Ai Inventory logo

Ai Inventory

OrganizationPopular
anthropics
ai-inventory

EU AI Act per-system inventory — track each AI system's role (provider, deployer, importer, distributor, authorized representative, product manufacturer) and risk tier (prohibited, high-risk, limited, minimal, GPAI, GPAI+systemic). Role and tier are assessed per system, not per company. Use when the user says "ai inventory", "add an ai system", "what systems do we have", "classify this ai system", "eu ai act register", or "ai system registry".

Overview

Publisheranthropics
Repositoryclaude-for-legal
Skill nameai-inventory
Stars
9.5K
Forks
1.8K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Ai Inventory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/anthropics/claude-for-legal.git /tmp/claude-for-legal
mkdir -p .claude/skills
cp -r /tmp/claude-for-legal/ai-governance-legal/skills/ai-inventory .claude/skills/ai-inventory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Inventory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Inventory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Inventory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/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

  1. 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.

  2. 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.

  3. 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.
  4. On list, offer the dashboard: "Want the full dashboard? Filter by status / tier / EU nexus / owner. Say the word."

  5. 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:

IDNameOwnerStatusEU nexusRoleTierNext review
sys-001Resume screeningHR / Jamiein_productionyesdeployerhigh_risk2026-08-01
sys-002Email drafting assistantIT / Priyain_productionnodeployerlimited2026-12-01

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>."

  1. Name. Short label for the system.
  2. Owner. Person or team accountable for it day-to-day.
  3. Description. One or two sentences. What does it do, and against what data?
  4. Status. planned | in_development | in_production | deprecated.
  5. 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.
  6. 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:

  1. Biometric identification and categorization
  2. Critical infrastructure (digital infrastructure, road traffic, supply of water / gas / heating / electricity)
  3. Education and vocational training (access, evaluation, proctoring, monitoring prohibited behavior)
  4. Employment, worker management, self-employment access — recruitment, selection, promotion, termination, task allocation, monitoring, performance
  5. Essential private and public services (public benefits, credit scoring for individuals, risk assessment and pricing for life/health insurance, emergency dispatch)
  6. Law enforcement (risk assessment, polygraphs, deepfake detection, reliability of evidence, profiling)
  7. Migration, asylum, border control (risk assessment, travel document verification, examination of applications)
  8. 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:

  1. "Want me to walk through obligations for this system? I'll do it in conversation — I don't derive them from a table."
  2. "Want to run /ai-governance-legal:aia-generation to produce a full impact assessment?"
  3. "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.

Frequently asked questions

What does the Ai Inventory AI skill do?

EU AI Act per-system inventory — track each AI system's role (provider, deployer, importer, distributor, authorized representative, product manufacturer) and risk tier (prohibited, high-risk, limited, minimal, GPAI, GPAI+systemic). Role and tier are assessed per system, not per company. Use when the user says "ai inventory", "add an ai system", "what systems do we have", "classify this ai system", "eu ai act register", or "ai system registry".

Why use Ai Inventory on TypingMind?

Because you install it once and use it with any model. Ai Inventory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Inventory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/claude-for-legal/tree/main/ai-governance-legal/skills/ai-inventory. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ai Inventory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Inventory?

As many as you like. As long as a model supports skills, you can use Ai Inventory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Inventory AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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