Quick Verify

Organization

greenfield-inc

quick-verify

Use while working to confirm a change does what you meant before moving on. Observe its real effect the way a user or caller would, compare that with your intent, fix what is off, and observe again.

Overview

Publisher	greenfield-inc
Repository	`Pane`
Skill name	`quick-verify`
Stars	529
Forks	227
Bundled files	1
Links	Skill source Repository Homepage

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 greenfield-inc on GitHub. Read the source before you install it.

Installation

Install the Quick Verify AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/greenfield-inc/Pane/tree/main/main/src/services/paneChatBundle/skills/quick-verify
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/greenfield-inc/Pane/tree/main/main/src/services/paneChatBundle/skills/quick-verify

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/greenfield-inc/Pane.git /tmp/Pane
mkdir -p .claude/skills
cp -r /tmp/Pane/main/src/services/paneChatBundle/skills/quick-verify .claude/skills/quick-verify

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Quick Verify 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 Quick Verify 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 Quick Verify 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.

Quick verify

Code that compiles, and tests that pass, can still do the wrong thing. The only proof is the real effect, seen from where it matters. Run this loop yourself, between edits, in minutes. It is not a full QA pass; use qa-and-verify for that on a finished pull request.

The loop

Say what "working" looks like. One or two sentences, in terms someone could see: "the settings page shows the new toggle, off by default", "export --csv writes a header row". If you cannot say it, you are not ready to check it.
Pick the most faithful cheap way to observe it. Look from where the effect lands: the screen a user sees, the response a caller gets, the file or row that gets written, the log a job emits. Prefer the tool the repository already uses. Find how to run things in AGENTS.md, the README, or the package scripts, and never invent a launch command.
Produce the state. Run the app, command, or job with realistic input. Reuse what is already running; stop only what you start.
Capture evidence. Save it under tmp/verify/<change>/ with ordered names (01-before, 02-after) so each attempt sits next to the last. Capture the states that matter, not only the happy path.
Judge it critically. Compare it with step 1, then check around the change for what else it touched. Believe the evidence over your expectation.
Fix and observe again the same way, until the evidence shows what you meant.

Examples

Change	Observe	Look for
Web UI	Screenshot the dev server (Playwright, Cypress, or a temporary script)	Layout, spacing, empty and error states, a narrow viewport
Mobile UI	Simulator or device screenshot	The same, plus safe areas and keyboard overlap
API or backend	One real request, and read the response and the logs	Status, shape, edge input, the error path
CLI or script	Run it with real arguments	Output, exit code, files written
Job, queue, or migration	Run it once on test data, then read what changed	The rows or records it touched, and running it twice
Library function	Call it from a scratch script or one focused test	Return value at the edges
Docs or copy	Render it and read it as a newcomer would	Wrong commands, missing steps

Always

Never commit anything under tmp/verify/, and never point a check at production.
Report what you checked, how, and what you did not check.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

metadata/codex.yaml1 KB

Frequently asked questions

What does the Quick Verify AI skill do?

Use while working to confirm a change does what you meant before moving on. Observe its real effect the way a user or caller would, compare that with your intent, fix what is off, and observe again.

Why use Quick Verify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/greenfield-inc/Pane/tree/main/main/src/services/paneChatBundle/skills/quick-verify. 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 Quick Verify?

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 Quick Verify?

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

Is the Quick Verify AI skill free?

It is published on GitHub by greenfield-inc. Check the repository for licensing terms. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Quick verify

The loop

Examples

Always

Bundled files

Frequently asked questions

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