Institutional Infrastructure & U.S. DOL TEN No. 07-25 Framework

Empowering State Hubs & Institutions as a Non-Competing 50-State Enabling Partner

Deploying turnkey Curriculum-as-Code (CaC) pipelines, air-gapped local RAG coaching engines, and zero-cost OER course fleets that satisfy NSF TechAccess: AI-Ready America, U.S. DOL ETA TEN No. 07-25 (Issued February 13, 2026), and federal ADA Title II (28 CFR Part 35) accessibility mandates out of the box.

Core Infrastructure: Curriculum-as-Code (CaC) & The S-A-R Standard

Traditional curriculum development is static, siloed, and slow to adapt. Curriculum-as-Code (CaC) treats course design as Scalable, Auditable, and Repeatable (S-A-R) software infrastructure to deliver institutional agility:

• Version-Controlled Single Sources of Truth (SSoT): Course content lives in structured Git and Markdown repositories (EXW-Master-Library) driven by a 3-Stage “Write Once -> Push Many” Zero-Drift Publishing Pipeline, enabling instant synchronization across Canvas LMS shells via a Canvas GraphQL-First API transport and automated Non-Destructive Dual-Namespace OER Syndication (EXW -> KIN / HLTH).
• Automated ADA Title II & 9:1 Double-Contrast Linters: Custom build pipelines programmatically enforce all 8 Canvas Course Accessibility Checker rules before content reaches student screens—including sequential semantic hierarchy starting at <h2>, explicit table scopes and captions, and a strict 9:1 Double-Contrast Guard (doubling WCAG 2.2 AA’s 4.5:1 baseline).
• Air-Sovereign Local AI Integration (AI-AWARE -> AI-READY): Local AI coaching and auditing engines (exw-argon4, exw-argon4-cac, exw-gemma4 via Apple Silicon Ollama and Google Gemma open-weight models) are integrated directly into the repository workflow alongside a 3,265-node DOM GraphRAG pedagogical graph and W3C PROV-O cryptographic provenance receipts—guaranteeing 100% FERPA and HIPAA data sovereignty with zero cloud egress or recurring commercial licensing fees.

The U.S. DOL ETA TEN No. 07-25 & CaC Integration Matrix

Part A: The 5 Foundational AI Literacy Content Areas

To satisfy institutional accreditors (HLC), state workforce boards, and federal grant officers, our Curriculum-as-Code architecture maps every kinesiology, health, and nutrition module directly to the five foundational competencies of the U.S. DOL AI Literacy Framework (TEN No. 07-25):

Part B: Alignment with the 7 Community College Delivery Design Principles

1. Enable Experiential Learning: Tactile client case simulations on exwtoolkit.com paired with live P-T-C-F prompt auditing.
2. Embed Learning in Context: Grounded in authentic regional demographics and extreme-heat physiology (Yuma, AZ and Imperial Valley, CA Hispanic-Serving Institution context—serving a 77% Latino [3.08x the 25% federal Title V HSI threshold] and 62% first-generation student body, per AWC President Dr. Reetika Dhawan as quoted in The Yuma Sun, November 11, 2026).
3. Build Complementary Human Skills (The Centaur Protocol): Trains the student as the Architect and Pilot (Problem Framing & Clinical Judgment) and positions AI strictly as the Engine and Clinical Calculator (Problem Solving & Processing).
4. Address Prerequisites & Equitable Access: Engineered directly for borderland cohorts where “challenge is everywhere” (Dr. Reetika Dhawan, The Yuma Sun, Nov. 11, 2026): every module opens with a bilingual In Plain English / En Palabras Sencillas callout, zero-textbook-cost ($0 ZTC) CC BY-NC-SA 4.0 OER materials, unlimited mastery revisions (allowed_attempts = -1 on the 10-7-0 scale), and browser-native simulators requiring zero paid AI subscriptions.
5. Create Pathways for Continued Learning: Dual-mapped from K-16 dual-enrollment standards (CCSS and WA.ELA) to community college transfer catalogs (KIN and HLTH) and workforce credentials.
6. Prepare Enabling Roles: Equips allied health, wellness, and exercise science paraprofessionals to safely govern AI decision-support tools in clinical and community settings.
7. Design for Agility: Terminology-agile central standards registry (01_INFRASTRUCTURE/educational_alignment_registry.json) that recompiles all 5 courses and 10 OER packages in seconds when federal guidelines update.

Why We Are the Non-Competing 50-State Enabling Partner

NSF TechAccess: AI-Ready America Coordination Hubs, community colleges, and state workforce boards need ready-to-deploy technical infrastructure, not another competing prime applicant:

• Zero Grant Friction: We never compete against community colleges, universities, or state consortia for primary NSF State or Territory Coordination Hub or state block grants; we serve strictly as a 50-state technical readiness resource, open reference architecture, and subaward implementation partner that helps primary awardees accelerate their broader impacts.
• Turnkey Dual-Namespace OER Fleet: Instant 4-Tier educationalAlignment crosswalk mapping across all 5 fleet courses—bridging local catalog codes (EXW 101, EXW 150, EXW 210, EXW 245, EXW 265) to national transfer namespaces (KIN-101, HLTH-150, KIN-210, KIN-245, KIN-265) with Schema.org/Dataset JSON-LD syndication.
• Sovereign, Air-Gapped AI Architecture: Built on local Apple Silicon (M1 and M2) Ollama and open-weight Google Gemma frameworks—guaranteeing 100% FERPA compliance with zero recurring cloud subscription costs.
• Verified Federal Subaward & Procurement Readiness (Webcognita LLC):
  • Socioeconomic Status: Service-Disabled Veteran-Owned Small Business (SDVOSB / VOSB — USMC Veteran)
  • SAM.gov UEI: EZ34U9KNJA17 • CAGE Code: 4ZCN6
  • NAICS Codes: 541511 (Primary), 541715, 541512, 611710, 541690
  • Federal PSC Codes: U008 (Curriculum Development), U012 (IT and AI Training), DA01, DJ01, R425, AN12
  • Cleared Security Pedigree & Technical Trust: Principal is a USMC & U.S. Army Counterintelligence Veteran with a documented background holding a Top Secret / SCI (SI/TK) clearance and leading elite Technical Surveillance Countermeasures (TSCM) operations. This foundational pedigree ensures absolute alignment with federal data sovereignty, strict operational security (OPSEC), and rapid corporate clearability for classified or sensitive task orders.
  • Official .edu Institutional Features: 2025 U.S. SBA Arizona Veteran Small Business Champion of the Year (AWC SBDC PDF) • Adaptive Training Available at AWC — VA Grant-Funded Adaptive Athlete Specialist & Audio-Feedback Ergometer Telemetry (AWC News, Feb 7, 2018)
  • Open-Source Reference Architecture & Codebase: Alan Pruitt (@alanpruitt) on GitHub — Curriculum-as-Code (CaC) & S-A-R v2.2 Repositories

Reference Implementation: argon4_cac_evaluator.py (S-A-R v2.2)

Below is the executable Compliance-as-Code linter (scripts/argon4_cac_evaluator.py) that audits all 5 fleet courses (EXW 101, 150, 210, 245, 265 → KIN / HLTH Dual-Namespace) against U.S. DOL ETA TEN No. 07-25, the 8 Canvas Course Accessibility Checker rules, and the 9:1 Double-Contrast Guard before emitting a SHA-256 provenance receipt:

#!/usr/bin/env python3
"""
Module: argon4_cac_evaluator.py
Description: S-A-R (Scalable / Auditable / Repeatable) Compliance-as-Code linter and
             Curriculum-as-Code (CaC) validator for the EXW 5-course fleet
             (EXW 101, 150, 210, 245, 265 -> KIN / HLTH OER Dual-Namespace)
             enforcing U.S. DOL ETA TEN No. 07-25, NSF TechAccess: AI-Ready America,
             and the 8 Canvas Course Accessibility Checker rules (9:1 Double-Contrast Guard).
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

from __future__ import annotations

import hashlib
import json
from pathlib import Path
import re

MIN_CONTRAST_RATIO = 9.0  # EXW 9:1 Double-Contrast Guard (2x WCAG 2.2 AA 4.5:1 minimum)
MAX_HEADING_LENGTH = 120  # Canvas Course Accessibility Checker Rule #2

COURSE_SSOT_MAP = {
    "EXW101": {"oer_namespace": "KIN-101", "required_ssot": ["PAGA", "Physical Activity Guidelines", "2018"]},
    "EXW150": {"oer_namespace": "HLTH-150", "required_ssot": ["Dietary Guidelines", "DGA", "Real Food"]},
    "EXW210": {"oer_namespace": "KIN-210", "required_ssot": ["PAGA", "ACSM", "NASM", "OPT"]},
    "EXW245": {"oer_namespace": "KIN-245", "required_ssot": ["ACSM", "12th Edition", "Exercise Testing"]},
    "EXW265": {"oer_namespace": "KIN-265", "required_ssot": ["PAGA", "Physical Activity Guidelines", "ACSM"]},
}

Reference Implementation: argon4_peer_reply_auditor.py (S-A-R v2.2)

Below is the peer-response baseline and recursive thread-traversal verifier (scripts/argon4_peer_reply_auditor.py) enforcing the 10-7-0 Mastery Scale (>= 2 peer replies in Module 00 Self-Introduction; >= 1 peer reply in Modules 01–16; 10.0 Exemplary initial post with 0 replies = 9.0 / 10) and Module 10+ Bilingual Thread Closure:

#!/usr/bin/env python3
"""
Module: argon4_peer_reply_auditor.py
Description: Enforces the SSoT peer-response baseline (>= 2 replies in Module 00;
             >= 1 reply in Modules 01-16 across EXW 101, 150, 210, 245, 265),
             recursively traverses Canvas /discussion_topics/:id/view trees, and
             verifies Module 10+ bilingual thread-closure integrity.
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

import re

TARGET_COURSES = {
    "EXW101": {"oer_namespace": "KIN-101", "fall26_pro_id": 38773, "dev_id": 42044, "sp27_pro_id": 41088},
    "EXW150": {"oer_namespace": "HLTH-150", "fall26_pro_id": 39322, "dev_id": 38147, "sp27_pro_id": 41625},
    "EXW210": {"oer_namespace": "KIN-210", "dev_id": 42045},
    "EXW245": {"oer_namespace": "KIN-245", "dev_id": 38148},
    "EXW265": {"oer_namespace": "KIN-265", "fall26_pro_id": 38769, "dev_id": 38156, "sp27_pro_id": 41083},
}


def verify_discussion_baseline(module_id: str, current_replies: int) -> bool:
    norm = re.sub(r"[^a-z0-9]", "", module_id.lower())
    required_min = 2 if norm in ("module00", "mod00", "00", "selfintroduction") else 1
    return current_replies >= required_min

Reference Implementation: canvas_cac_sync.py (S-A-R v2.2)

Below is the 3-Stage “Write Once -> Push Many” Zero-Drift Synchronization Engine (01_INFRASTRUCTURE/canvas_cac_sync.py) pairing the Canvas GraphQL-First Mandate (/api/graphql) with exact-slug REST page upserts and mandatory remediate_canvas_rce_html() pre-write accessibility gating across all 12 instructional DEV/PRO shells and EXW-Demo (42051):

#!/usr/bin/env python3
"""
Module: canvas_cac_sync.py
Description: 3-Stage "Write Once -> Push Many" Zero-Drift Curriculum-as-Code (CaC)
             synchronization engine for Canvas LMS (GraphQL-First + Exact-Slug REST Upsert),
             enforcing all 8 Canvas Course Accessibility Checker rules and the 9:1 Double-Contrast Guard.
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

CANVAS_3STAGE_PIPELINE_MATRIX = {
    "EXW101": {"oer_namespace": "KIN-101", "stage1_fall26_pro": 38773, "stage2_dev_8wk": 42044, "stage2_dev_16wk": 38157, "stage3_sp27_pro": 41088},
    "EXW150": {"oer_namespace": "HLTH-150", "stage1_fall26_pro": 39322, "stage2_dev": 38147, "stage3_sp27_pro": 41625},
    "EXW210": {"oer_namespace": "KIN-210", "stage2_dev_warm_standby": 42045},
    "EXW245": {"oer_namespace": "KIN-245", "stage2_dev": 38148},
    "EXW265": {"oer_namespace": "KIN-265", "stage1_fall26_pro": 38769, "stage2_dev": 38156, "stage3_sp27_pro": 41083},
    "EXW-DEMO": {"oer_namespace": "EXW-DEMO-HUB", "showcase_id": 42051},
}

Reference Implementation: push_announcement.py (S-A-R v2.2)

Below is the idempotent announcement publisher (scripts/push_announcement.py) enforcing the Plain-English & Norteño-Spanish Student-Facing Gate (validate_plain_english_copy()), 9:1 Double-Contrast Guard, and in-place GET → PUT deduplication across all 5 fleet courses:

#!/usr/bin/env python3
"""
Module: push_announcement.py
Description: Idempotent S-A-R Canvas LMS Announcement Publisher enforcing the
             Plain-English / Norteño-Spanish student-facing gate, 9:1 contrast
             verification, and read-only dry-run safety across EXW 101-265.
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

ACTIVE_SHELLS = {
    "EXW101": {"oer_namespace": "KIN-101", "fall26_pro_id": 38773, "dev_8wk_id": 42044, "sp27_pro_id": 41088},
    "EXW150": {"oer_namespace": "HLTH-150", "fall26_pro_id": 39322, "dev_id": 38147, "sp27_pro_id": 41625},
    "EXW210": {"oer_namespace": "KIN-210", "dev_warm_standby_id": 42045},
    "EXW245": {"oer_namespace": "KIN-245", "dev_id": 38148},
    "EXW265": {"oer_namespace": "KIN-265", "fall26_pro_id": 38769, "dev_id": 38156, "sp27_pro_id": 41083},
}

Reference Implementation: test_rag_pipeline.py (S-A-R v2.2)

Below is the 3-gate Python pre-flight verifier (scripts/test_rag_pipeline.py & scripts/test_rag_pipeline.sh) validating /health (HTTP 200), mandatory boundary-tag delimiter enforcement (###CASE_DATA_START### / ###CASE_DATA_END### $\rightarrow$ HTTP 422), and local Apple Silicon RAG inference (exw-carbon4:latest $\rightarrow$ HTTP 200 with W3C PROV-O provenance, DOM GraphRAG, 4-D XAI Radar, and W3C SKOS bilingual taxonomy):

#!/usr/bin/env python3
"""
Module: test_rag_pipeline.py
Description: 3-Gate S-A-R Pre-Flight Verifier for The EXW Clinical Auditor API
             (`scripts/canvas_rag_api.py` & `scripts/test_rag_pipeline.sh`), testing
             /health, HTTP 422 delimiter rejection, and HTTP 200 W3C PROV-O / DOM GraphRAG
             / W3C SKOS local Apple Silicon inference.
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

from canvas_rag_api import handle_audit_payload, handle_health

def verify_rag_gates(neg_payload: dict, pos_payload: dict) -> dict:
    health_body, health_code = handle_health()
    neg_body, neg_code = handle_audit_payload(neg_payload)  # Expects HTTP 422 Rejected
    pos_body, pos_code = handle_audit_payload(pos_payload)  # Expects HTTP 200 W3C PROV-O
    return {
        "health_ok": health_code == 200 and health_body.get("status") == "healthy",
        "delimiter_422_guard_ok": neg_code == 422,
        "prov_o_and_skos_ok": pos_code == 200 and "provenance" in pos_body,
    }

Reference Implementation: argon4_fleet_evaluator.py (Gate 5: U.S. DOL TEN No. 07-25 Audit)

Below is the Gate 5 pre-commit compliance evaluator (scripts/argon4_fleet_evaluator.py) auditing all 119 active curriculum files across EXW101, EXW150, EXW210, EXW245, and EXW265 for U.S. DOL ETA TEN No. 07-25 Content Areas 3–5 (P-T-C-F Prompt Engineering, 150-Word Clinical SSoT Audit, and 150-Word Active Verification) and stamping data-exw-dol-ten="07-25" metadata:

#!/usr/bin/env python3
"""
Module: argon4_fleet_evaluator.py
Description: Gate 5 Pre-Commit Compliance Evaluator enforcing U.S. DOL ETA TEN No. 07-25
             Content Areas 3-5 (P-T-C-F Prompt Engineering, 150-Word Clinical SSoT Audit,
             and Active Verification) and Pattern / Rule / Solve clinical framing across
             all 5 fleet courses (EXW101, EXW150, EXW210, EXW245, EXW265).
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

FLEET_MODULES = ["EXW101", "EXW150", "EXW210", "EXW245", "EXW265"]
TEN_COMPLIANCE_TAG = 'data-exw-dol-ten="07-25"'

Reference Implementation: argon4_local_rag_verify.py (S-A-R v2.2)

Below is the sovereign Apple Silicon RAG index verifier (scripts/argon4_local_rag_verify.py) validating SHA-256 chunk integrity (verify_rag_chunk_integrity()) across all 3,638 indexed curriculum chunks (nomic-embed-text + exw-carbon4-cac:latest) and enforcing ### RETRIEVED ACADEMIC SSoT CONTEXT ### boundary encapsulation:

#!/usr/bin/env python3
"""
Module: argon4_local_rag_verify.py
Description: Sovereign Apple Silicon RAG Corpus & Vector Index Integrity Verifier
             (`exw-carbon4-cac:latest` + `nomic-embed-text`), validating SHA-256
             chunk provenance and ### RETRIEVED ACADEMIC SSoT CONTEXT ### encapsulation.
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

OLLAMA_MODEL = "exw-carbon4-cac:latest"
EMBEDDING_MODEL = "nomic-embed-text"
SOVEREIGN_MODEL_TRIAD = ("exw-carbon4-cac:latest", "exw-carbon4:latest", "exw-gemma4:latest")

Reference Implementation: canvas_graphql_sync.py (S-A-R v2.2)

Below is the Canvas GraphQL-First (/api/graphql) synchronization validator (scripts/canvas_graphql_sync.py & scripts/canvas_graphql_baseline.py) verifying all fleet queries and mutations against the Live AWC Canvas GraphQL Schema Baseline (30 root queries, 122 root mutations, 674 named types, <375,000 complexity ceiling) across EXW101, EXW150, EXW210, EXW245, and EXW265:

#!/usr/bin/env python3
"""
Module: canvas_graphql_sync.py
Description: Canvas LMS GraphQL-First (/api/graphql) & Curriculum-as-Code (CaC)
             Synchronization Orchestrator validating queries/mutations against
             docs/standards/canvas_graphql_schema_baseline.json (<375,000 complexity).
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

CANVAS_GRAPHQL_ENDPOINT = "https://azwestern.instructure.com/api/graphql"
MAX_GRAPHQL_COMPLEXITY = 375_000
TEN_COMPLIANCE_TAG = 'data-exw-dol-ten="07-25"'

Reference Implementation: prov_o_receipt_generator.py (W3C PROV-O JSON-LD S-A-R v2.2)

Below is the W3C PROV-O (http://www.w3.org/ns/prov#) + W3C SKOS provenance receipt generator (scripts/prov_o_receipt_generator.py) binding exw:LocalRAGInferenceExecution (prov:Activity), exw:CurriculumMarkdownCorpus (prov:Entity), exw:OllamaModelExwCarbon4Cac (prov:Entity), exw:Carbon4AppleSiliconNode (prov:SoftwareAgent), and exw:ComplianceAuditReceipt (prov:Entity) into a SHA-256 signed JSON-LD graph:

#!/usr/bin/env python3
"""
Module: prov_o_receipt_generator.py
Description: W3C PROV-O + W3C SKOS JSON-LD Provenance Receipt Generator tracing
             119 curriculum Markdown modules, 3,638 SHA-256 vector chunks, and
             local Apple Silicon inference (`exw-carbon4-cac:latest` + `nomic-embed-text`).
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

PROV_O_CONTEXT = {
    "prov": "http://www.w3.org/ns/prov#",
    "exw": "https://alanpruitt.com/ns/exw#",
    "dcterms": "http://purl.org/dc/terms/",
    "skos": "http://www.w3.org/2004/02/skos/core#",
    "schema": "https://schema.org/",
}

Reference Implementation: build_oer_syndication_export.py (Dual-Namespace & 4-Tier K–16 Standards Compiler)

Most institutions face an accreditation catch-22 when scaling Dual-Enrollment, Perkins V CTE, Running Start, and WIOA cohorts. Placing Grade 11–12 standards (CCSS or WA.ELA) on a college syllabus risks regional accreditor questions over collegiate credit rigor. Omitting those codes entirely hides the course from K–12 Dual-Enrollment districts, OER Commons harvesters, and Google Dataset Search.

Our Dual-Namespace (EXW -> KIN / HLTH) & 4-Tier Standards Compiler (scripts/build_oer_syndication_export.py + 01_INFRASTRUCTURE/educational_alignment_registry.json) solves both from a single JSON registry with zero accreditation collision:

• Collegiate Syllabus Plane: Preserves pure HLC / university-transfer post-secondary competencies (Procedure 601.3 / 615.3) with zero secondary-level dilution.
• K–16 & Workforce Syndication Plane: Automatically compiles Tier 1 Federal Clinical SSoTs, Tier 2 Workforce AI Literacy (US-DOL-TEN-07-25 & NSF-TECHACCESS-AI-READY-2026), Tier 3 HLC ILOs, and Tier 4 Dual-Enrollment Bridge Standards (CCSS.ELA-LITERACY.W.11-12.6, CCSS.ELA-LITERACY.RST.11-12.7, WA.ELA.11-12.W.6—adaptable in minutes to any of the 50 states) into Module 00 crosswalk tables, SHA-256 .zip OER packages, and Schema.org/Dataset JSON-LD.

Reference Implementation: .github/workflows/deploy.yml (CI/CD WCAG & Canonical Gate)

Below is the automated GitHub Actions production deployment workflow (.github/workflows/deploy.yml) executing Hugo --minify, canonical tag verification (inject_canonicals.py), markdownlint, pour_audit.py, wcag3_atomic_linter.py, Playwright route auditing, and native GitHub Pages artifact deployment (actions/deploy-pages@v4):

name: Deploy Hugo Site with WCAG & Route Verification
on:
  push:
    branches: [main]
  workflow_dispatch:

jobs:
  build-and-audit:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
          submodules: recursive
      - uses: peaceiris/actions-hugo@v3
        with:
          hugo-version: "latest"
          extended: true
      - name: Build, Inject Canonicals & Audit WCAG/POUR
        run: |
          hugo --minify
          python3 scripts/inject_canonicals.py
          npm run lint:md
          python3 pour_audit.py content/en/institutional-infrastructure/_index.md
          python3 scripts/wcag3_atomic_linter.py content/en/institutional-infrastructure/_index.md

Reference Implementation: argon4_checklist_evaluator.py (5-Phase Master Deployment Checklist Auditor)

Below is the 5-Phase S-A-R Master Deployment Checklist Evaluator (scripts/argon4_checklist_evaluator.py) dynamically verifying all generated JSON/PROV-O receipts, SHA-256 signatures, local Apple Silicon RAG readiness (3,638 vector chunks), Gate 5 U.S. DOL TEN No. 07-25 compliance (119/119 PASS), Canvas GraphQL schema parity (0 violations), and Hugo/GitHub Actions portal deployment:

#!/usr/bin/env python3
"""
Module: argon4_checklist_evaluator.py
Description: 5-Phase S-A-R Master Deployment Checklist Evaluator verifying local
             Apple Silicon RAG readiness, Gate 5 TEN No. 07-25 compliance, Canvas
             GraphQL-First sync, W3C PROV-O JSON-LD receipts, and Hugo CI/CD deployment.
Author: Coach Alan Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)
"""

CHECKLIST_PHASES = [
    "Phase 1: Local Environment & Repository Synchronization",
    "Phase 2: Local AI & RAG Inference Pre-Flight",
    "Phase 3: Compliance & Accessibility Validation (Gates 1-5)",
    "Phase 4: LMS Synchronization & Provenance Tracing",
    "Phase 5: Production Deployment & Portal Overhaul",
]

Reference Implementation: argon4_ollama_env_config.sh (Apple Silicon Sovereign Ollama Runtime)

Below is the Apple Silicon unified-memory configuration and verification script (scripts/argon4_ollama_env_config.sh) tuning OLLAMA_FLASH_ATTENTION=1, OLLAMA_KV_CACHE_TYPE="q8_0", OLLAMA_NUM_PARALLEL=4, and OLLAMA_MAX_LOADED_MODELS=2 while verifying the live /api/tags manifest for exw-carbon4-cac:latest, exw-carbon4:latest, exw-gemma4:latest, and nomic-embed-text:latest:

#!/bin/zsh
export OLLAMA_HOST="127.0.0.1:11434"
export OLLAMA_MODELS="${HOME}/.ollama/models"
export OLLAMA_NUM_PARALLEL=4
export OLLAMA_MAX_LOADED_MODELS=2
export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE="q8_0"

curl -sf "http://${OLLAMA_HOST}/api/tags" > /dev/null
ollama list | grep -E "exw-argon4-cac|exw-argon4|exw-gemma4|nomic-embed-text|gemma"

Master Deployment Status Manifest (m2_argon4_cac_deployment_status.json)

Below is the consolidated S-A-R v2.2 deployment status manifest generated by scripts/argon4_checklist_evaluator.py, binding all 5 deployment phases and 13 cryptographic audit receipts across EXW-Master-Library, Canvas LMS (EXW 101, 150, 210, 245, 265 $\rightarrow$ KIN / HLTH), alanpruitt.com, and webcognita.com:

{
  "system_report": "M2 / Carbon 4 Curriculum-as-Code (CaC) Deployment Status",
  "schema_version": "2.2.0",
  "author": "Coach Alan L. Pruitt (Webcognita LLC • SDVOSB • SAM.gov UEI: EZ34U9KNJA17 • CAGE: 4ZCN6)",
  "strategic_positioning": "Non-Competing 50-State Enabling Partner for NSF TechAccess: AI-Ready America & U.S. DOL ETA TEN No. 07-25 Initiatives",
  "infrastructure_status": {
    "hardware_nodes": "M1 MacBook Pro <-> M2 MacBook Air <-> Carbon 4 Edge Node (Thunderbolt Bridge 192.168.100.2 & Mandatory Two-Machine Git Handshake)",
    "local_rag_inference": "Active (Apple Silicon MLX Ollama runtime hosting exw-carbon4-cac:latest, exw-carbon4:latest, exw-gemma4:latest & nomic-embed-text:latest across 3,638 SHA-256 chunks)",
    "ferpa_compliance": "100% Air-Gapped Sovereign Execution (Zero Cloud Egress)"
  },
  "pipeline_execution_summary": {
    "phase_1_repository_sync": "PASSED (119 active Markdown SSoT modules verified across EXW101, EXW150, EXW210, EXW245, EXW265)",
    "phase_2_rag_preflight": "PASSED (Flask API /health HTTP 200, HTTP 422 ###CASE_DATA_START### delimiter guard & W3C PROV-O / SKOS active)",
    "phase_3_compliance_gates": "PASSED (Gate 5 TEN No. 07-25 data-exw-dol-ten='07-25' & 9:1 Double-Contrast Guard verified)",
    "phase_4_lms_sync": "PASSED (Canvas GraphQL-First /api/graphql & 3-Stage CaC receipts verified across EXW101, EXW150, EXW210, EXW245, EXW265 -> KIN / HLTH)",
    "phase_5_production_deployment": "PASSED (Hugo static site build, 232 canonicals, POUR/WCAG 3.0 linters, GitHub Actions CI/CD & 12-shell Coach Alan portrait verified)"
  }
}

Institutional Notice & Compliance Disclaimer: The Curriculum-as-Code architecture, S-A-R Standard, and open-source software tools described on this platform represent independent faculty engineering and research through Webcognita LLC (SAM.gov UEI: EZ34U9KNJA17 | CAGE: 4ZCN6) and do not represent official policy or endorsement by Arizona Western College or The University of Arizona.