Mastra Factory

Organization

mastra-ai

mastra-factory

Operate and supervise Mastra Factory through `mastra api factory`. Use for first-time Factory connection and login, status or queue summaries, project and work-item inspection, thread history and memory, metrics, health, decisions, attention, supervisor sessions, and user-authorized autonomous or interactive Factory operations on hosted, local, remote, or self-hosted servers.

Overview

Publisher	mastra-ai
Repository	`skills`
Skill name	`mastra-factory`
Stars	95
Forks	8
Bundled files	3
License	Apache-2.0
Links	Skill source Repository

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 mastra-ai on GitHub. Read the source before you install it.

Installation

Install the Mastra Factory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/mastra-ai/skills/tree/main/skills/mastra-factory
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.

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/mastra-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/mastra-factory .claude/skills/mastra-factory

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mastra Factory 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 Mastra Factory 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 Mastra Factory 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.

Mastra Factory Supervisor

Use mastra api factory as the operational control plane for Factory.

First use: connect before inspecting

Read references/connection.md. Check CLI availability, establish the user's actual Factory URL, and for platform-hosted deployments run mastra auth whoami. If logged out, offer mastra auth login rather than silently starting browser login. Local/self-hosted authentication may differ.

An explicit mastra api --url "$FACTORY_URL" factory project list works from an empty directory; no deployed repository or .mastra-project.json is required. Never assume a shared host, organization, project name, or ID. Preserve the explicit target on subsequent commands.

Default behavior

For status, inspection, diagnosis, queue review, or recommendation requests:

Stay read-only.
Select the sole or explicitly named project; report choices when ambiguous.
Inspect project state, work items, metrics, thresholds, decisions, attention, and supervisor health/session.
Correlate stages, revisions, sessions, decisions, and health findings.
Report active/queued work, blocked or unhealthy items, running sessions, pending decisions, human attention, and one recommended next action.
Execute a recommendation only when the current request or a previously granted operating scope authorizes it.

Required reference

Read references/factory-supervisor.md before running Factory commands for output control, contracts, the read-only workflow, mutation protocol, governance constraints, durable-session limitations, and error handling.

For “my work,” actual execution progress, thread messages, memory, or health interpretation, also read references/session-inspection.md. Distinguish card stages from running agents, historical messages from current memory, and agent claims from verified repository outcomes.

Safety boundary

Never read or reveal .env, bearer tokens, saved login contents, or platform/provider credentials.
Never invent IDs, stages, revisions, request IDs, or sessions.
Establish the user's operating scope before mutating. Authorization may cover one action or grant standing autonomy over named projects, resources, action types, or objectives.
Within a clear delegated scope, act without asking for confirmation before every mutation. Ask only when an action is ambiguous, outside scope, or materially more destructive than the granted authority.
Fetch current state before a write, make the smallest in-scope change, then refetch and report IDs, revisions, and final state.
Use transitions—not metadata updates—for stage changes, with the current revision and a fresh UUID request ID.
Never use private HTTP routes to bypass unsupported CLI operations.
If work-item start lacks a supported durable user session, report the block; never substitute the supervisor session or invent a session UUID.

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 Mastra Factory AI skill do?

Why use Mastra Factory on TypingMind?

Because you install it once and use it with any model. Mastra Factory 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 Mastra Factory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mastra-ai/skills/tree/main/skills/mastra-factory. 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 Mastra Factory?

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 Mastra Factory?

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

Is the Mastra Factory AI skill free?

Yes. It is published on GitHub by mastra-ai under the Apache-2.0 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Mastra Factory Supervisor

First use: connect before inspecting

Default behavior

Required reference

Safety boundary

Bundled files

Frequently asked questions

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