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Test Kanban Md

Community
antopolskiy
test-kanban-md

Plan and run verification for kanban-md changes, including test-driven bug fixes, Go package tests, CLI end-to-end tests, TUI behavior and snapshots, lint, and precommit checks. Use when implementing or reviewing a change, fixing a bug, adding tests, updating snapshots, or deciding what evidence is required. Do not use as the source of release or schema-migration policy.

Overview

Publisherantopolskiy
Repositorykanban-md
Skill nametest-kanban-md
Stars
226
Forks
34
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 antopolskiy on GitHub. Read the source before you install it.

Installation

Install the Test Kanban Md 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/antopolskiy/kanban-md/tree/main/.agents/skills/test-kanban-md
  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/antopolskiy/kanban-md.git /tmp/kanban-md
mkdir -p .claude/skills
cp -r /tmp/kanban-md/.agents/skills/test-kanban-md .claude/skills/test-kanban-md
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Test Kanban Md 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 Test Kanban Md 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 Test Kanban Md 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.

Test kanban-md

Choose the smallest fast feedback loop while developing, then widen verification in proportion to the change before declaring it complete.

Required discipline

  • Fix bugs with test-driven development: first reproduce the exact failure in a test and confirm it fails for the expected reason, then implement the fix.
  • Match the test layer to the behavior. Prefer package tests for internal logic, command tests for CLI wiring, E2E tests for complete binary workflows, and TUI behavioral or snapshot tests for rendered interaction.
  • Run focused tests during implementation, the affected package suite after the change, and the full suite before completing production-code work.
  • Run lint after Go changes and resolve findings introduced by the change.
  • Update golden files only after confirming the new rendering is intended, then inspect the resulting diff rather than accepting it blindly.
  • Report the exact commands run and distinguish passing checks, known baseline failures, and checks that were not run.

Bug-fix loop

  1. Identify the affected package or interface and study nearby test patterns.
  2. Add the narrowest test that reproduces the reported behavior.
  3. Run that test and confirm the pre-fix failure is meaningful.
  4. Implement the smallest sufficient fix.
  5. Run the reproducer again and confirm it passes.
  6. Run the affected package or E2E suite.
  7. Run the full repository tests and lint.
  8. Review snapshots, generated files, and the final diff for unintended changes.

Do not weaken an assertion merely to make a failing test pass. If the expected behavior is unclear, resolve that product decision before encoding it.

Common verification commands

bash
# One test or package during development
go test ./path/to/package -run TestName -count=1
go test ./path/to/package -count=1

# Repository-wide checks
go test ./...
golangci-lint run ./...

# Full branch validation used by the repository
make precommit

Use make precommit before integration when the change warrants the full build, race-enabled coverage, E2E, lint, and clean-worktree checks. If it fails because of a pre-existing baseline, verify the current diff separately and report the baseline precisely; do not silently present a partial check as a full pass.

For package locations, helpers, and TUI snapshot commands, read references/test-patterns.md.

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 Test Kanban Md AI skill do?

Plan and run verification for kanban-md changes, including test-driven bug fixes, Go package tests, CLI end-to-end tests, TUI behavior and snapshots, lint, and precommit checks. Use when implementing or reviewing a change, fixing a bug, adding tests, updating snapshots, or deciding what evidence is required. Do not use as the source of release or schema-migration policy.

Why use Test Kanban Md on TypingMind?

Because you install it once and use it with any model. Test Kanban Md 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 Test Kanban Md in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/antopolskiy/kanban-md/tree/main/.agents/skills/test-kanban-md. 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 Test Kanban Md?

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 Test Kanban Md?

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

Is the Test Kanban Md AI skill free?

Yes. It is published on GitHub by antopolskiy 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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