Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Mark393295827/third-brain-v7-skills --skill ai-six-sigma-property-osgit clone --depth 1 https://github.com/Mark393295827/third-brain-v7-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mark393295827/third-brain-v7-skills/ai-six-sigma-property-os)<a href="https://agentmods.dev/skills/mark393295827/third-brain-v7-skills/ai-six-sigma-property-os"><img src="https://agentmods.dev/badge/skills/mark393295827/third-brain-v7-skills/ai-six-sigma-property-os/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mark393295827/third-brain-v7-skills/ai-six-sigma-property-os"><img src="https://agentmods.dev/badge/skills/mark393295827/third-brain-v7-skills/ai-six-sigma-property-os.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.01194 |
| Opus 5 | $0.00019 | $0.00597 |
| Sonnet 5 | $0.00008 | $0.00239 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
ai-six-sigma-property-os scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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>
- Define: set customer pain, process boundary, work-order type, SLA, CTQs, and excluded scope.
- Measure: map each CTQ to formula, source field, owner, baseline, target, and data-quality check.
- 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.
- Improve: propose one bounded change with hypothesis, owner, rollout cohort, budget, success/guardrail metrics, and rollback trigger.
- Control: define dashboard, alert, approval, exception, audit sample, and review cadence.
- Define ontology objects and legal work-order transitions before assigning agent roles.
- Give each agent a bounded input, action, output, confidence, evidence, and human gate.
- Keep customer-facing quotes, pricing/policy changes, low-confidence dispatch, safety, compliance, privacy, payment, case closure, and disciplinary action under human approval.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 105 lines · 39 tokens per session scan A 1ab052346a28
ai-six-sigma-property-os is a skill published in the GitHub repository Mark393295827/third-brain-v7-skills (138 stars, last pushed 22d ago), licensed MIT. It adds 39 tokens to every session and 1,194 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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