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Handoff

Community
Owl-Listener
handoff

Generate an accessibility decision handoff for engineering. Chains: decision-documentation, compliance-mapping, accessibility-testing-strategy. Use when a design is ready for implementation and the engineering team needs clear accessibility specifications.

Overview

PublisherOwl-Listener
Repositoryinclusive-design-skills
Skill namehandoff
Stars
108
Forks
28
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 Owl-Listener on GitHub. Read the source before you install it.

Installation

Install the Handoff 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/Owl-Listener/inclusive-design-skills/tree/main/accessibility-decisions/skills/handoff
  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/Owl-Listener/inclusive-design-skills.git /tmp/inclusive-design-skills
mkdir -p .claude/skills
cp -r /tmp/inclusive-design-skills/accessibility-decisions/skills/handoff .claude/skills/handoff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Handoff 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 Handoff 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 Handoff 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.

Accessibility Decision Handoff

Package accessibility decisions into a clear specification that engineers can implement against — because "make it accessible" is not a specification.

Process

Step 1: Compile Decisions

Using decision-documentation, gather all accessibility decisions for the feature:

  • Interaction patterns and keyboard behaviour
  • Screen reader announcements and ARIA requirements
  • Visual specifications (contrast, colour independence, motion)
  • Content requirements (reading level, alt text, link text)
  • Adaptive behaviour (preferences, zoom, density)

Step 2: Map to Implementation

Using compliance-mapping, translate each design decision into specific implementation requirements:

For each interactive element specify:

  • HTML element or ARIA role
  • Keyboard behaviour (which keys do what)
  • Focus management (where focus goes after actions)
  • Screen reader name, role, and state announcements
  • Visual states (default, hover, focus, active, disabled, error)

For each content element specify:

  • Heading level
  • Alt text (exact text or strategy)
  • Link text
  • Language attributes if multilingual

For each adaptive behaviour specify:

  • Media query and expected response
  • Preference detected and what changes
  • Breakpoints and reflow behaviour

Step 3: Define Test Criteria

Using accessibility-testing-strategy, provide specific test cases for each requirement:

  • Keyboard test: "Tab to the submit button, press Enter, verify focus moves to the confirmation message"
  • Screen reader test: "Navigate to the error, verify it announces: 'Error: email address is required'"
  • Visual test: "Verify contrast ratio of body text is at least 4.5:1 in both light and dark modes"
  • Zoom test: "At 200% zoom, verify the form reflows to single column with no horizontal scroll"

Step 4: Flag Risks and Dependencies

Note anything engineers need to be aware of:

  • Components that need custom keyboard handling
  • Places where framework defaults aren't accessible
  • Third-party components that may need wrapping or replacing
  • Areas where automated testing won't catch the issue

Output

Deliver a handoff document containing:

  1. Accessibility specification — element-by-element requirements with HTML, ARIA, keyboard, and visual specifications
  2. Test cases — specific, executable test scenarios for keyboard, screen reader, visual, and zoom testing
  3. WCAG mapping — which success criteria each requirement addresses
  4. Risk register — known challenges and recommended approaches
  5. Definition of done — the specific accessibility criteria that must pass before the feature ships

Frequently asked questions

What does the Handoff AI skill do?

Generate an accessibility decision handoff for engineering. Chains: decision-documentation, compliance-mapping, accessibility-testing-strategy. Use when a design is ready for implementation and the engineering team needs clear accessibility specifications.

Why use Handoff on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Owl-Listener/inclusive-design-skills/tree/main/accessibility-decisions/skills/handoff. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Handoff?

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 Handoff?

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

Is the Handoff AI skill free?

Yes. It is published on GitHub by Owl-Listener 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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