🦔 SmartMoat

Know your moat. Score the tasks AI can't easily take — and widen them.

IAIso §6 · Workforce · SmartTasks.cloud · the Smart* family

Which of your skills can AI still not touch — and for how long?

As AI reshapes how we work, a new gap opens: your tasks are automating and you don't know which skills still defend you. SmartMoat closes it — score at the exact moment the gap bites, and it works the second you clone it (a synthetic demo ships in demo/).

Install

SmartMoat is not published on PyPI or any other package registry yet. Until this section says otherwise, a package called sf-smartmoat on any registry is not ours, and neither is smartmoat.

Install from a clone (Python 3.10 or later):

git clone https://github.com/SmartTasksOrg/sf-smartmoat
cd sf-smartmoat
python -m venv .venv
. .venv/bin/activate          # Windows PowerShell: .\.venv\Scripts\Activate.ps1
python -m pip install .
sf-smartmoat --demo

Status

• Version 3.0.0, experimental. A small deterministic command-line tool with a bundled synthetic demo and two smoke tests.
• Published: nowhere yet; install from a clone (above).
• Tested: the 2 smoke tests in tests/ on Python 3.12, Linux, on every push to master and every pull request (.github/workflows/ci.yml).
• Not tested: Windows and macOS; Python versions other than 3.12.
• Security review: none independent. Report vulnerabilities as described in SECURITY.md.

Run it in your stack

What's in this repo

• Core engine — src/sf_smartmoat/: score() -> MoatScore; plan() -> Plan. Deterministic, dependency-free.
• CLI — sf-smartmoat --demo (and --version): a deterministic demo of the core.
• Also included — a runnable demo/, examples/, the IAIso mapping spec/iaiso-map.json, a browser site/playground.html, plus public smoke tests in tests/.

How it works

Rule IDs are namespaced MOAT-* so output looks kin to the rest of the family (SmartPangolin's SEC-*, etc.). Deterministic, dependency-free, fail-loud.

The data objects (UML)

These are real dataclasses in src/sf_smartmoat/models.py — the diagram and the code are the same thing:

classDiagram
    class Task {
      +name: str
      +agent_exposure: float
      +human_moat: float
    }
    class MoatScore {
      +exposure: float
      +moat: float
      +percentile: int
    }
    class Plan {
      +moves: list[str]
    }
    class IAIsoControl {
      +section: str
      +name: str
    }
    Plan ..> IAIsoControl : conforms to

Where it sits in the architecture

SmartMoat doesn't stand alone — it stacks with the family, and everything conforms to the IAIso standard — the same standard that governs SmartTasks' own apps, while each tool here stays standalone and drops into your architecture:

graph LR
    IAIso([IAIso standard]):::std
    Cloud([SmartTasks.cloud]):::cloud
    SmartPangolin[SmartPangolin]:::tool
    SmartPrompt[SmartPrompt]:::tool
    SmartCheck[SmartCheck]:::tool
    SmartSeal[SmartSeal]:::tool
    SmartStandard[SmartStandard]:::tool
    SmartSim[SmartSim]:::tool
    SmartMoat[SmartMoat]:::tool
    SmartRoute[SmartRoute]:::tool
    SmartFeed[SmartFeed]:::tool
    SmartPangolin -->|emits clean artifacts to| SmartSeal
    SmartPrompt -->|hands secret/PII flags to| SmartPangolin
    SmartPrompt -->|enforces prompt rules from| SmartStandard
    SmartCheck -->|stamps verified output with| SmartSeal
    SmartCheck -->|checks against rules from| SmartStandard
    SmartSeal -->|issues receipts consumed by| SmartCheck
    SmartSeal -->|issues receipts consumed by| SmartRoute
    SmartStandard -->|supplies rule sets to| SmartPrompt
    SmartStandard -->|supplies rule sets to| SmartCheck
    SmartSim -->|feeds role forecasts to| SmartMoat
    SmartSim -->|draws signals from| SmartFeed
    SmartMoat -->|consumes forecasts from| SmartSim
    SmartRoute -->|verifies receipts from| SmartSeal
    SmartRoute -->|enforces the standard from| SmartStandard
    SmartFeed -->|feeds signals to| SmartSim
    SmartFeed -->|feeds signals to| SmartMoat
    SmartPangolin -.conforms.-> IAIso
    SmartPangolin -.shares IAIso with.-> Cloud
    SmartPrompt -.conforms.-> IAIso
    SmartPrompt -.shares IAIso with.-> Cloud
    SmartCheck -.conforms.-> IAIso
    SmartCheck -.shares IAIso with.-> Cloud
    SmartSeal -.conforms.-> IAIso
    SmartSeal -.shares IAIso with.-> Cloud
    SmartStandard -.conforms.-> IAIso
    SmartStandard -.shares IAIso with.-> Cloud
    SmartSim -.conforms.-> IAIso
    SmartSim -.shares IAIso with.-> Cloud
    SmartMoat -.conforms.-> IAIso
    SmartMoat -.shares IAIso with.-> Cloud
    SmartRoute -.conforms.-> IAIso
    SmartRoute -.shares IAIso with.-> Cloud
    SmartFeed -.conforms.-> IAIso
    SmartFeed -.shares IAIso with.-> Cloud
    IAIso -.governs.-> Cloud
    classDef tool fill:#1c232d,stroke:#f5b83d,color:#efe9f5;
    classDef std fill:#04121f,stroke:#46d6c8,color:#46d6c8;
    classDef cloud fill:#1a1327,stroke:#a78bfa,color:#a78bfa;
    style SmartMoat stroke-width:3px,stroke:#ff6b6b;

• SmartMoat consumes forecasts from SmartSim →

Open site/playground.html for the interactive version.

Part of the Smart* family

One system, not nine projects — same mascot, same manifesto voice, same rule-ID style, all aligned to the IAIso standard. Each is an independent, open-source, single-purpose tool you can integrate into your own architecture:

Backed by the standard: SmartMoat implements IAIso §6 · Workforce. Open-source edition: this repo is the simplified, single-purpose version, built for any org to integrate into its own architecture. SmartTasks' desktop app and SmartTasks.cloud run a more advanced, deeply-integrated implementation of the same IAIso governance — a separate product, not this code bundled.

Who's behind this

• Roen Branham — CEO & AI Strategy Architect · CISSP-certified AI, security & governance architect; author of IAIso and sole inventor of the Z4 Semantic Fabric patent application. LinkedIn
• Le Vu Tanh — CTO & Core Engineering Lead · Chief architect of the Cortex engine; large-scale system reliability and low-latency infrastructure — the engineer who ships what gets architected. LinkedIn

The team behind IAIso & SmartTasks: a CISSP-certified security & governance architect and a large-scale systems engineer — 20+ years shipping secure, AI-driven platforms for regulated, blue-chip environments (Allianz, BMW, Rolls-Royce, Heidenhain).

Runs on governed local models

Capability tiers (L1 Layman → L5 Agentic) help place the right model at the right task.

This tool is local-first, so pair it with models you can actually vet. SmartTasks publishes 21+ governance-validated GGUF builds on Hugging Face — each with a machine-readable scorecard (capability tiers L1 Layman → L5 Agentic, IAIso conformance invariants (pass/warn/fail), OWASP-mapped garak red-team, transparency probes (viewpoint-alignment / over-refusal), and per-file SHA-256). Gate model selection on evidence, not vibes — and every finding, including warnings, is published in full.

→ SmartTasks on Hugging Face · Qwen3.6-27B (L5 agentic) · react-agent-coder-llama-3.1-8b (agentic coder) · gpt-oss-20b (open reasoning)

Get in touch

• Companies & enterprises: enterprise@smarttasks.cloud — we help teams integrate SmartMoat + IAIso into their architecture so governance and audit-readiness become a byproduct of how they already work.
• The standard: IAIso · iaiso.org
• The product: SmartTasks.cloud

Built by SmartTasks Lab. Apache-2.0. Contributions welcome.