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Maestro Design

Community
ReinaMacCredy
maestro-design

Resolve material unknowns blocking the next authorized slice, using research, grilling, prototypes, models, or wayfinding. Record durable decisions and apply the shared workflow tier rule.

Overview

PublisherReinaMacCredy
Repositorymaestro
Skill namemaestro-design
Stars
232
Forks
23
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ReinaMacCredy on GitHub. Read the source before you install it.

Installation

Install the Maestro Design 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.
  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/ReinaMacCredy/maestro.git /tmp/maestro
mkdir -p .claude/skills
cp -r /tmp/maestro/src/plugins/skills/maestro-design .claude/skills/maestro-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maestro Design 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 Maestro Design 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 Maestro Design 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.

maestro-design

Use when a material choice blocks the next slice. Read Decisions and readiness and Authorization boundaries. Design is read-only toward production code and authorizes nothing.

Mode per unknown

Identify what kind of unknown blocks progress, then load only the reference that resolves it:

The unknownModeReference
Decisions only the user can make, several and interdependentgrillreferences/grilling.md
A decision owned by someone not in the conversationroute to maestro-questionnaire
A fact in docs, APIs, or source outside this reporesearchmaestro-explore (research mode)
"Does this state model feel right?" or "What should it look like?"prototypemaestro-explore (prototype mode)
Fuzzy terminology, or a hard-to-reverse choice worth recordingmodelreferences/domain-modeling.md
The effort exceeds one session and is wrapped in fog, or the user does not know what to do nextwayfindreferences/wayfinder.md

Facts are yours to find; material product and scope choices are the user's. Never ask the user for anything you could look up. Modes compose: grill runs with the glossary in hand; wayfind dispatches grill, research, and prototype per child work item. Each design pass must close at least one fork; a pass that closes none surfaces the blocker to the user instead of looping.

Intake

Pin the problem before choosing a method:

For [who], reach [observable outcome] within [boundary], because [impact], without [excluded effect].

Route uncertainty to a lane:

Current uncertaintyLane
state unknownscout, no-write
several architecturestwo or three decision lanes
contract cleardelivery
candidate needs breakingchallenge
hard-to-reverse forkcouncil

Weigh the ROI of independent judgment on five questions:

  1. Would wrong framing be costly?
  2. Is the decision hard to reverse?
  3. Is the domain new to the owner?
  4. Can independent judgment produce a materially different option?
  5. Is human attention fragmented?

Mostly no: the direct session. Mixed: a Lead plus one peer. Mostly yes: several lanes. Yes on every question including hard to reverse: a council. Before launching any lane, pin the branch and commit, active writers, and dirty paths.

Recall pass first

Before proposing anything, search the store for precedent:

maestro search "<topic keyword>"        # hits labeled work/decision/note/bundle
maestro bundle show <id>                # deep-read a bundle hit
maestro decision list                   # locked choices that bind this scope

A past bundle that settled the same fork is evidence; cite it instead of re-deriving the argument.

Working method

  • Read the current maestro work show, linked decisions, notes, and source.
  • Present a blocking user-owned fork with a concrete recommendation; resolve reversible implementation details within the approved scope directly.
  • Record durable decisions under WORKFLOW.md's threshold: maestro decision draft "<choice>" --rationale "<why, with the rejected alternative>" --work <id> then maestro decision lock <id>. Supersede an old decision with --supersedes; never rewrite its history.
  • Keep acceptance, non-goals, and authority visible on the work item.
  • Do not edit code during a design-only engagement.

Council

A hard-to-reverse fork with wide blast radius runs the maestro-council protocol: neutral brief, sealed seats, one premise verifier on unanimity, bounded verifiers, one cross-examination round, an audit by tier, and one binding verdict recorded with maestro decision draft --rationale carrying the dissent. The candidate under review stays frozen; a new finding creates a new candidate.

Readiness gate and exit

Check readiness of the next bounded slice, not the number of open questions about the whole project. Keep later questions visible without blocking an independent slice. If the problem itself is unclear, wayfind. Forks already settled are synthesized, never re-asked. An external claim entering a decision (API behavior, library semantics, versions) comes from research against primary sources, never from memory.

Then exit by Tiers:

  • Light: design ends with a work item with clear acceptance, maestro work add "<title>" --acceptance "<observable result>" --kind <kind>, plus any durable decisions. Kind routes the policies: feature, task, bug, chore, implement are execution units; idea and research are scope notes under a parent and never hold it open. The why lives in the title or acceptance; when it needs a paragraph, add maestro work note <id> "why: <paragraph>", and record findings from the research mode as research: <finding> notes (what policy-research reads when enabled). No SPEC is required; the work is verified inline by maestro-work. A quickfix never reaches design.
  • Full: maestro bundle open <id> --work <workId>, opened in the store whose checkout will change (a walk run in the Hub room still opens its bundle where the code lives; note the bundle on the Hub map and the map on the bundle's work item), then fill SPEC.md as a pure contract: Problem, Solution, Scope, Anti-goals (each traces to a real risk in this repo and gets a matching VERIFY.md check; an anti-goal that cannot be checked is a wish, not a constraint), Decisions (ids only, Hub decisions as hub:<id>; maestro bundle show <id> renders them). Plan checks using Testing discipline, including existing checks and necessary new tests, not a test quota. Draft VERIFY.md from acceptance, relevant risks, and anti-goals; seed NOTES.md with Current State, Next Action, original authorization, and Base:.

If the next slice's acceptance or authority needs guessing, resolve that blocker. Otherwise continue implementation when the user's original request already authorizes it; do not ask again merely because design is complete. For a design-only request, finish with the proposed scope and implementation gate. A SPEC authorizes nothing.

For unattended/away-mode design constraints, read references/unattended.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 Maestro Design AI skill do?

Resolve material unknowns blocking the next authorized slice, using research, grilling, prototypes, models, or wayfinding. Record durable decisions and apply the shared workflow tier rule.

Why use Maestro Design on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ReinaMacCredy/maestro/tree/main/src/plugins/skills/maestro-design. 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 Maestro Design?

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 Maestro Design?

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

Is the Maestro Design AI skill free?

Yes. It is published on GitHub by ReinaMacCredy 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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