Ai Six Sigma Property Os logo

Ai Six Sigma Property Os

Community
Mark393295827
ai-six-sigma-property-os

Use when property-service operations need an AI plus ontology plus DMAIC design for work orders, dispatch, quotes, evidence, CTQ metrics, and control dashboards.

Overview

PublisherMark393295827
Repositorythird-brain-v7-skills
Skill nameai-six-sigma-property-os
Stars
141
Forks
22
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 Mark393295827 on GitHub. Read the source before you install it.

Installation

Install the Ai Six Sigma Property 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/Mark393295827/third-brain-v7-skills/tree/main/skills/ai-six-sigma-property-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/Mark393295827/third-brain-v7-skills.git /tmp/third-brain-v7-skills
mkdir -p .claude/skills
cp -r /tmp/third-brain-v7-skills/skills/ai-six-sigma-property-os .claude/skills/ai-six-sigma-property-os
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Six Sigma Property 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 Ai Six Sigma Property 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 Ai Six Sigma Property 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.

AI Six Sigma Property OS

<skill_contract> Named property-service workflow, actors, evidence, CTQs, approval boundaries, and MVP constraints. A bounded ontology, DMAIC control plan, agent roles, gates, metrics, and rollback-ready MVP design. Every proposed state transition and CTQ has an owner, evidence source, verifier, approval gate, and control receipt. <non_goals>Full ERP replacement, autonomous safety or pricing decisions, and automation of undefined processes.</non_goals>

Ontology defines the operating world; bounded agents execute and audit; DMAIC improves rules from work-order evidence. Design the management system before software scope. Load references/property-control-model.md for the baseline ontology, CTQs, and state machine.

Usage Template

Provide: business type, stage, first workflow, current process/data, service standards, approval boundaries, failure history, and MVP budget. Optional: table schemas and sample work orders.

Workflow

Verify the operating objective and select one first workflow: classification, dispatch recommendation, quote draft, evidence audit, or quality dashboard. Map actors, current states, systems of record, customer/safety impact, and data maturity.

<unknowns_gate>

If service standard, accountable owner, safety boundary, or system of record is missing, return NEEDS_INPUT. Treat absent baseline data as a Measure-phase task; never invent CTQ thresholds or automation accuracy.

</unknowns_gate>

  1. Define: set customer pain, process boundary, work-order type, SLA, CTQs, and excluded scope.
  2. Measure: map each CTQ to formula, source field, owner, baseline, target, and data-quality check.
  3. Analyze: for red metrics, use process bottlenecks, fishbone categories, and 5 Why until the cause can change a rule, field, SOP, training item, or threshold.
  4. Improve: propose one bounded change with hypothesis, owner, rollout cohort, budget, success/guardrail metrics, and rollback trigger.
  5. Control: define dashboard, alert, approval, exception, audit sample, and review cadence.
  6. Define ontology objects and legal work-order transitions before assigning agent roles.
  7. Give each agent a bounded input, action, output, confidence, evidence, and human gate.
  8. Keep customer-facing quotes, pricing/policy changes, low-confidence dispatch, safety, compliance, privacy, payment, case closure, and disciplinary action under human approval.

Use an independent quality reviewer for closure and abnormal cases. Rollback must restore the prior rule/SOP/version without deleting work-order evidence.

Trace every agent action and dashboard metric to a field, state transition, CTQ, owner, and gate. Simulate normal, missing-data, exception, rework, and cancellation paths. Reject modules with no objective metric or safe manual fallback.

<retry_policy>

max_attempts: 2. Retry design only after changing scope, data definition, rule, or control. Stop on repeated missing baseline, unsafe transition, or NO_PROGRESS; escalate the decision to the accountable operator.

</retry_policy>

<state_contract>

Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus process version, ontology, state machine, CTQ dictionary, agent contracts, approval matrix, experiment cohort, exceptions, independent review, and rollback receipt.

</state_contract>

Failure Protocol

  • NEEDS_INPUT: owner, service standard, safety boundary, or data source is unclear.
  • INSUFFICIENT_EVIDENCE: baseline cannot support threshold or automation decisions.
  • BLOCKED_PERMISSION: required approval/system access is absent; remain in manual mode.
  • VERIFY_FAILED: state, metric, or agent action is not traceable; block rollout.
  • NO_PROGRESS: two changed designs fail the same control. max_attempts: 2.
  • BUDGET_STOP: preserve the manual workflow and return the smallest measurable MVP.

Output Contract

Return status, result (DMAIC memo, ontology, states, CTQs, agent/gate matrix, dashboard, MVP), evidence, unknowns, and next_action with approval and rollback condition.

Edge Cases

  • Quote automation has no reliable material-cost feed: generate an internal draft with uncertainty and require human pricing approval; do not send it.
  • Worker recommendation is high-confidence but violates access/safety rules: rules override score and the case moves to exception review.

Success Metrics

  • One bounded workflow is measurable end to end.
  • Every automated action maps to state, evidence, CTQ, owner, and human gate.
  • Red metrics produce controlled countermeasures rather than commentary.

Quality Gates

  • MVP excludes unrelated ERP/marketplace/payroll scope.
  • CTQs have formulas, source fields, baselines, and owners.
  • Independent review, approval, manual fallback, and rollback are explicit.
  • Exception and rework paths were simulated.

</skill_contract>

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 Ai Six Sigma Property Os AI skill do?

Use when property-service operations need an AI plus ontology plus DMAIC design for work orders, dispatch, quotes, evidence, CTQ metrics, and control dashboards.

Why use Ai Six Sigma Property Os on TypingMind?

Because you install it once and use it with any model. Ai Six Sigma Property 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 Ai Six Sigma Property Os in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mark393295827/third-brain-v7-skills/tree/main/skills/ai-six-sigma-property-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 Ai Six Sigma Property 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 Ai Six Sigma Property Os?

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

Is the Ai Six Sigma Property Os AI skill free?

Yes. It is published on GitHub by Mark393295827 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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