/ai-inventory

When this runs

The user wants to manage their AI system inventory under the EU AI Act. The core idea the skill exists to enforce: role and tier are per-system, not per-company. A single organization can be a provider of System A, a deployer of System B, and an importer of System C. Each combination triggers a different set of obligations under the AI Act. The inventory exists so those assessments are tracked where you can find them — the obligations themselves are derived in conversation, not from a table.

What to do

Read the config. Read

~/.claude/plugins/config/claude-for-legal/ai-governance-legal/CLAUDE.md

. If it doesn't exist or still has

[PLACEHOLDER]

markers, direct the user to

/ai-governance-legal:cold-start-interview

first.

Read the inventory. Inventory lives at
```
~/.claude/plugins/config/claude-for-legal/ai-governance-legal/ai-systems.yaml
```
. If it doesn't exist, create it with an empty
```
systems:
```
list when the first
```
add
```
runs.
Dispatch on the argument:
- No argument, or
```
list
```
  → show the inventory table (see List below).
- ```
add
```
  → run the Add flow.
- ```
edit <id>
```
  → show the current record, ask what to change, update one field, confirm, write.
- ```
classify <id>
```
  → run the Classification walk-through on an existing record, updating role, tier, role_basis, and tier_basis.
- ```
show <id>
```
  → show the full record.
On list, offer the dashboard: "Want the full dashboard? Filter by status / tier / EU nexus / owner. Say the word."
Close every action with a hook into the lawyer's work. After any write, say:

Recorded. When you're ready to walk through obligations for this system, just ask — I'll do it in-conversation and flag where the AI Act article mapping needs your verification. I don't derive obligations from a table because the mapping is complex and changing.

List format

Render as a compact table:

ID	Name	Owner	Status	EU nexus	Role	Tier	Next review
sys-001	Resume screening	HR / Jamie	in_production	yes	deployer	high_risk	2026-08-01
sys-002	Email drafting assistant	IT / Priya	in_production	no	deployer	limited	2026-12-01

Under the table, show counts by tier and a line: "N systems flagged for review within 30 days."

Add flow (interview)

Ask, one field at a time (or accept a paste). The required fields are

name

owner

description

status

eu_nexus

. The rest can be deferred — say so explicitly: "you can come back to classification with

/ai-governance-legal:ai-inventory classify <id>

Name. Short label for the system.
Owner. Person or team accountable for it day-to-day.
Description. One or two sentences. What does it do, and against what data?

Status.

planned | in_development | in_production | deprecated

EU nexus. Is the system deployed in the EU/EEA, offered to users in the EU/EEA, or used to produce outputs that affect people in the EU/EEA? If any of these are true, EU AI Act analysis applies.
Proceed to classification? Offer to run the walk-through now, or skip and come back later.

Assign an ID:

sys-NNN

where NNN is the next integer in the file.

Classification walk-through

The walk-through produces

role

role_basis

tier

tier_basis

. Both bases are tagged

[verify against current AI Act text]

— not because the skill is hedging, but because the article mapping is complex and the AI Act is still phasing in. The lawyer owns verification.

Step 1: Role

Who does what to this system?

Options, with the distinguishing test:

Provider — you develop it (or have it developed) and place it on the EU market or put it into service under your own name or trademark.
Deployer — you use it under your own authority, not for personal non-professional use. (Most common inside companies.)
Importer — you bring an AI system into the EU from a provider established outside the EU.
Distributor — you make an AI system available on the EU market without being the provider or importer.
Authorized representative — you act on behalf of a non-EU provider and are established in the EU.
Product manufacturer — you put a general-purpose AI system (or another AI system) into a product under your own name/trademark. Treated as provider for the product.

Dual-role flag. If the user substantially modifies a vendor system (fine-tunes on their own data, changes the intended purpose, rebrands), they may become a provider of the modified system even if they started as a deployer. Call this out when they describe any modification beyond configuration.

[verify against current AI Act text — Article 25, provider obligations and substantial modification]

Write the role. Write

role_basis

in one sentence.

Step 2: Tier

What does the system do, and does the use case fall into a regulated category?

Check in order:

A. Article 5 prohibited practices.

[verify against current AI Act text — Article 5]

Summaries, not definitive text:

Subliminal or deceptive techniques materially distorting behavior
Exploiting vulnerabilities (age, disability, socio-economic status) to materially distort behavior
Social scoring by public authorities leading to detrimental treatment
Real-time remote biometric ID in publicly accessible spaces for law enforcement (narrow exceptions)
Biometric categorization inferring race, political opinions, union membership, religious or philosophical beliefs, sex life, or sexual orientation
Emotion recognition in the workplace or education (medical and safety exceptions)
Facial image database scraping from the internet or CCTV
Predictive policing based solely on personality traits

If matched → tier is

prohibited

. Flag the use case as stop and route to the governance team's prohibited-practice workflow.

B. Annex III high-risk areas.

[verify against current AI Act text — Annex III]

Summaries:

Biometric identification and categorization
Critical infrastructure (digital infrastructure, road traffic, supply of water / gas / heating / electricity)
Education and vocational training (access, evaluation, proctoring, monitoring prohibited behavior)
Employment, worker management, self-employment access — recruitment, selection, promotion, termination, task allocation, monitoring, performance
Essential private and public services (public benefits, credit scoring for individuals, risk assessment and pricing for life/health insurance, emergency dispatch)
Law enforcement (risk assessment, polygraphs, deepfake detection, reliability of evidence, profiling)
Migration, asylum, border control (risk assessment, travel document verification, examination of applications)
Administration of justice and democratic processes (research and interpretation, influencing elections)

If matched → tier is

high_risk

. Note the Annex III area and subsection.

C. GPAI.

[verify against current AI Act text — Article 51 and surrounding]

GPAI: model trained on broad data at scale, designed for generality, capable of competently performing a wide range of distinct tasks.
GPAI + systemic risk: cumulative compute > 10^25 FLOPs, or designated by the Commission.

D. Limited risk. Chatbots interacting with natural persons, deepfakes, emotion recognition and biometric categorization systems outside Article 5 scope — transparency obligations apply.

E. Minimal risk. Everything else.

Write the tier. Write

tier_basis

in one sentence, citing the article or Annex entry that matched, tagged

[verify against current AI Act text]

Step 3: Recommendations

Offer three next steps:

"Want me to walk through obligations for this system? I'll do it in conversation — I don't derive them from a table."
"Want to run
```
/ai-governance-legal:aia-generation
```
to produce a full impact assessment?"
"Want to set a next review date? I'll add it to the inventory."

Record format

yaml

systems:
  - id: sys-001
    name: "Resume screening tool"
    owner: "HR / Jamie"
    description: "Filters inbound CVs against job criteria"
    status: in_production          # planned | in_development | in_production | deprecated
    eu_nexus: true                 # deployed, offered, or affects people in the EU/EEA
    role: deployer                 # provider | deployer | importer | distributor | authorized_rep | product_manufacturer
    role_basis: "We license from VendorX and deploy internally [verify against current AI Act text]"
    tier: high_risk                # prohibited | high_risk | limited | minimal | gpai | gpai_systemic
    tier_basis: "Annex III(4)(a) — employment, recruitment selection [verify against current AI Act text]"
    obligations_assessed: false
    obligations_note: "To assess: as deployer of a high-risk system — human oversight, input data quality, monitoring, record-keeping, informing workers, FRIA if public body/service — see Article 26 [verify against current AI Act text]"
    next_review: "2026-08-01"
    review_trigger: "on substantial modification or annually"
    created: "2026-05-11"
    updated: "2026-05-11"

Why this skill does NOT auto-derive obligations

The inventory stores role, tier, and the basis for each. It does NOT contain a hardcoded role × tier → obligations table.

When the user asks "what are my obligations for System X?", the skill does the analysis in conversation, tagged

[verify]

, and routes to

/ai-governance-legal:aia-generation

for the formal impact assessment if needed.

This is deliberate:

Article mapping is complex and the AI Act is phasing in through 2027.
Confident-and-wrong on a compliance obligation ends up in a board memo.
The inventory is a registry for the lawyer. The lawyer owns the obligation analysis.

Guardrails

Never classify silently. The classification walk-through must be visible; do not auto-classify from a system description.
[verify]
tags stay. They are not hedging — they are the point. Do not strip them in outputs.
Flag substantial modification. Whenever a system is modified beyond configuration, prompt the user to re-run
```
/ai-inventory classify
```
— modification can change role.
Don't declare obligations from a table. If asked, do the analysis in conversation and route to
```
/aia-generation
```
for anything that needs a formal record.

ai-inventory

NPX Install

Tags

SKILL.md Content