How do I install the Aims Audit skill?

Run npx skillmds@latest add borghei/aims-audit in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Aims Audit skill do?

ISO 42001 AI Management System (AIMS) audit-prep playbook. Use when an ISO 42001 certification audit is scheduled (Stage 1 or Stage 2), when a surveillance or internal AIMS audit is due, or when preparing an AI Impact Assessment (AIIA). It is listed under Security on SkillMD.

Is Aims Audit safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Aims Audit?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Aims Audit free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT + Commons Clause.

Who published Aims Audit?

borghei (@borghei) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Aims Audit by borghei — Agent Skill

Name: Aims Audit
Rating: 4.5 (4 reviews)
Author: borghei

AIMS Audit Prep (ISO 42001)

Operational playbook for ISO 42001:2023 AI Management System (AIMS) audit preparation. Whether targeting initial certification, surveillance audit, or annual internal audit.

When to use this skill vs. iso42001-ai-management:

This skill: audit imminent; need readiness sprint
iso42001-ai-management: building AIMS from scratch; multi-quarter program

When to use this skill

Situation	Skill applies
ISO 42001 Stage 1 audit scheduled	Yes — documentation review prep
Stage 2 (onsite/operational) audit	Yes — operational evidence sprint
Annual surveillance audit	Yes — surveillance prep
Internal AIMS audit	Yes — internal audit playbook
AI Impact Assessment for new system	Yes — `scripts/ai_impact_assessment_checker.py`
Building AIMS from scratch	Use `ra-qm-team/iso42001-ai-management`

ISO 42001 audit structure

Stage 1 (documentation review)

Auditor reviews AIMS documentation (typically 1-3 days)
Confirms scope, applicability, key documents present
Identifies gaps before Stage 2
Typically 2-3 weeks before Stage 2

Stage 2 (operational assessment)

Auditor onsite (or remote) verifies AIMS operating effectively
3-10 days depending on scope + system count
Walkthroughs, interviews, evidence sampling
Conclusion: certification recommended (subject to non-conformity closure) or not

Surveillance audits

Annual; reduced scope vs initial
Typically 1-3 days
Focus on high-risk areas + changes since prior audit

Re-certification (year 3)

Full audit; similar to initial
Typically every 3 years

AIMS audit-prep sprint

4-week sprint (mature AIMS, surveillance audit)

Week 1: Internal audit + gap analysis
Week 2: Remediation + AI inventory refresh
Week 3: Documentation review + walkthrough rehearsal
Week 4: Auditor onsite

8-week sprint (Stage 1 + Stage 2 initial certification)

Weeks 1-3: AIMS documentation completion (Annex A controls coverage)
Weeks 4-5: Stage 1 audit + gap closure
Weeks 6-7: Stage 2 operational evidence prep + mock walkthroughs
Week 8: Stage 2 audit

ISO 42001 clauses + Annex A controls

Clauses (4-10): management system

Clause	Topic
4	Context of the organization
5	Leadership
6	Planning (including AI risk + AI objectives)
7	Support (resources, competence, awareness, communication, documentation)
8	Operation (AI lifecycle, supplier relationships)
9	Performance evaluation (monitoring, internal audit, management review)
10	Improvement (nonconformity, continual improvement)

Annex A controls (10 areas)

Annex A area	Topics
A.2	Policies related to AI
A.3	Internal organization
A.4	Resources for AI systems
A.5	Assessing impacts of AI systems
A.6	AI system lifecycle
A.7	Data for AI systems
A.8	Information for interested parties
A.9	Use of AI systems
A.10	Third-party relationships

Critical audit areas

AI Inventory + Impact Assessments

Item	Evidence	Common gap
Complete AI system inventory	Inventory document	Shadow AI not captured
AI Impact Assessment (AIIA) per system	Per-system AIIA	Skipped for "low-risk" systems
AIIA reviewed periodically	Review records	One-time only
Risk classification of systems	Per system	Not documented

AI Policy + Governance

Item	Evidence	Common gap
AI policy approved + dated	Signed policy	Not signed / stale
AI ethics principles	Documented principles	Generic; not actionable
AI governance body	Charter / minutes	Not formalized
Roles + responsibilities	RACI	Not defined

AI Lifecycle Management (Annex A.6)

Item	Evidence	Common gap
AI development lifecycle defined	Process documentation	Not formalized
Data quality controls	Per system	Generic only
Model validation procedures	Per system	Validation skipped
AI system testing	Per system	Inadequate testing
Deployment controls	Per system	No controls
Operational monitoring	Per system	Drift not monitored
Decommissioning procedures	Per system	Not defined

Data Governance (Annex A.7)

Item	Evidence	Common gap
Data sources documented	Per system	Vague
Data quality assessed	Quality metrics	Not measured
Data lineage tracked	Documentation	Untracked
Sensitive data protection	Controls	Insufficient

Third-party AI (Annex A.10)

Item	Evidence	Common gap
Third-party AI inventory	List	Incomplete
Vendor due diligence for AI	Per vendor	Generic IT only
Contract terms for AI vendors	AI-specific clauses	Standard MSA only

Clarify First

Before running the audit-prep, confirm these inputs. If any is unknown or vague, ASK — do not assume:

Audit type and stage — Stage 1 documentation review, Stage 2 operational, surveillance, or internal (sets sprint length and whether the focus is documentation or operational evidence)
AIMS maturity — mature system vs building from gaps (picks the 4-week vs 8-week sprint)
AI systems in scope — which systems and how many (drives the AIIA count and Annex A coverage)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the readiness assessment.

Quick start

Run readiness score: python3 scripts/aims_readiness_score.py --config aims-controls.yaml
Check AIIA per system: python3 scripts/ai_impact_assessment_checker.py --aiia system-aiia.yaml
Pick sprint length based on score
Execute sprint per references/iso42001-aims-readiness-checklist.md

Common AIMS audit findings

AI inventory incomplete — shadow AI not captured
AIIA missing for systems that look "small" but have impact
AI lifecycle process not implemented (designed only)
Data governance generic — not AI-specific
Performance monitoring missing — drift not detected
AI policy stale — written before current AI deployment
Third-party AI vendors not assessed
Continual improvement not evidenced

Tooling

Script	Purpose
`scripts/aims_readiness_score.py`	Score AIMS readiness per clause + Annex A area
`scripts/ai_impact_assessment_checker.py`	Validate AI Impact Assessment completeness

References

iso42001-aims-readiness-checklist.md — punch list per clause + Annex A
aims-internal-audit-playbook.md — internal audit execution playbook

Related skills

ra-qm-team/iso42001-ai-management — deep ISO 42001 AIMS program management
ra-qm-team/eu-ai-act-specialist — EU AI Act regulatory companion
ra-qm-team/audit-prep/ai-act-readiness — AI Act audit-prep variant
ra-qm-team/audit-prep/compliance-readiness — multi-framework readiness

Aims Audit

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