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Kb Testing Strategy

Organization
Community-Access
kb-testing-strategy

Reference data, not a reviewer. Accessibility testing decision trees, browser/AT compatibility matrices, manual vs. automated test coverage, regression testing patterns, and acceptance criteria templates for user stories.

Overview

PublisherCommunity-Access
Repositoryaccessibility-agents
Skill namekb-testing-strategy
Stars
429
Forks
51
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 Community-Access on GitHub. Read the source before you install it.

Installation

Install the Kb Testing Strategy 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/Community-Access/accessibility-agents/tree/main/skills/kb-testing-strategy
  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/Community-Access/accessibility-agents.git /tmp/accessibility-agents
mkdir -p .claude/skills
cp -r /tmp/accessibility-agents/skills/kb-testing-strategy .claude/skills/kb-testing-strategy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kb Testing Strategy 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 Kb Testing Strategy 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 Kb Testing Strategy 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.

Testing Strategy Skill

Decision frameworks for accessibility testing — when to use automated tools vs. manual testing, which screen reader + browser combinations to test, and how to write accessibility acceptance criteria.


Automated vs. Manual Testing Coverage

Each row, with what automated tools catch (~30-40%) and what requires manual testing (~60-70%).

What Automated Tools Catch (~30-40%)What Requires Manual Testing (~60-70%)
Missing alt text on imagesAlt text quality and accuracy
Missing form labelsLabel clarity and helpfulness
Color contrast ratios (computed)Color contrast in context (gradients, images)
Missing lang attributeCorrect language identification
Duplicate IDsLogical reading order
Missing ARIA roles on custom widgetsCorrect ARIA role for the interaction pattern
Heading hierarchy violationsHeading text meaningfulness
Empty links and buttonsLink/button text descriptiveness
Missing table headersTable caption and header association quality
Syntax errors in ARIAScreen reader announcement correctness

Browser + Screen Reader Compatibility Matrix

Primary Test Combinations (Required)

Each screen reader, with its browser, OS and priority.

Screen ReaderBrowserOSPriority
NVDAFirefoxWindowsMust test
NVDAChromeWindowsMust test
JAWSChromeWindowsMust test
VoiceOverSafarimacOSMust test
VoiceOverSafariiOSMust test
TalkBackChromeAndroidShould test

Secondary (Nice to Have)

Each screen reader, with its browser and OS.

Screen ReaderBrowserOS
NarratorEdgeWindows
JAWSEdgeWindows

Testing Decision Tree

text
Is it a new component or page?
├── Yes → Full test coverage (automated + manual)
│   ├── Run axe-core / Lighthouse scan
│   ├── Keyboard-only navigation test
│   ├── Screen reader announcement test (NVDA + VoiceOver minimum)
│   └── Visual check at 200% zoom
└── No → What changed?
    ├── Colors/styling → Contrast check + visual review
    ├── Interactive behavior → Keyboard + screen reader test
    ├── Content/text → Screen reader announcement check
    ├── Layout/order → Reading order + focus order test
    └── Dependencies updated → Regression scan (axe-core)

Regression Testing Patterns

CI Pipeline Accessibility Gates

  1. axe-core scan: Run on every PR. Fail on new critical/serious violations.
  2. Baseline management: Store known issues in .a11y-baseline.json. Only fail on new issues.
  3. Lighthouse score threshold: Set minimum accessibility score (e.g., 90). Fail on regression.
  4. Visual regression: Capture screenshots at 200% zoom. Compare for focus indicator and layout changes.

Preventing Regressions

  • Add accessibility assertions to existing component tests (see testing-accessibility.instructions.md)
  • Include keyboard navigation in E2E test suites
  • Track accessibility score trends over time (not just pass/fail)

Acceptance Criteria Template

For User Stories

text
Given [context],
When [action by keyboard/mouse/screen reader],
Then [accessible outcome]:

- [ ] Component has an accessible name (via label, aria-label, or aria-labelledby)
- [ ] Component has the correct ARIA role
- [ ] Component is reachable and operable by keyboard (Tab, Enter, Space, Escape, Arrows as appropriate)
- [ ] Focus is visible when the component receives focus
- [ ] State changes are announced to screen readers (aria-expanded, aria-selected, aria-checked, etc.)
- [ ] Error messages are associated with their inputs (aria-describedby or aria-errormessage)
- [ ] Content is readable at 200% zoom without horizontal scrolling
- [ ] Color is not the only means of conveying information

Common Testing Tools

Each tool, with type and best for.

ToolTypeBest For
axe-core / @axe-core/cliAutomatedCI/CD integration, broad violation scan
LighthouseAutomatedPerformance + accessibility combined score
WAVESemi-automatedVisual overlay of accessibility features/issues
Accessibility InsightsSemi-automatedFastPass (automated) + Assessment (guided manual)
pa11yAutomatedCI/CD, HTML CodeSniffer rules
jest-axeUnit testComponent-level axe scans in jest
cypress-axeE2E testPage-level axe scans in Cypress
playwright + @axe-core/playwrightE2E testPage-level axe scans in Playwright

Screen Reader Testing Quick Reference

NVDA (Windows)

Each key, with its action.

KeyAction
Insert + SpaceToggle focus/browse mode
TabMove to next focusable element
HNext heading
DNext landmark
FNext form field
TNext table
Insert + F7Elements list (links, headings, landmarks)

VoiceOver (macOS)

Each key, with its action.

KeyAction
VO (Ctrl+Option) + RightMove to next element
VO + SpaceActivate current element
VO + UOpen rotor (navigate by type)
VO + Cmd + HNext heading

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 Kb Testing Strategy AI skill do?

Reference data, not a reviewer. Accessibility testing decision trees, browser/AT compatibility matrices, manual vs. automated test coverage, regression testing patterns, and acceptance criteria templates for user stories.

Why use Kb Testing Strategy on TypingMind?

Because you install it once and use it with any model. Kb Testing Strategy 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 Kb Testing Strategy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Community-Access/accessibility-agents/tree/main/skills/kb-testing-strategy. 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 Kb Testing Strategy?

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 Kb Testing Strategy?

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

Is the Kb Testing Strategy AI skill free?

Yes. It is published on GitHub by Community-Access 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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