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Plan

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owainlewis
plan

Turns a reviewed spec or decided brief into ordered tasks for separate agent runs. Use for implementation tasks, tracker tickets, or useful milestones. Do not use for one coding task or its short execution outline.

Overview

Publisherowainlewis
Repositoryblueprint
Skill nameplan
Stars
419
Forks
73
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 owainlewis on GitHub. Read the source before you install it.

Installation

Install the Plan 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/owainlewis/blueprint/tree/main/skills/plan
  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/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/skills/plan .claude/skills/plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan 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 Plan 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 Plan 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.

Plan

Create tasks that a new agent can finish without making product or technical decisions. Every task and milestone must be a vertical slice: a usable outcome that runs through the required layers and has acceptance checks. Split by working result, not by file, layer, or team boundary.

Process

  1. Read the source, repository instructions, and relevant code.
  2. Stop and return to /spec if an unresolved choice would change behavior, interfaces, data, security, scale, performance, compatibility, operations, cost, or proof.
  3. Split the work into tasks that each deliver working behavior, fit one agent run, and produce one focused pull request.
  4. Keep shared contracts in one task. Do not make two tasks answer the same question independently.
  5. Separate refactoring when it would hide a behavior change.
  6. Order tasks by dependency. Group tasks into milestones when useful. Each milestone delivers a usable end-to-end result, with acceptance checks and dependencies. Do not use milestones such as “database”, “backend”, or “frontend”.
  7. Return the plan in chat. Create tracker tickets only when the user asks. Never write a plan document.
  8. Stop after planning. Do not implement.

For example, “add a task and see it after a restart” includes CLI input, validation, storage, output, and tests in one task. A later milestone could deliver “manage unfinished tasks” through add, list, and complete slices. Name the milestone's observable result and how to prove the whole result works.

Write for two readers

Each task is read by a human and executed by an agent.

The first sections must let a human understand the task in under a minute. State the concrete problem, the result, and why it matters in everyday words. Do not use requirement IDs, implementation details, undefined project terms, or acronyms unfamiliar to the intended readers there.

Put task-specific decisions, interfaces, failure rules, and security constraints in Agent notes. Link the spec or decided source for shared architecture and full requirement definitions. Use a repository path or URL that will resolve from the published ticket, and pin the decided version when later edits could change the contract. If no durable source exists, include the required shared decisions in Agent notes. Do not copy the whole source into every ticket.

A task stands alone when its purpose, boundary, dependencies, non-negotiable decisions, and proof are clear. It does not need to repeat background that the linked source already explains.

Task shape

Use this compact shape. Omit optional sections that add no information.

markdown
## <Plain action and result>

### What are we building?
In one to three short sentences, say what is wrong or missing and what will work after this task.

### Why?
In one or two short sentences, explain the practical value to a user, operator, or developer.

### Done when
- Three to seven observable results.

### How to check
Exact commands and required manual checks.

### Agent notes
- Depends on: <task titles, or None>
- Source: <durable spec, brief, issue, or request path/URL, pinned when needed>
- Only the definitions, decisions, constraints, and failure behavior specific to this task.

### Out of scope
- Related work this task is likely to absorb by mistake.

Writing rules

  • Use a short title that names an action and result.
  • Use familiar words and specific verbs.
  • Define a necessary technical term where it first appears. Otherwise replace it with observable behavior.
  • Say sending the same event twice creates one reply, not prove idempotency.
  • Say the answer is supported by the cited document, not prove grounding.
  • Do not add user stories by default. Add one only when it clarifies product behavior that the first two sections do not.
  • Keep implementation mechanics out of the first two sections.
  • Keep task-specific implementation choices in Agent notes. Leave interchangeable local mechanics to the implementing agent.
  • Preserve applicable AC-n, REQ-n, and INV-n references in Done when or Agent notes without making a reader decode them to understand the task.
  • Aim for 250 to 500 words per ticket. Exceed 700 only when the extra text is required to prevent an unsafe or incompatible implementation. Otherwise split the task or link the shared source.
  • Do not repeat a fact in several sections.
  • Do not use slogans, metaphors, filler, or vague claims such as robust, seamless, comprehensive, and future-proof.

Boundaries

  • Do not split one working behavior into separate file or technical-layer tasks.
  • Do not create scaffolding or cleanup tasks without a checked outcome.
  • Do not hide unresolved decisions inside implementation tickets.
  • Keep each task small enough for one agent run and one focused review.
  • In Done when, state observable behavior and any internal rule that must hold.
  • In How to check, include exact commands and manual proof when automation cannot cover the behavior.
  • If the source uses requirement IDs, keep each ID attached to the same rule. Do not renumber or reuse it.

Before returning the plan, read each task twice:

  1. Human pass: can someone explain what will change and why after reading only the title and first two sections?
  2. Agent pass: can a fresh agent find the source, identify dependencies and fixed decisions, implement the task, and prove it without asking a product or architecture question?

Delete repeated background after both passes succeed.

Return

Return the ordered tasks, any useful milestones with acceptance checks, their dependencies, and any decision that still blocks implementation. Do not add a separate summary that repeats the tasks.

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 Plan AI skill do?

Turns a reviewed spec or decided brief into ordered tasks for separate agent runs. Use for implementation tasks, tracker tickets, or useful milestones. Do not use for one coding task or its short execution outline.

Why use Plan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/owainlewis/blueprint/tree/main/skills/plan. 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 Plan?

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 Plan?

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

Is the Plan AI skill free?

Yes. It is published on GitHub by owainlewis 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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