',
  agents: [
    'qe-quality-analyzer',         // Trend analysis
    'qe-test-executor',            // Test metrics
    'qe-coverage-analyzer',        // Coverage data
    'qe-production-intelligence',  // Production metrics
    'qe-quality-gate'              // Gate decisions
  ],
  topology: 'mesh'
});

Related Skills

agentic-quality-engineering - Agent coordination
cicd-pipeline-qe-orchestrator - Quality gates
risk-based-testing - Risk-informed metrics
shift-right-testing - Production metrics

Remember

With Agents: Agents track metrics automatically, analyze trends, trigger alerts, and make gate decisions. Use agents to maintain continuous quality visibility.

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 Quality Metrics AI skill do?

Why use Quality Metrics on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/quality-metrics. 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 Quality Metrics?

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 Quality Metrics?

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

Is the Quality Metrics AI skill free?

Yes. It is published on GitHub by proffesor-for-testing 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Quality Metrics

Quick Reference Card

When to Use

Quality Gate Thresholds

Dashboard Design

Quality Gate Configuration

Agent-Assisted Metrics

Agent Coordination Hints

Memory Namespace

Fleet Coordination

Related Skills

Remember

Bundled files

Frequently asked questions

Related AI Skills

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