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Buildable Planner

Community
suntay44
buildable-planner

Plan or classify a Buildable app idea into an archetype, ask-vs-build decision, phase plan, references, mock-data guidance, and local-first app spec. Use for Buildable product planning and ambiguous app-build requests; do not use for ordinary coding, debugging, or generic project planning.

Overview

Publishersuntay44
Repositorybuildable-plugin-skills
Skill namebuildable-planner
Stars
54
Forks
8
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 suntay44 on GitHub. Read the source before you install it.

Installation

Install the Buildable Planner 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/suntay44/buildable-plugin-skills/tree/main/skills/planner
  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/suntay44/buildable-plugin-skills.git /tmp/buildable-plugin-skills
mkdir -p .claude/skills
cp -r /tmp/buildable-plugin-skills/skills/planner .claude/skills/buildable-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Buildable Planner 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 Buildable Planner 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 Buildable Planner 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.

Buildable Planner Skill

Produce a local-first app spec and phase plan from the user's prompt, explicit reference files, and relevant existing-project context. Resolve bundled paths from the Buildable plugin or repository root.

CLI Workflow

  1. Run buildable plan "<prompt>" --compact. This retains the structured spec and phases while omitting the duplicate Markdown render; full .buildable/phase-plan.json, .md, and compact .toon files are still saved. Use --no-write only for terminal-only inspection. For MCP, use buildable_plan with its compact default.
  2. Pass explicit user files with --file <path>, --reference <path>, or --screenshot <path>; inspect appSpec.referenceInputs without pasting file contents into the prompt. For requested auth use --with-auth; record a named provider with --with-auth-provider <provider>. Keep auth local/mock behind a seam unless a provider is named.
  3. Use the returned classification, appSpec, and phasePlan as the planning result. Do not repeat classification or reopen registries/policies already applied by the CLI. Load only needed files from appSpec.references; load selected starter source only when generating or editing it. Never scan whole knowledge/template directories or unselected templates.
  4. Check appSpec.planAudit gates and ask blocking appSpec.questions when questionsNeeded is true before design or generation. Otherwise ask at most one or two promptRefinement.optionalQuestions only when the answer materially changes the plan; use defaults when the user wants to proceed.
  5. Summarize the plan once using the output guidance below. Keep implementation within the user's request; a planning-only request does not start app code. The included design system is sufficient to proceed; run buildable design only when a deeper UI/UX brief is useful.

CLI-Unavailable Fallback

Only when the CLI is unavailable, build the spec manually:

  • Match core/archetype-registry.json tags, then apply core/ask-vs-build-policy.md and core/activation-policy.md. Use knowledge/INDEX.md and templates/INDEX.md only for discovery.
  • Load the selected archetype, matching data-model/screen-graph files when present, and the best target-specific template spec. Preserve runnable versus plan-only status.
  • Select design guidance from core/design-system-registry.json and compatible micro-blocks from blocks/registry.json; include only selected references.
  • Follow core/app-spec-schema.md: include realistic mockData and state coverage, explicit referenceInputs, promptRefinement assumptions/questions/defaults, planAudit gates, and requested auth/persistence seams. Keep data local/mock and state non-goals explicitly.
  • Include clarify, plan, mock-data, design, build, and review phases with blockers and completion criteria tied to the selected screens, features, and acceptance criteria.

Output And Handoff

Give a short decision summary: app direction, target/stack, screens and key behavior, non-goals, design/mock-data approach, blockers or important assumptions, and the next actionable phase. Link the saved plan instead of repeating the full app spec, audit checks, reference list, and phase plan in chat; expand those details when requested. On revisions, retain the accepted direction and constraints, update the saved plan, and summarize what changed.

For planning-only requests, end with one satisfaction checkpoint: revise in Buildable Planner if needed, or continue with Buildable Web Builder/Mobile Builder for the selected target. If the user already asked to build and there are no blockers, continue to that builder without another approval checkpoint.

The builder should read .buildable/phase-plan.toon first for compact context, then retrieve omitted details from .buildable/phase-plan.json as needed (for example, full design rules or expected files). TOON is a summary; JSON remains the source of truth. Reuse the saved plan rather than re-planning unchanged requirements, and load only selected appSpec.references, explicit user reference inputs, and necessary starter/project source.

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 Buildable Planner AI skill do?

Plan or classify a Buildable app idea into an archetype, ask-vs-build decision, phase plan, references, mock-data guidance, and local-first app spec. Use for Buildable product planning and ambiguous app-build requests; do not use for ordinary coding, debugging, or generic project planning.

Why use Buildable Planner on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/suntay44/buildable-plugin-skills/tree/main/skills/planner. 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 Buildable Planner?

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 Buildable Planner?

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

Is the Buildable Planner AI skill free?

Yes. It is published on GitHub by suntay44 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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