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Maestro Explore

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ReinaMacCredy
maestro-explore

Answer an evidence question without touching production code - research external facts against primary sources, build or iterate a disposable prototype (its own bugfixes included), or baseline current repository behavior before deciding or implementing. Use when the question is answerable by reading, building, or measuring with no user decision required; unsettled decisions belong to maestro-design. Prototypes are throwaway. Also triggers on Vietnamese phrasings such as làm 1 vòng research xem, research xem.

Overview

PublisherReinaMacCredy
Repositorymaestro
Skill namemaestro-explore
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 Explore 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-explore .claude/skills/maestro-explore
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maestro Explore 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 Explore 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 Explore 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-explore

Answer an open question with evidence, without touching production code. Read-only toward production paths; exploration never authorizes implementation. Any tier may use it. Apply WORKFLOW.md for tier selection, authority, and durable decision thresholds.

Three modes; pick whichever settles the question:

  • research - documented or external facts: read source, docs, or the web; record findings with links so claims stay traceable. Procedure: references/research.md.
  • prototype - an environment-specific behavior question that reading cannot settle: build the smallest throwaway that answers it, outside production paths. Prototypes are disposable; only a decision-encoding fragment (a schema, a reducer, a type shape) may be inlined into a locked decision or the bundle's SPEC.md. Fixing the prototype's own bugs is still this mode - a fix routes to maestro-work only when the target is production code; iterate directly, no re-routing pass per request. When the user approves porting, the port is production work: route it by tier (maestro-bundle tier rule); the prototype never merges as-is. "Ship it as-is" waives the rewrite, not the route. A prototype that is deployed or relied on in real work is no longer throwaway: say so, and route its next change by tier. Procedure: references/prototype.md.
  • baseline - current repository behavior that must be preserved or changed: capture it as runnable commands with observed output, so maestro-verify can compare later. Phrase each behavior the work must preserve as a guard, "the system SHALL CONTINUE TO ", so preserved behavior is a checkable claim, not an assumption.

Where findings land

  • A fact is evidence, not automatically a decision. If it supports a durable choice under the shared workflow, record that choice: maestro decision draft "<choice>" --rationale "<why, with source link>" --work <id>, then maestro decision lock <id>.
  • Working evidence for an open work item: maestro work note <id> "<finding + link>"; in a bundle, also the NOTES.md Current State.
  • A baseline the work must preserve: a VERIFY.md scenario in the bundle, or the work item's acceptance when there is no bundle.
  • No work item and no bundle: deliver the findings in the conversation. Open a work item only if the question feeds work that will actually happen; a finding alone never opens a bundle.

maestro search "<topic>" before any research: a past decision or note may already hold the answer.

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 Explore AI skill do?

Answer an evidence question without touching production code - research external facts against primary sources, build or iterate a disposable prototype (its own bugfixes included), or baseline current repository behavior before deciding or implementing. Use when the question is answerable by reading, building, or measuring with no user decision required; unsettled decisions belong to maestro-design. Prototypes are throwaway. Also triggers on Vietnamese phrasings such as làm 1 vòng research xem, research xem.

Why use Maestro Explore on TypingMind?

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

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

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 Explore?

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

Is the Maestro Explore 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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