Rell domain

Skill Chase-Key/aurelion-skills/skills/rell-domain

The RELL compliance audit engine — architecture, philosophy, active modules, and development status.From its SKILL.md

Install

npx -y skills add Chase-Key/aurelion-skills --skill rell-domain

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SKILL.md

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RELL Domain Skill

What RELL Is

RELL — Regulatory Enforcement & Governance Layer with Live-monitoring

RELL is a compliance and data governance product line built on Aurelion architecture. It is not an Aurelion module — it is a separate, standalone product that derives architectural patterns from Aurelion.

Location: rell-eco/ — sibling project in your Personal Projects folder

"You have a compliance officer who sleeps. Rell doesn't."

Architecture Philosophy

Lean core. Value in the profiles.

The RELL engine (rell-engine) is domain-agnostic. It doesn't care about GDPR or HIPAA. You bring the compliance profile. The engine runs the checks.

rell-engine     ← The open core (MIT). Anyone can run this.
rell-profiles   ← The curated ruleset registry (BSL). This is the product.
rell-gov        ← Commercial: GDPR, CCPA, ISO 27001
rell-health     ← Healthcare: HIPAA, HL7, FHIR
rell-econ       ← Economic: IMF, World Bank, sovereign indicators
rell-infra      ← Infrastructure: NERC CIP, CMMC, ICS compliance
rell-esg        ← ESG: GRI, TCFD, SEC climate disclosure

Active Components (as of March 2026)

rell-engine (Phase 1 — In Progress)

The portable audit core. Extracted from AURELION Nexus Premium. All AURELION-specific references (Stonecrest, Memoria, D&D world engine) are being removed.

Capabilities:

Flat file ingestion and anomaly detection
Workload tracker scoring and load analysis
SQL schema ingestion, versioning, and drift detection
Live database auditing (read-only, credential-validated)
Audit cycle management with persistent finding memory
Structured report generation (Markdown + JSON)

Run the engine:

cd rell-eco
python run_audit.py --scan-file path/to/data.txt
python run_web.py   # Web interface

Key files:

rell-engine/engine/audit_engine.py — Core audit loop
rell-engine/engine/audit_agent.py — Agent orchestration
rell-engine/engine/workload_engine.py — Workload tracker
rell-engine/engine/llm_integration.py — LLM interface
rell-engine/web/api.py — REST API

rell-workload (Standalone deployment)

The workload tracker has been productized as a standalone module with its own Dockerfile, Fly.io deployment config, and user-facing web UI.

Location: rell-eco/rell-workload/ Entry: JOSEFINA_START_HERE.bat — named for a specific user (Josefina) who is the primary operator

Active Profiles

governance/ccpa-ca.json — California Consumer Privacy Act
governance/gdpr-eu.json — EU General Data Protection Regulation

Audit Data

Cycles: rell-engine/data/audit/memory/cycle_logs/
Reports: rell-engine/data/audit/memory/reports/
Findings: rell-engine/data/audit/memory/finding_logs/
State: rell-engine/data/audit/state/audit_state.json

Development Phases

Phase	Target	Status
1	`rell-engine` standalone, installable	In Progress
2	`rell-profiles` with GDPR-EU as first profile	Planned
3	`rell-gov` — GDPR/CCPA commercial instance	Planned
4	`rell-health` — HIPAA, HL7, FHIR	Planned
5+	`rell-econ`, `rell-infra`, `rell-esg`	Roadmap

Phase 1 exit criteria: Someone with zero Aurelion context can clone rell-engine, install it, drop a flat file in the intake folder, and get an audit report.

License Structure

Component	License	Rationale
`rell-engine`	MIT	Open core drives adoption and trust
`rell-profiles`	BSL	Curated, conflict-resolved rulesets
Domain instances	BSL	Bundled for specific markets

How RELL Relates to Aurelion

RELL extracted its audit engine from Aurelion Nexus Premium. Now they are separate:

Aurelion = cognitive framework (memory, reasoning, planning, collaboration)
RELL = compliance product (watch data, report violations)

They share design philosophy, not code. Same architect, different buyers.

What ships with it: 1 file

544 B alongside SKILL.md

skill-manifest.json544 B

Gives 0 of the 12 instructions most audit compliance skills give in 994 tokens

Counted across 937 of the 1,487 authors here whose files we hold, read 2026-08-07

Fetch latest guidelines before each reviewin 43 of 937, across 3 files
Group findings by severityin 43 of 937
Check files against all fetched rulesin 42 of 937, across 2 files
Output findings in terse file:line formatin 41 of 937, across 3 files
Ask user which files to review if none specifiedin 41 of 937, across 3 files
Read specified files or prompt user for filesin 39 of 937, across 1 file
Generate the audit reportin 33 of 937, across 30 files
Assign a severity to every findingin 25 of 937
Run automated accessibility scansin 23 of 937, across 13 files
Output a markdown audit reportin 22 of 937
Map findings to WCAG criteriain 20 of 937, across 10 files
Confirm audit scopein 19 of 937, across 9 files

Said here and by no other author read

remove all legacy references from the engine
run the engine to scan flat files
maintain persistent audit cycle and finding memory

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.