Business Os logo

Business Os

Organization
microsoft
business-os

Turns vague or complex business requests (decisions, problems, projects, incidents, processes, ideas, documents, communications) into a clear outcome, an appropriately sized plan, and a verified result. Also assesses whether repeated work should become a reusable Skill and can draft that Skill package. Use for business questions, tasks, decisions, problems, projects, incidents, process design, organisational planning, "figure this out" requests, or requests to turn a process into a Skill. Do not use for casual conversation, trivial rewrites, or work already covered by a more specific installed Skill.

Overview

Publishermicrosoft
Repositorycat-agent-skills
Skill namebusiness-os
Stars
95
Forks
115
Bundled files
30
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.

  • 30 bundled files

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

  • Open source

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

Installation

Install the Business Os 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.
    https://github.com/microsoft/cat-agent-skills/tree/main/submissions/business-os
  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/microsoft/cat-agent-skills.git /tmp/cat-agent-skills
mkdir -p .claude/skills
cp -r /tmp/cat-agent-skills/submissions/business-os .claude/skills/business-os
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Business Os 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 Business Os 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 Business Os 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.

Business OS

Business OS is an operating layer for business work. It works out what outcome is actually needed, picks a proportionate method, does what it can, and is honest about what it cannot verify. It also recognises when repeated work is a candidate for a reusable Skill.

When to use this Skill

Use it for: business decisions, problems, projects, incidents, process design or improvement, ambiguous "sort this out" requests, business communications and meeting outputs, and requests to turn a completed process into a Skill.

Do not use it for: casual chat, a one-line factual lookup, a small rewrite or formatting request, or anything a more specific installed Skill already handles well. For those, just do the task.

Operating loop

Work through these steps mentally for anything beyond a trivial request. Skip stages that add no value for the task at hand.

  1. Outcome - What result would count as success here? Is the stated request a proxy for a bigger need? Say the outcome back in one line before doing the work.
  2. Classify the work - Pick a primary type: question, task, decision, problem, project, incident, process, idea/opportunity, document, research, communication, review, strategy, or unknown (investigate until it's classifiable). Work is often hybrid; name a primary type and any secondary ones.
  3. Size the response - Judge reversibility, financial/legal/security exposure, number of stakeholders, and urgency. Choose:
    • Fast - simple, low-risk, obvious. Just do it.
    • Structured - needs a short plan, some evidence gathering, or a template.
    • Deep - high-impact, ambiguous, regulated, or multi-stage. Load the relevant reference(s) below. Do not apply Deep-path rigour to Fast-path work, and do not give a shallow answer to a Deep-path problem.
  4. Check what's known versus missing. Separate fact, retrieved evidence, user-supplied information, reasonable inference, and assumption. Only raise a question if answering it changes what you'd do next; otherwise proceed on a stated, labelled assumption.
  5. Do the work, pulling in one or two relevant references from below, not all of them.
  6. Verify - did the actual outcome from step 1 get met, not just "was an action taken"? State completion status plainly (complete, complete with caveats, partially complete, blocked, or needs a human decision).
  7. Capture learning, if it's worth capturing. Only propose a playbook, template, or Skill candidate when the work is genuinely repeatable and valuable, not after a one-off task.

Reference library

Load only the reference file(s) relevant to the work in front of you.

ReferenceLoad when
references/work-classification.mdThe request is ambiguous, or you need help sizing Fast/Structured/Deep, or the user says something like "figure this out"
references/executive-lenses.mdThe work benefits from more than one professional viewpoint (finance, operations, technology, security, legal, customer, people, risk)
references/decision-engine.mdThe work is a decision between options
references/problem-solving.mdThe work is a problem with an unclear cause
references/process-engine.mdThe work is about designing, mapping, or improving a business process
references/project-engine.mdThe work needs a plan with scope, milestones, dependencies and owners
references/incident-engine.mdSomething has gone wrong and needs stabilising, containing, and reviewing
references/communication-engine.mdThe output is a message, update, or meeting artefact
references/company-dna.mdYou need to represent or ask about organisation-specific policy, structure, or ways of working
references/memory-and-learning.mdSubstantial work has finished and reusable learning might exist
references/skill-generator.mdThe user wants to turn a process into a Skill, or repeated work looks like a strong Skill candidate
references/governance-evidence-qa.mdThe work involves a high-impact action, a confidence judgement, or a final quality check before handing something over

Evidence and honesty rules

  • Never present an assumption as a fact. Label assumptions.
  • Never invent organisation-specific policy, approvals, people, dates, or tool results. If you don't know the organisation's rule, say so and ask, or flag it as unknown in a Company DNA sense (see references/company-dna.md).
  • Distinguish drafting/recommending from doing. Do not treat permission to analyse as permission to send, commit, delete, or approve something irreversible.
  • If a needed tool or piece of information genuinely isn't available, say so plainly and give the best next-best output rather than pretending the action happened.

Output style

Default to concise, plain, and useful. Use structure (headings, short lists) when it aids clarity, not as a template you apply automatically every time. For substantial work, useful sections often include: outcome, what was found, recommendation, plan, risks or open decisions, next actions - but don't force all of them if they don't apply.

Anti-patterns to avoid

  • Turning a small task into a consulting engagement.
  • Asking questions before making any progress, when a labelled assumption would do.
  • Producing a risk register, stakeholder map, or 12-step plan for something that doesn't need one.
  • Claiming a task is done when it wasn't verified.
  • Loading every reference file for one request.
  • Treating one successful example as proof something is a mature, repeatable process.

Skill generation, briefly

If asked to turn a process into a Skill, or if repeated, stable, valuable work looks like a strong candidate, open references/skill-generator.md. It covers candidate assessment, the build pipeline, and the quality bar a generated Skill must meet, including the assets/templates/skill-specification.md and assets/templates/skill-candidate-assessment.md shapes.

Further reading

references/worked-examples.md shows short walk-throughs across the Fast, Structured and Deep paths, Figure It Out mode, and Skill generation. references/test-scenarios.md sets out expected behaviour for normal, edge and adversarial cases.

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 Business Os AI skill do?

Turns vague or complex business requests (decisions, problems, projects, incidents, processes, ideas, documents, communications) into a clear outcome, an appropriately sized plan, and a verified result. Also assesses whether repeated work should become a reusable Skill and can draft that Skill package. Use for business questions, tasks, decisions, problems, projects, incidents, process design, organisational planning, "figure this out" requests, or requests to turn a process into a Skill. Do not use for casual conversation, trivial rewrites, or work already covered by a more specific instal...

Why use Business Os on TypingMind?

Because you install it once and use it with any model. Business Os 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 Business Os in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/cat-agent-skills/tree/main/submissions/business-os. 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 Business Os?

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 Business Os?

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

Is the Business Os AI skill free?

Yes. It is published on GitHub by microsoft 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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