A repository that systematically fetches, archives, tracks, and analyzes the AIUC-1 standard — and distributes role-based Claude Agent Skills for working with it.
AIUC-1 (AI Use Case standard 1) is the world's first security, safety, and reliability standard designed specifically for AI agents. Developed with 100+ Fortune 500 CISOs, it covers 51 requirements across six domains:
| Domain | Description |
|---|---|
| A. Data & Privacy | Input/output data policies, PII protection, IP safeguards |
| B. Security | Adversarial robustness, access controls, endpoint protection |
| C. Safety | Harmful output prevention, pre-deployment testing, monitoring |
| D. Reliability | Hallucination prevention, safe tool calls |
| E. Accountability | Failure plans, vendor due diligence, logging, transparency |
| F. Society | Cyber misuse prevention, catastrophic risk controls |
The standard maps to: ISO 42001 · MITRE ATLAS · EU AI Act · NIST AI RMF · OWASP Top Ten · CSA AICM
This repo ships four installable Claude Agent Skills for AIUC-1 compliance work. Each skill gives Claude a specialized role and embeds the full AIUC-1 spec as a reference document.
# Add this repo's dist/ as a marketplace source
claude plugin marketplace add joncutrer/aiuc-1-context/dist
# Install the skills you need
claude plugin install aiuc-1-assessor@aiuc-1-skills
claude plugin install aiuc-1-auditor@aiuc-1-skills
claude plugin install aiuc-1-implementer@aiuc-1-skills
claude plugin install aiuc-1-advisor@aiuc-1-skillsPre-certification gap analysis. Evaluates an AI system against all applicable AIUC-1 requirements and produces a prioritized remediation plan.
| Command | Description |
|---|---|
/assess |
Full readiness assessment with gap analysis |
/scope |
Determine which requirements apply to your system |
/evidence-checklist |
Generate a checklist of required evidence artifacts |
Conducts formal AIUC-1 audits. Verifies evidence artifacts against controls, checks review frequency compliance, and produces structured findings with a certification recommendation.
| Command | Description |
|---|---|
/audit |
Formal audit report with certification recommendation |
/verify |
Verify a specific evidence artifact against a control |
Engineering-focused guidance for building AIUC-1 controls. Provides concrete technical patterns, policy templates, and evidence artifact generation.
| Command | Description |
|---|---|
/implement |
Implementation guidance for a specific requirement (by ID) |
/draft-policy |
Draft a policy document that satisfies one or more requirements |
Strategic compliance consulting. Produces roadmaps, vendor questionnaires, framework crosswalk mappings, and executive briefings.
| Command | Description |
|---|---|
/roadmap |
Phased compliance roadmap (0–90 days, 90–180 days, ongoing) |
/vendor-questionnaire |
Vendor assessment questionnaire based on AIUC-1 requirements |
/crosswalk |
Map AIUC-1 to ISO 42001, EU AI Act, NIST AI RMF, MITRE ATLAS, etc. |
/exec-briefing |
Executive compliance briefing (leadership, customer, or regulatory) |
aiuc-1-context/
├── skills/ # Authored skill source files (edit these)
│ ├── aiuc-1-assessor/
│ │ ├── SKILL.md
│ │ └── commands/
│ ├── aiuc-1-auditor/ (same structure)
│ ├── aiuc-1-implementer/ (same structure)
│ └── aiuc-1-advisor/ (same structure)
│
├── dist/ # Built output — commit and distribute from here
│ ├── .claude-plugin/
│ │ └── marketplace.json
│ └── aiuc-1-{assessor,auditor,implementer,advisor}/
│ ├── SKILL.md
│ ├── commands/
│ └── references/
│ └── aiuc-1-spec.md
│
├── src/ # Automation scripts
│ ├── fetch_spec.py # Fetch a quarterly spec version from aiuc-1.com
│ ├── diff_specs.py # Compare two spec versions
│ ├── fetch_news.py # Harvest news/research articles
│ ├── build_ai_context.py # Compile all spec data into AI-optimized context
│ ├── build_skills_dist.py # Assemble skills bundle into dist/
│ └── run_periodic.py # Orchestrate all tasks in order
│
└── data/
├── spec-versions/ # Raw spec snapshots (one folder per quarter)
├── changelog/ # Per-quarter changelogs
├── spec-diffs/ # Structured diffs between releases
├── ai-context/ # AI-optimized single source of truth
└── news/ # Monthly news/research digests
Prerequisites: Python 3.11+ and uv
# Install dependencies
uv sync
# Run everything (fetch → diff → build context → build skills)
uv run src/run_periodic.py
# Or run individual steps
uv run src/fetch_spec.py 2026-Q1 # Fetch a specific spec version
uv run src/build_ai_context.py # Rebuild the AI context doc
uv run src/build_skills_dist.py # Rebuild dist/ from skills/
uv run src/fetch_news.py # Harvest latest news articles
uv run src/diff_specs.py 2025-Q1 2026-Q1 # Diff two versionsTypical cadence:
- Run
run_periodic.pyquarterly when a new AIUC-1 release is announced - Run
fetch_news.pymonthly to keep news digests current
- Edit the source files in
skills/<skill-name>/SKILL.mdorskills/<skill-name>/commands/<command>.md - Rebuild:
uv run src/build_skills_dist.py - The updated
dist/is ready to install from
Never hand-edit files in
dist/— they are always overwritten by the build script.
aiuc-1.com → fetch_spec.py → data/spec-versions/YYYY-QN/
↓
build_ai_context.py → data/ai-context/aiuc-1-context-latest.md
↓
build_skills_dist.py → dist/aiuc-1-*/
├── SKILL.md (from skills/)
├── commands/ (from skills/)
└── references/aiuc-1-spec.md (injected)
The AI context document (aiuc-1-context-latest.md) is LLM-optimized: nav menus, marketing content, and duplicate text are stripped. It contains a requirements table with keywords, controls & evidence, domain descriptions, and framework crosswalk content — all in a single dense document.
Apache 2.0