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Context Driven Testing

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proffesor-for-testing
context-driven-testing

Apply context-driven testing principles where practices are chosen based on project context, not universal 'best practices'. Use when making testing decisions, questioning dogma, or adapting approaches to specific project needs.

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill namecontext-driven-testing
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 Context Driven Testing 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/context-driven-testing .claude/skills/context-driven-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Context Driven Testing 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 Context Driven Testing 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 Context Driven Testing 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.

Context-Driven Testing

<default_to_action> When making testing decisions or adapting approaches:

  1. ANALYZE context: project goals, constraints, risks, team skills
  2. QUESTION practices: "Why this? What risk does it address? What's the cost?"
  3. INVESTIGATE not just check: Does software solve the problem, or create new ones?
  4. ADAPT approach based on context, not "best practices"
  5. DOCUMENT discoveries, not pre-written plans

Quick Context Analysis:

  • Mission: "Find important problems fast enough to matter" (not "execute test cases")
  • Risk: Safety-critical = high rigor; internal tool = lighter touch
  • Constraints: Startup with tight timeline ≠ enterprise with compliance
  • Skills: Novice needs structure; expert adapts intuitively

Critical Success Factors:

  • No "best practices" work everywhere - only good practices in context
  • Testing is investigation, not script execution
  • Context changes; your approach should too </default_to_action>

Quick Reference Card

When to Use

  • Making testing decisions for new project
  • Questioning "that's how it's done" dogma
  • Adapting approach to specific constraints
  • Exploratory testing sessions

RST Heuristics

HeuristicApplication
SFDIPOTStructure, Function, Data, Interfaces, Platform, Operations, Time
OraclesConsistency with history, similar products, expectations, docs
ToursBusiness District, Historical, Bad Neighborhood, Tourist, Museum

Context-Driven Decisions

Example: Test Automation Level

Startup Context:

  • Small team, rapid changes, unclear product-market fit
  • Decision: Light automation on critical paths, heavy exploratory
  • Rationale: Requirements change too fast for extensive automation

Enterprise Context:

  • Stable features, regulatory requirements, large team
  • Decision: Comprehensive automated regression suite
  • Rationale: Stability allows automation investment to pay off

Example: Documentation

Regulated (FDA/medical):

  • Decision: Detailed test protocols, traceability matrices
  • Rationale: Regulatory compliance isn't optional

Fast-paced startup:

  • Decision: Lightweight session notes, risk logs
  • Rationale: Bureaucracy slows more than it helps

Agent-Assisted Context-Driven Testing

typescript
// Agent analyzes context and recommends approach
const context = await Task("Analyze Context", {
  project: 'e-commerce-platform',
  stage: 'startup',
  constraints: ['timeline: tight', 'budget: limited'],
  risks: ['payment-security', 'high-volume']
}, "qe-fleet-commander");

// Context-aware agent selection
// - qe-security-scanner (critical risk)
// - qe-performance-tester (high volume)
// - Skip: qe-visual-tester (low priority in startup context)

// Adaptive testing strategy
await Task("Generate Tests", {
  context: 'startup',
  focus: 'critical-paths-only',
  depth: 'smoke-tests',
  automation: 'minimal'
}, "qe-test-generator");

Agent Coordination Hints

Memory Namespace

aqe/context-driven/
├── context-analysis/*    - Project context snapshots
├── decisions/*           - Testing decisions with rationale
├── discoveries/*         - What was learned during testing
└── adaptations/*         - How approach changed over time

Fleet Coordination

typescript
const contextFleet = await FleetManager.coordinate({
  strategy: 'context-driven',
  context: {
    type: 'greenfield-saas',
    stage: 'growth',
    compliance: 'gdpr-only'
  },
  agents: ['qe-test-generator', 'qe-security-scanner', 'qe-performance-tester'],
  exclude: ['qe-visual-tester', 'qe-requirements-validator']  // Not priority
});

Practical Tips

  1. Start with risk assessment - List features, ask: How likely to fail? How bad? How hard to test?
  2. Time-box exploration - 2 hours checkout, 30 min error handling, 15 min per browser
  3. Document discoveries - Not "Enter invalid email, verify error" but "Payment API returns 500 instead of 400, no user-visible error. Bug filed."
  4. Talk to humans - Developers, users, support, product
  5. Pair with others - Different perspectives = different bugs

Related Skills


Remember

With Agents: Agents analyze context, adapt strategies, and learn what works in your situation. Use agents to scale context-driven thinking while maintaining human judgment for critical decisions.

Frequently asked questions

What does the Context Driven Testing AI skill do?

Apply context-driven testing principles where practices are chosen based on project context, not universal 'best practices'. Use when making testing decisions, questioning dogma, or adapting approaches to specific project needs.

Why use Context Driven Testing on TypingMind?

Because you install it once and use it with any model. Context Driven Testing 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 Context Driven Testing in TypingMind?

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

Which AI models can use Context Driven Testing?

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 Context Driven Testing?

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

Is the Context Driven Testing 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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