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The GRC Companion

A learning companion for GRC practitioners, built from the original GRC Engineering Lab Builder.

The companion helps users learn, practise, and internalise GRC engineering thinking from both synthetic labs and their actual day-to-day GRC work. In local adapters, the learner can intentionally point it at review notes, drafts, files, diffs, terminal output, questionnaires, policies, controls, and other work artefacts so the Companion can extract learning opportunities.

It is not an operational GRC decision-maker: it does not approve vendors, certify audit positions, author final production policies, run controls, or operate a user's programme. It helps the practitioner get better at doing those things.

Public Surface

Target URL:

https://grc.engineering/companion/

Legacy project-site URL:

https://grc.engineering/grc-companion/

Because this is hosted on GitHub Pages, the public website stays static. It is an installer and orientation wizard, not the Companion runtime.

Architecture

grc-companion/
├── brain/          Companion persona, voice, learning method, primitives
├── knowledge/      Structured corpus and case studies for citation grounding
├── profile/        Learner-owned profile schema and examples
├── skills/         Learning skills only
├── commands/       Portable learning-only command contracts
├── demos/          Behavioural examples and terminal transcripts
├── webapp/         Tier 0 product surface notes/source spec
├── adapters/       Tier 1 and Tier 2 distribution targets
├── docs/           GitHub Pages deployable static landing page
└── scripts/        Corpus and adapter build entry points

Tiers

Tier 0: Static Setup Wizard

The first shipped surface. Static and GitHub Pages-compatible. It helps a learner pick Claude Code, Claude Projects, Cursor, Codex, or the raw files, then generates setup instructions and a system prompt.

Tier 1: Portable Skill Pack

Platform-specific adapters: Claude Code, Codex, Cursor, and Claude Projects.

Tier 2: PAI Pack

Roadmap. Ships only after Tier 0 has active learners and Tier 1 stabilises.

Sprint 1 Scope

  • Repo restructured around brain, knowledge, skills, webapp, and adapters
  • Companion brain populated with persona, voice, learning method, and primitives
  • Existing lab-builder prompts migrated into skills/lab-builder
  • Profile wizard and refresher skills added for learner-owned context
  • SOC 2 parsing example migrated into knowledge/case-studies
  • Tier 0 static setup wizard scaffolded in docs

Redesign Scope

  • Skills are invisible by default through brain/skill-router.md.
  • task-retrospective extracts learning from work the learner already completed.
  • cross-domain-translator uses adjacent-domain patterns to clarify GRC concepts.
  • The website routes users toward the right package and system prompt instead of pretending to run the Companion.
  • Local adapters can learn from real local work artefacts the learner intentionally provides; the public website does not upload those artefacts.
  • The actual Companion behaviour runs through generated adapters and canonical repo contracts.

Deferred:

  • Real signed-in cross-device profile
  • Scheduler/reminder infrastructure
  • Tier 1 adapters
  • Tier 2 PAI Pack
  • Operational GRC skills

Local Preview

Open docs/index.html directly in a browser, or serve the folder with any static server:

python3 -m http.server 8080 --directory docs

Then visit:

http://localhost:8080/

Build Derived Context

The canonical files live in brain/, skills/, commands/, and knowledge/. Generated surfaces are rebuilt with:

scripts/build-knowledge-index
scripts/build-webapp-context
scripts/build-adapters
scripts/verify-structure
scripts/run-evals

Generated outputs:

  • knowledge/index.json
  • docs/data/companion-context.json
  • dist/adapters/

Behaviour Demo

Run the terminal transcript:

demos/run-terminal-demo

The demo shows the expected boundary pattern: the Companion can use real local review notes as learning material, but it does not make the vendor approval decision.

Non-Goals

The companion refuses operational work. It may teach a user how to think through a vendor review, audit prep pattern, policy design principle, or trust-center architecture, but it should not perform those activities on behalf of the user.

About

A fully customisable and personalised lab builder which is tailored to your context, experience, current technical literacy, objectives and time allocation. You can use it in any LLM/AI chat app and build amazing custom learning paths which will be extremely specific and tailored to you.

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