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Maestro Work

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ReinaMacCredy
maestro-work

Implement or fix one authorized unit with minimal edits and sufficient evidence. Reuse existing checks and add tests only for concrete uncovered behavior or risk.

Overview

PublisherReinaMacCredy
Repositorymaestro
Skill namemaestro-work
Stars
232
Forks
23
Bundled files
3
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.

  • 3 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 Work 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-work .claude/skills/maestro-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maestro Work 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 Work 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 Work 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-work

Use for one accepted implementation unit. Keep the change inside the work item's acceptance and authority. Apply WORKFLOW.md for method rules; pause only the slice blocked by scope or authority.

Recon and preconditions

Inspect the relevant source and existing checks, then apply Tiers. Confirm the original implementation request and accepted scope; for Full, read the matching SPEC. A newly found in-scope test gap does not require a new design pass. A throwaway prototype not approved to port remains maestro-explore's scope.

Before writing code, read any language-convention notes the user's setup provides for the language being edited. Repository conventions override them.

Dispatch

When work is handed to a lane (a Herdr pane in the room, or a sub-agent where no room exists), send this envelope:

text
Objective: <observable outcome>
Owned scope: <paths or responsibility>
Excluded scope: <explicit non-goals>
Mutation: <no-write | write-bounded: paths>
Stop condition: <done or blocked boundary>
Lane: scout | decision | delivery | challenge | shadow
Evidence required: <proof and layer>

A tiny task may collapse the envelope to three lines, but it never drops Excluded scope or Mutation.

  • scout reads and reports state, never writes.
  • delivery may write and is the only lane that holds the lease.
  • decision investigates, compares, and recommends without writing.
  • challenge breaks the premise or candidate and returns findings only, with no fixes or redesign.
  • shadow runs beside the owner without writing and returns comparison evidence that is never a candidate or a work write lease.

No-write names the file boundary only. maestro dispatch accept and maestro handback file are the lane's own two writes and are never in the excluded scope, so a scout or shadow lane can still accept and return.

The canonical parallel shapes are delivery and challenge on the same scope, or a council of decision lanes run by maestro-council.

Handback

Return this packet when the lane stops. maestro dispatch accept leaves the dispatch claimed, not held, and maestro handback file refuses with DISPATCH_UNCONFIRMED until the opener runs maestro dispatch confirm, so ask for the confirm at acceptance rather than at the stop condition.

text
Status: <DONE | BLOCKED | UNTESTABLE | UNKNOWN | FAILED | CHALLENGE | REOPEN_REQUEST | DEPENDENCY_REQUEST | COUNCIL_REQUEST>
Claim: <what is now believed true>
Proof: <evidence with its layer named>
Assumptions not verified: <items or None>
Residual risks: <items or None>
Incidental findings: <items or None>

Unknown is a valid result; it is never rounded up to PASS.

A peer that discovers a dependency stops the mutation that depends on the new assumption and hands back DEPENDENCY_REQUEST with evidence and impact. The Lead re-scopes the work. A never silently becomes A+B+C.

For repeated failures, apply Recovery and verification. Record the episode in one failed: work note, carrying:

text
Attempted: <approaches tried>
Invariant assumed: <belief shared by the attempts>
Exact failure: <literal evidence>
What changed between attempts: <delta>
What did not change: <stable conditions>
Smallest new information needed: <next fact that would change the approach>

Loop

  1. Perceive - maestro work show <id>, maestro ready, relevant source, tests, and repository instructions. Name the task-owned dirty paths before editing.
  2. Choose - the smallest behavior falsifiable at the accepted seam. Apply Testing discipline: identify existing evidence and the concrete gap before writing any test. New child work gets --acceptance "<observable result>" and --kind: feature, task, bug, chore, implement are execution units; idea and research are scope notes under a parent and never hold it open. A parentless item the agent creates carries its why in the title or acceptance; a longer why is a maestro work note <id> "why: <one paragraph>".
  3. Act - maestro work start <id>. With policy-breakdown enabled it refuses a parentless write-like item: pass --atomic-reason "<why this is one unit>" when it truly is one, otherwise maestro work add ... --parent <id> first and start the child - a parent with open children never starts. Then the minimum source and test edits for that behavior. Reach for what the repo already uses first: a helper, type, component, or installed dependency beats new code, and beats a native platform feature the repo has an established equivalent for. Minimum means the fewest concepts a maintainer meets at the seam, not the fewest lines; a wrapper that hides behavior to shorten a diff is a new concept, and the smallest change in the wrong layer is a second bug. A bug fix lands once where every caller routes through. Lazy about the solution, not about trust-boundary validation, error handling that prevents data loss, security, or anything explicitly requested.
  4. Observe - run the focused test, then type/lint/build checks. A suite that takes minutes runs in the background; its completion notification wakes you, so never hold the turn on sleep, osascript -e 'delay', or a poll loop against its log. Review the diff against acceptance; confirm the test could expose the defect.
  5. Learn - a pass that failed gets exactly one line, maestro work note <id> "failed: <one line>"; the lowercase failed: prefix is what maestro attention counts. Otherwise note only a reusable correction. Keep a checkpoint on the held item when state or the next action meaningfully changes, and before any handoff: maestro work note <id> "checkpoint:\nstate: <where it stands>\nnext: <concrete action>\navoid: <what not to repeat>". Include the base, task-owned dirty paths, original authorization and retained gates for a successor. Only the latest one counts; the brief prints it back after a compaction, and maestro handoff renders it into NOTES.md.
  6. Continue - maestro work done <id> with --claim/--proof naming the real falsifier (the check that would have failed if the claim were wrong). In a bundle, run maestro handoff <bundle-id> before releasing the work item: it renders NOTES.md from the store (work, decisions, handbacks, failed: and checkpoint: notes, base commit); hand-edit only Authority and whatever the store cannot derive, never the rendered sections.

Test technique

Use the shared Testing discipline for whether a test is needed. When writing one, prefer a stable consumer seam so internals stay free to change. Demonstrate that a plausible wrong implementation fails it, and derive expectations from the accepted behavior rather than current output.

Concrete smells and fixes: references/tdd-antipatterns.md.

Hard rules

  • Never delete, skip, or weaken a failing test to make the suite pass. A failing test is information: fix the code or surface the conflict.
  • A new test's failure must reflect the behavior gap, not an unrelated setup failure. Do not invent public behavior merely to get a test to compile.
  • For a material choice outside the acceptance, pause that slice and apply Decisions and readiness. Reversible internal details do not require a user question or a decision lock.
  • Scope the user cuts mid-loop leaves in the same turn: drop it from the red list and VERIFY.md, remove the tests and dead code written for it, and record the cut as a decision.
  • When the failure's cause is unknown, diagnosis (maestro-diagnose steps) is the first phase of this authorized fix, done here, not as a separate engagement.
  • A missing external fact (API behavior, library semantics, version differences) is not scope expansion: look it up against primary sources, record the finding and its link with maestro work note <id>, and continue. If the answer contradicts a locked decision, stop and supersede the decision first.
  • For a behavior-preserving change, compare existing checks or a captured baseline before and after. Apply Testing discipline if a coverage gap is discovered. Changing baselined behavior needs the appropriate scope approval; do not silently edit expectations to match a regression.
  • Generated or vendored files are never the target: fix the generator or pin and regenerate.

Red flags

The thoughtThe reality
"The test is basically right - I'll adjust the assertion to match the output"That documents the current bug as expected behavior. Assert from the decision's promise and fix the code.
"Another test would make this feel safer"Name the wrong implementation existing checks miss first; duplicate confirmation is not additional evidence.
"The test doesn't compile - I'll create the missing symbol so it can run"Check the accepted contract first; a test does not authorize new public behavior.
"While I'm here, this nearby code could use a cleanup"Not in the acceptance means not in scope. Mention it; do not touch it.
"Skipping this failing test unblocks the suite"A failing test is information. Fix the code or surface the conflict.
"All later design questions must be settled before I start"Only unresolved choices blocking the next authorized slice stop that slice.
"The tier requires another test"Tier determines record depth, not test count; use the shared Testing discipline.

When the scope is done and the checks are green: Light closes with maestro work done; a Full bundle routes to maestro-verify.

Coordination

Isolated lanes and worktrees: references/worktree.md. Contested files or overlapping sessions: references/conflict-handoff.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 Work AI skill do?

Implement or fix one authorized unit with minimal edits and sufficient evidence. Reuse existing checks and add tests only for concrete uncovered behavior or risk.

Why use Maestro Work on TypingMind?

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

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

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 Work?

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

Is the Maestro Work 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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