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Planner Spec Expand

Community
Archive228
planner-spec-expand

Expand a 1-4 sentence product brief into a full spec with a design language, acceptance surface, and an ordered feature list.

Overview

PublisherArchive228
Repositoryloopkit
Skill nameplanner-spec-expand
Stars
755
Forks
124
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 Archive228 on GitHub. Read the source before you install it.

Installation

Install the Planner Spec Expand 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/Archive228/loopkit/tree/main/skills/planner-spec-expand
  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/Archive228/loopkit.git /tmp/loopkit
mkdir -p .claude/skills
cp -r /tmp/loopkit/skills/planner-spec-expand .claude/skills/planner-spec-expand
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Planner Spec Expand 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 Planner Spec Expand 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 Planner Spec Expand 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.

Planner Spec Expand

A one-line brief ("build a claude.ai clone", "a habit tracker with streaks") is not enough surface for a coding agent to make good decisions from. It has no design language, no acceptance criteria, no ordering. The generator ends up inventing scope mid-session and shipping incoherent slices.

The planner's job is to expand the brief into a spec dense enough that every downstream decision — what feature to pick, what "done" looks like, what the button should say — has an answer already written down. See Prithvi's post on planner/generator/evaluator separation for the underlying architecture: https://www.prithvirajrk.com/blog/three-agent-harness.

When to apply

  • The user handed you a brief under ~200 words and expects a real product.
  • You're about to seed [[feature-list-json]] and the brief has no orderable features yet.
  • Scope drifted mid-project and the original spec no longer describes the target — rewrite, don't patch.

Procedure — expand in this order

Do the sections in order. Later sections depend on earlier ones being locked.

  1. Restate the brief in one paragraph. In your own words, what is being built and for whom. If you can't write this cleanly, ask the user before continuing — the brief is under-specified.
  2. Pick a design language. One sentence each on: visual tone (minimal / dense / playful), typography stance (one sans / serif+sans / mono accents), color posture (monochrome + one accent / two-color / full palette), density (airy / compact). This locks a thousand later micro-decisions.
  3. Enumerate the acceptance surface. For each user-observable capability, write one sentence of user-observable behavior AND the concrete steps a human would take to verify it. This is the shape [[feature-list-json]] wants — write it in that shape now.
  4. Order the features. Sort by dependency: nothing appears before what it depends on. Ties broken by "what does the user see first when they open the app." The top of the list must be runnable-alone.
  5. Name the out-of-scope. One short list of things the brief could imply but you are explicitly not building. Prevents the generator from wandering.
  6. Write the smoke path. The single user journey that proves the product exists — 3-6 steps end-to-end. This becomes the initializer's smoke test.

Checklist before you hand off

  • Every feature has description + steps in the shape [[feature-list-json]] expects.
  • The first 3 features can be built in order with no forward dependency.
  • Design language fits on one screen — if it's a page, you over-specified.
  • Out-of-scope list is non-empty. If everything is in scope, you didn't plan, you transcribed.
  • Smoke path touches the core value prop, not auth or settings.

Anti-patterns

  • Padding the feature list to look thorough. 40 real features beats 200 fake ones. The generator will build all of them.
  • Designing the schema. That's the generator's job. You describe user-observable behavior; the generator picks the data model.
  • Writing prose where you should write steps. "User can manage conversations" is not a feature. "Clicking the trash icon on a sidebar item deletes that conversation and removes it from the sidebar" is.
  • Leaving priority implicit. If two features tie, break the tie now. The generator will not.

When NOT to apply

Skip this for briefs already specified to acceptance-criteria depth, or for single-feature edits to an existing project — use [[shift-notes]] and pick from the existing [[feature-list-json]] instead.

Related: [[feature-list-json]], [[shift-notes]], [[broken-window-check]].

Frequently asked questions

What does the Planner Spec Expand AI skill do?

Expand a 1-4 sentence product brief into a full spec with a design language, acceptance surface, and an ordered feature list.

Why use Planner Spec Expand on TypingMind?

Because you install it once and use it with any model. Planner Spec Expand 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 Planner Spec Expand in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Archive228/loopkit/tree/main/skills/planner-spec-expand. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Planner Spec Expand?

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 Planner Spec Expand?

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

Is the Planner Spec Expand AI skill free?

Yes. It is published on GitHub by Archive228 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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