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Holistic Testing Pact

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holistic-testing-pact

Apply the Holistic Testing Model evolved with PACTS (Proactive, Autonomous, Collaborative, Targeted, Structured) principles. Use when designing comprehensive test strategies for Classical, AI-assisted, Agent based, or Agentic Systems building quality into the team, or implementing whole-team quality practices.

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill nameholistic-testing-pact
Stars
480
Forks
92
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 proffesor-for-testing on GitHub. Read the source before you install it.

Installation

Install the Holistic Testing Pact 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/proffesor-for-testing/agentic-qe.git /tmp/agentic-qe
mkdir -p .claude/skills
cp -r /tmp/agentic-qe/assets/skills/holistic-testing-pact .claude/skills/holistic-testing-pact
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Holistic Testing Pact 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 Holistic Testing Pact 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 Holistic Testing Pact 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.

Holistic Testing Model with PACTS Principles

<default_to_action> When designing test strategies or building quality into teams:

  1. APPLY PACTS principles: Proactive (test before bugs), Autonomous (teams own quality), Collaborative (whole-team responsibility), Targeted (risk-focused), Structured (governance, observability, and explainability of agent behavior)
  2. IDENTIFY quadrant focus: Technology-facing (unit, integration, performance) or Business-facing (acceptance, exploratory, usability)
  3. SELECT agents based on PACTS dimension and testing quadrant
  4. IMPLEMENT feedback loops that catch issues in minutes, not days
  5. MEASURE outcomes (bug escape rate, release confidence, agent confidence) not activities (test count)

Quick PACTS Application:

  • Proactive → Design testability into architecture, risk analysis during refinement
  • Autonomous → Devs run tests locally, CI pipeline with no manual gates
  • Collaborative → Three Amigos, QE pairs with dev, shared test ownership
  • Targeted → Risk-based planning, focus on critical flows, kill valueless tests
  • Structured → Governance, observability, and explainability of agent behavior; measure confidence, not trust

Critical Success Factors:

  • Quality is a whole-team responsibility, not a QA phase
  • QA as enablers (build infrastructure, coach), not gatekeepers
  • Fast feedback during development, not after </default_to_action>

Quick Reference Card

When to Use

  • Designing comprehensive test strategies
  • Building quality culture in teams
  • Choosing testing approach for new projects
  • Evolving from sequential QA to concurrent quality

PACTS Principles

PrincipleFocusAnti-Pattern
ProactiveTest before code, design testabilityWaiting for bugs to find you
AutonomousTeams deploy when readyQA as manual gatekeepers
CollaborativeWhole-team quality thinkingQA works in isolation
TargetedRisk-based, high-value testsExhaustive checkbox testing
StructuredGovernance, observability, explainability of agent behaviorBolting agentic AI onto chaos

Holistic Testing Quadrants

QuadrantPurposeExamples
Tech + SupportFast feedbackUnit, component, integration tests
Tech + CritiqueFind limitsPerformance, security, chaos
Business + SupportShared understandingBDD, acceptance tests
Business + CritiqueDiscover unknownsExploratory, usability, A/B

Agent Selection by PACTS + Quadrant

PACTS DimensionAgents
Proactive + Techqe-test-generator, qe-requirements-validator
Autonomous + Techqe-test-executor, qe-coverage-analyzer
Collaborativeqe-fleet-commander (orchestration)
Targetedqe-regression-risk-analyzer, qe-quality-gate
Structuredqe-fleet-commander, qe-quality-gate (governance, observability, explainability)

PACTS in Practice

Proactive: Test Before Bugs

javascript
// During API design, ask: "How will we know if this times out under load?"
// Build observability from start
await Task("Risk Analysis", {
  phase: 'refinement',
  question: 'What could go wrong and how will we know?'
}, "qe-requirements-validator");

Autonomous: Teams Own Quality

  • Developers run full test suite locally
  • CI fails fast with clear diagnostics
  • No manual deployment approvals
  • Self-service test environments

Collaborative: Whole-Team Thinking

  • QE attends planning and refinement
  • Three Amigos for every user story
  • Shared ownership of test code
  • Ensemble testing for complex scenarios

Targeted: Test What Matters

javascript
// E-commerce checkout? Test thoroughly.
// Admin panel used twice a month? Lighter touch.
await Task("Risk-Based Planning", {
  critical: ['checkout', 'payment'],
  light: ['admin-panel', 'settings']
}, "qe-regression-risk-analyzer");

Structured: Governance, Observability, Explainability

The 5th principle for agentic systems. As agents take on quality work, you need governance (boundaries and guardrails), observability (what agents did and why), and explainability (decisions you can audit). Especially important in regulated industries.

  • We measure CONFIDENCE, not trust — trust is a human feeling; confidence can be mathematically explained.
  • Ships with a playbook + readiness-assessment guide.
  • Anti-pattern: bolting agentic AI onto chaos. Structure first, then automate.
javascript
// Make agent behavior auditable and explainable, not a black box
await Task("Agent Governance Review", {
  guardrails: ['scope-boundaries', 'approval-gates'],
  observability: ['decision-traces', 'confidence-scores'],
  explainability: 'every-decision-auditable'
}, "qe-fleet-commander");

From PACT to PACTS

PACT originated with Reuven Cohen (Agentics Foundation) and was adapted to quality engineering by Dragan Spiridonov. PACTS adds a fifth principle — Structured — inspired by DORA's research on AI-assisted delivery and platform-engineering quality measurement. The addition reflects that autonomous, agentic quality work needs governance, observability, and explainability to be trustworthy at scale.


Evolution from Traditional

Old Way (Sequential)Holistic + PACTS (Concurrent)
Dev writes → QA tests → bugs found → fixesTeam discusses what to build and how to test
Slow feedback, finger-pointingFast feedback, shared ownership
Quality as gatekeepingQuality as enabler
QA on critical pathQA builds infrastructure, coaches

Success Signals

  • Features deploy multiple times per day
  • Bug escape rate trending down
  • Team discusses quality naturally
  • Developers write tests without being told
  • Releases are boring (in a good way)

Agent Coordination Hints

Memory Namespace

aqe/holistic-testing/
├── pacts-assessment/*    - PACTS maturity analysis
├── quadrant-coverage/*   - Coverage per quadrant
├── team-metrics/*        - Quality ownership metrics
└── feedback-loops/*      - Cycle time data

Fleet Coordination

typescript
const holisticFleet = await FleetManager.coordinate({
  strategy: 'holistic-testing',
  pacts: { proactive: true, autonomous: true, collaborative: true, targeted: true, structured: true },
  agents: [
    'qe-fleet-commander',       // Orchestration
    'qe-test-generator',        // Tech quadrant
    'qe-requirements-validator', // Business quadrant
    'qe-quality-analyzer'       // Metrics
  ],
  topology: 'mesh'
});

Related Skills


Remember

PACTS = Proactive + Autonomous + Collaborative + Targeted + Structured

Quality is built in, not tested in. Teams own quality. QA enables, doesn't gate. Test what matters, skip what doesn't. Govern agent behavior with observability and explainability — measure confidence, not trust. Measure outcomes, not activities.

With Agents: Agents analyze PACTS maturity, recommend quadrant coverage, and coordinate whole-team quality. Use agents to scale holistic thinking while maintaining human judgment.

Frequently asked questions

What does the Holistic Testing Pact AI skill do?

Apply the Holistic Testing Model evolved with PACTS (Proactive, Autonomous, Collaborative, Targeted, Structured) principles. Use when designing comprehensive test strategies for Classical, AI-assisted, Agent based, or Agentic Systems building quality into the team, or implementing whole-team quality practices.

Why use Holistic Testing Pact on TypingMind?

Because you install it once and use it with any model. Holistic Testing Pact 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 Holistic Testing Pact in TypingMind?

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

Which AI models can use Holistic Testing Pact?

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 Holistic Testing Pact?

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

Is the Holistic Testing Pact 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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