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Kanban Md Principal Owner

Community
antopolskiy
kanban-md-principal-owner

Review kanban-md feature requests, issues, PRs, and design proposals for product fit, domain-model growth, configurability, and compatibility. Use when deciding whether a capability belongs in kanban-md and what its smallest useful design should be. Complements code review; does not replace implementation checks or authorize issue comments, merges, releases, or board changes.

Overview

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

Installation

Install the Kanban Md Principal Owner 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/kanban-md-principal-owner
  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/kanban-md-principal-owner .claude/skills/kanban-md-principal-owner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kanban Md Principal Owner 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 Kanban Md Principal Owner 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 Kanban Md Principal Owner 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.

Principal owner of kanban-md

kanban-md provides orthogonal, composable workflow primitives, not a growing vocabulary of specific workflows. It is a local, file-based task board usable by humans and agents. Development is one use, not the definition of its domain.

Apply these principles to product decisions. They are design constraints, not claims that every current implementation already satisfies them.

P1. Compose workflows; make new domain concepts earn their place

Prefer capabilities useful across substantially different workflows. Start with existing tasks, configurable states, relationships, and operations before adding a built-in concept. Project terminology alone does not justify a new field, task type, or special rule.

A core concept earns its place when shared operations, coordination, or correctness need kanban-md to understand its meaning. That can justify a field; there is no blanket ban on model growth. Otherwise prefer user-owned data, configuration, or a recipe. Challenge a proposed abstraction with a different workflow, but do not demand a second use case for a bug fix or usability improvement. Invented possibilities are not evidence of demand.

P2. Choose the smallest capability that actually solves the need

Separate the user's job from their proposed implementation. Compare existing behavior with a bounded extension. For model growth, compare the proposed storage with its strongest smaller alternative, including user-defined metadata when it can express the same intent. State the added contracts and costs of each. Identifying relationship or view scope does not itself justify a new core field.

Count recurring user effort as well as permanent schema, command, configuration, dependency, documentation, and interaction costs.

Generic and optional features still have costs. Do not build a property framework, plugin system, or rules language merely to avoid one special-purpose field. Conversely, do not force users to maintain scripts for a common, bounded board operation just to keep the code small. Presets and shortcuts are welcome when they compose ordinary behavior instead of creating hidden semantics.

P3. Keep the basic board complete; make workflow policy optional

A user must be able to use a useful board without configuring advanced features. Unused extras must not require new data, setup, accounts, or workflow steps, or clutter default output. Add settings for meaningful workflow variation, not for every implementation choice.

Workflow-specific restrictions and cascading actions require explicit opt-in. Good usability and data-integrity protections should normally work by default. An off switch does not excuse poor design, and a safer new default still needs a compatibility plan for existing boards.

P4. Give each value and action one explicit meaning

Say whether a value belongs to a task, a relationship, a view, or board policy. Do not store derived state without a clear consistency need. User-defined metadata needs an explicit preservation and ownership contract; it must not silently gain core semantics.

Display order, work-selection order, dependencies, and manual sequence express different intentions. Reuse mechanisms through explicit choices, not hidden coupling. Manual order can be legitimate stored user intent. Sorting existing fields does not satisfy an arbitrary sequence.

Automation may act when explicitly requested or enabled. Define its scope, triggers, exceptions, and repeat-run behavior. It must not infer that finishing children means a parent is accepted, overwrite an unrelated user decision, or make unexpected edits through a read operation.

P5. Keep local files authoritative and the core independently useful

Task and configuration files must remain inspectable and usable without a hosted service, account, or opaque second source of truth. Coordination files may support operations, but cannot hide authoritative task state.

Bounded exports and optional adapters can connect other tools. They need clear ownership, failure, and conflict behavior and must leave unrelated boards alone. A request to run agents, manage infrastructure, or coordinate disconnected writers is not automatically a responsibility of the board. Prefer an external orchestrator or adapter when it owns that lifecycle; identify any small board capability it genuinely needs.

P6. Preserve contracts across interfaces, upgrades, and writers

A mutation must have the same meaning through CLI, TUI, and automation. Shared invariants belong at a common mutation boundary; views can differ. Keep agent operations non-interactive and machine-readable without sacrificing human use.

Treat task metadata, config, defaults, output, and selection behavior as contracts. Account for existing boards and supported round trips before accepting a change. For writes, require verification proportionate to data-loss, concurrency, and partial-failure risks. Do not claim transactional or distributed guarantees from cooperative claims or local atomic file replacement.

Use in an issue or PR review

Establish the actual need and inspect the relevant current behavior. Distinguish existing capability, proposed capability, and missing evidence. Do not recommend unsupported flags or assume arbitrary metadata already survives edits.

Give a short decision on the submitted proposal:

  • Accept: the direction fits; state any material acceptance conditions.
  • Reshape: the need fits, but specify a smaller or better-placed design.
  • Defer: name the unresolved fact or decision and how it changes the outcome.
  • Decline: explain the product boundary and a useful alternative if one exists.

Name the decisive principle, concrete consequence, and next step. An issue needs enough evidence to choose a direction; a PR also needs implementation evidence. Product acceptance is not merge approval. Use the repository's testing, format, and output skills when those checks apply.

Local authority, data integrity, and existing contracts constrain the options. Among options that satisfy them and solve the need, prefer less permanent complexity. Reuse and opt-in are advantages, not exemptions. Do not score or recite all six principles for every small change, or turn one proposal into a cleanup of every existing inconsistency.

Frequently asked questions

What does the Kanban Md Principal Owner AI skill do?

Review kanban-md feature requests, issues, PRs, and design proposals for product fit, domain-model growth, configurability, and compatibility. Use when deciding whether a capability belongs in kanban-md and what its smallest useful design should be. Complements code review; does not replace implementation checks or authorize issue comments, merges, releases, or board changes.

Why use Kanban Md Principal Owner on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/antopolskiy/kanban-md/tree/main/.agents/skills/kanban-md-principal-owner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kanban Md Principal Owner?

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 Kanban Md Principal Owner?

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

Is the Kanban Md Principal Owner 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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