Verify Understanding logo

Verify Understanding

Organization
eai-org
verify-understanding

Verify the engineer is ready to implement a feature through a teach-back conversation over its ticket review.

Overview

Publishereai-org
Repositoryagent-toolkit
Skill nameverify-understanding
Stars
80
Forks
18
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 eai-org on GitHub. Read the source before you install it.

Installation

Install the Verify Understanding 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/eai-org/agent-toolkit/tree/main/skills/verify-understanding
  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/eai-org/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/verify-understanding .claude/skills/verify-understanding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify Understanding 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 Verify Understanding 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 Verify Understanding 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.

Verify understanding

One goal, nothing more, nothing less: verify the engineer is ready to move to the implementation phase. Exercise their understanding of a feature before they work on it — the pipeline's other artifacts serve fresh agent sessions; this step serves the human. Active recall: the user produces the feature in their own words, you only steer and fix. Re-runnable: right after the ticket review, or days later just before implementation — every run starts fresh from the review file, no state kept between runs.

Input

A .TICKET-REVIEW.md. Read it and the ticket files and parent it links. A reference that exists only as a tracker URL (no local file) may be read from the tracker at conversation time. A ticket with a newer -vN fetch beside the one the review read has likely overtaken it: say so, read the newest ticket files, and treat the review's open questions as possibly answered there. Then look for a .TICKET-REVIEW.md under the newest ticket file's base, flat beside it or inside its -vN/ directory: one that exists and is not the input → say so and run the conversation from it, the given review at most context; none → the overtaken handling stands. The .REQUIREMENTS.md of the newest ticket file's base (none: the ticket's latest across -vN versions) is read for its Overrides, which win over the ticket text. Given a ticket file and no review, never improvise one: say a review is needed and stop, handing off /review-ticket <ticket-file> as a launch command named review-<slug> (format at the close) when review-ticket is available.

The conversation

A conversation, not a test — the last sync between a dev and their PO before development starts. Word your probes and corrections via explain-in-simple-language when available.

  1. Ask the user to explain the feature in their own words: who uses it, what they see and do, why the feature exists, and what each ticket contributes.
  2. Let them produce the narrative. Probe one weak spot at a time; correct and complete only from the review file and the referenced materials — flag what those don't settle, never improvise requirements.
  3. Stay at that PO–dev depth: journey, actors, purpose, per-ticket contribution — not DTO, field, or architecture detail.

When the product's user-facing language differs from the conversation's, name each page, text, or element with its user-facing term in parentheses — "the client area (klantportaal)".

Done when the user has correctly produced — corrections absorbed — the journey, why the feature exists, and every input ticket's contribution: enough to start implementing. Close with a short wrap-up naming anything that stayed shaky, then, only when refine-ticket is available, hand off refining each ticket, in the set's suggested execution order, as its own fenced block under a label naming the ticket, holding one copy-pasteable launch command in the syntax of the agent tool in use (claude is only the example): claude --name refine-<slug> "/refine-ticket <ticket-file>"; then the alternative: one line offering the tool's clear command (/clear is only the example), then the same labelled blocks with /refine-ticket <ticket-file>.

Boundaries

  • The conversation is the product: write no files.
  • Read-only on the tracker.

Frequently asked questions

What does the Verify Understanding AI skill do?

Verify the engineer is ready to implement a feature through a teach-back conversation over its ticket review.

Why use Verify Understanding on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/eai-org/agent-toolkit/tree/main/skills/verify-understanding. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verify Understanding?

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 Verify Understanding?

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

Is the Verify Understanding AI skill free?

Yes. It is published on GitHub by eai-org 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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