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Qlty

Organization
bastani-inc
qlty

Code quality checks, formatting, and metrics via qlty CLI. Use when asked to verify code quality, add or run verifiers, lint or format code, measure complexity, or find code smells. Includes environment-aware setup and manual offline configuration when qlty is missing or installation is restricted.

Overview

Publisherbastani-inc
Repositoryatomic
Skill nameqlty
Stars
856
Forks
115
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bastani-inc on GitHub. Read the source before you install it.

Installation

Install the Qlty 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/bastani-inc/atomic/tree/main/packages/subagents/skills/qlty
  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/bastani-inc/atomic.git /tmp/atomic
mkdir -p .claude/skills
cp -r /tmp/atomic/packages/subagents/skills/qlty .claude/skills/qlty
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qlty 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 Qlty 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 Qlty 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.

Qlty code quality

Use qlty to coordinate relevant linters, formatters and built-in analysis. Repository checks in AGENTS.md, package scripts and CI remain authoritative. Prefer the project's tools and user priorities over generic language presets: a TypeScript repository using Biome does not need a competing ESLint/Prettier rollout.

Choose useful checks

Inspect the task, languages, existing config, scripts, lockfiles and baseline findings. Select reliable, low-noise checks that address the requested risks. Security plugins, linter extensions and metrics are choices, not a checklist to maximize. Record commands, scope, output and a relevant baseline; fix in-scope findings rather than reporting complexity numbers without context.

Check environment and authorization

  1. Look for existing .qlty/qlty.toml, installed qlty --version, cached plugins and runtimes. Preserve existing configuration; do not run init over it.
  2. During authorized coding, missing configuration is not a blocker: run qlty init --no when available, inspect its generated suggestions and tailor them. Configuration is a reviewable change. Explicit read-only tasks remain read-only, or use isolated scratch configuration.
  3. Prefer installed/cached tools. Install missing tools/plugins when online access and permissions permit. A known offline or restricted environment is sufficient reason not to attempt prohibited downloads; otherwise make a reasonable capability check or one bounded setup attempt. Do not repeat blocked installs or require production credentials.
  4. If the binary is missing or init cannot run, read manual configuration and prepare repo-appropriate .qlty/qlty.toml by hand when configuration changes are in scope. This requires no network. Validate TOML and available schema support, and label configuration preparation separately from checks executed.
  5. Continue available repository checks when optional qlty installation or execution is unavailable. Report the constraint, attempted commands and observed output, checks actually run and coverage still missing. Optional qlty availability is not a universal completion gate and does not relax authoritative checks.

Installation when permitted

The documented macOS/Linux installer is curl https://qlty.sh | sh. Inspect installer and platform support before executing downloaded code. The default binary location is ~/.qlty/bin; check that location before reinstalling and add it to this shell's PATH if appropriate. QLTY_NO_MODIFY_PATH=1 prevents shell-profile edits; QLTY_INSTALL_BIN_PATH chooses another installation directory. Windows support varies by release; verify current support rather than assuming POSIX commands work there.

Online, start with https://docs.qlty.sh/llms.txt and fetch relevant command/plugin documentation. Offline, use bundled references instead of attempting the network. Never invent plugin versions or flags.

Run and report

Use explicit paths or a suitable upstream base to keep checks in scope. Commands default to changed files; --all widens coverage. check and fmt may install plugin runtimes on first use. A cached plugin can work offline, but uncached dependencies cannot. Installed built-in metrics and smells may work offline without plugin downloads; configuration source resolution can still need locally available definitions. A missing binary runs none of these checks.

bash
qlty check
qlty check --upstream origin/main
qlty metrics --functions path/to/file.ts
qlty smells path/to/file.ts

Choose the relevant subset. For authorized formatting/fixes, use scoped qlty fmt or qlty check --fix --level=low, inspect the diff and rerun checks. Do not use --all formatting by habit or rewrite files during read-only review. Avoid unsafe auto-fixes and broad churn. --level controls displayed severity; --fail-level controls failure severity. Do not suppress errors to manufacture a green result.

Report four distinct facts: configuration prepared/changed, checks passed with command/output, failed checks, and checks not run with reasons. A parsed TOML file is not lint, security, metrics or full schema evidence. A clean run with no applicable plugins is not full quality coverage.

Bundled references

These are dated source excerpts. Prefer current official documentation when reachable; preserve the environment and authorization rules above when an upstream example assumes unrestricted installation.

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 Qlty AI skill do?

Code quality checks, formatting, and metrics via qlty CLI. Use when asked to verify code quality, add or run verifiers, lint or format code, measure complexity, or find code smells. Includes environment-aware setup and manual offline configuration when qlty is missing or installation is restricted.

Why use Qlty on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bastani-inc/atomic/tree/main/packages/subagents/skills/qlty. 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 Qlty?

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 Qlty?

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

Is the Qlty AI skill free?

It is published on GitHub by bastani-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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