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Maestro Improve

Community
ReinaMacCredy
maestro-improve

Turn filed lessons into the smallest doctrine edit. Use when a Lead assigns improvement work for one target: group pending lessons, make one evidence-linked commit per target, return it for independent challenge, and process each accepted or rejected lesson without deleting history.

Overview

PublisherReinaMacCredy
Repositorymaestro
Skill namemaestro-improve
Stars
232
Forks
23
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 ReinaMacCredy on GitHub. Read the source before you install it.

Installation

Install the Maestro Improve 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-improve .claude/skills/maestro-improve
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maestro Improve 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 Improve 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 Improve 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-improve

Use when a Lead assigns improvement work on one target to a Peer. The target is the only parameter; a separate Peer challenges the returned candidate before acceptance.

A correction that stays in a transcript is spent when that session ends. The lesson record is what survives, and doctrine is the only thing a future session actually reads. This skill is the one place the two are joined: it turns filed corrections into the smallest edit that would have prevented them.

The parameter

The target names the doctrine a lesson corrects: a recipe section, the SLP Workspace Pack in src/plugins/resources/SLP.md, a Hub template, a skills/maestro-* file, or a repository's Workspace Protocol. Where each one lives, and what an edit to it costs, is in references/targets.md.

The loop

sh
maestro lesson list --project <project>     # pending only, by design
maestro lesson show <id>                    # the gap, the expectation, the why
  • Group the pending lessons by target. Two lessons on one rule are one edit, not two, and the second one is usually what tells you which reading of the rule was ambiguous.
  • Per group, propose the smallest edit that would have prevented what happened. Doctrine is read under load, so a sentence that removes an ambiguity beats a paragraph that adds a procedure. If the existing text already says it, the lesson is a rejection, not an edit.
  • One commit per target group, on a branch, with the evidence ids of every lesson in the group in the message. The ids are how a later reader gets from the rule back to the incident that shaped it.
  • Mark each lesson processed by pointing at that commit:
sh
maestro lesson process <id> --commit <sha>

The improver never deletes a lesson and never edits one. Processing is a pointer, so the record of what was corrected stays readable after the doctrine it corrected has changed again.

Rejecting a lesson

A lesson can be wrong: the rule already covers it, the correction misread it, or two lessons disagree and only one survives. Answer it where it lives and mark it processed in the same command:

sh
maestro lesson process <id> --answer "<why this produced no edit>"

The answer is for whoever filed it. "Out of scope" is not an answer; name the text that already covers it, or the reading that was wrong.

The replay gate

Doctrine has golden scenarios: a script of maestro commands and the transcript it produced. In this repository they are tests/scenarios/<name>.script and <name>.golden, replayed by bun test tests/scenario-golden.test.ts.

Run the replay after every edit. An edit is accepted only when it still matches the golden set, or matches the change a lesson explicitly expected. A replay that drifts in a way no lesson asked for is a regression in the doctrine, not an improvement, and it goes back before the work return. When a lesson did ask for the change, re-record with MAESTRO_GOLDEN_UPDATE=1 bun test tests/scenario-golden.test.ts and put the new golden in the same commit, so the diff shows the behaviour that changed next to the sentence that changed it.

A doctrine edit no scenario covers is an edit nothing can falsify: add the scenario in the same commit rather than leaving the rule unwatched.

Filing new lessons

Reading a pile of corrections is the best moment to notice one nobody filed. File it (maestro lesson file) rather than folding it silently into an edit: the next improver run needs the same evidence trail this one had.

Return

Return the bounded candidate through the normal SLP work operation:

sh
maestro work return <work-id> "candidate: <branch and commits>; lessons: <ids and stores>; proof: <replay>; residual risk: independent challenge pending"

Name every lesson processed, answered, or left pending in the return body. A lesson in another store is processed by that store's holder after acceptance; do not mutate it from the wrong project. The Lead assigns a separate challenge Peer, reconciles the two returns, and accepts only after the challenge and replay support the candidate. Improving doctrine and approving it alone is the loop this separation exists to break.

Red flags

The thoughtThe reality
"While I am in this file I will also tidy..."Every changed line traces to a lesson, or the challenge Peer cannot tell an edit from an opinion.
"These five lessons all point at the same mess; I will rewrite the section"A rewrite loses the reading that was ambiguous. Fix the ambiguity, keep the section.
"This lesson is wrong, I will just leave it pending"Pending means unread. Answer it and mark it processed, so it stops counting toward the next threshold.
"The scenarios changed because my edit is better"An unrequested replay drift is a regression until a lesson says otherwise.
"I will land the branch since it is obviously right"The challenge Peer and reviewer boundary are the point; a self-approved doctrine edit is one model marking its own work.

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 Improve AI skill do?

Turn filed lessons into the smallest doctrine edit. Use when a Lead assigns improvement work for one target: group pending lessons, make one evidence-linked commit per target, return it for independent challenge, and process each accepted or rejected lesson without deleting history.

Why use Maestro Improve on TypingMind?

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

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

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 Improve?

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

Is the Maestro Improve 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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