assess-ai-risk-client-systems

assess-ai-risk-client-systems is a command for Claude Code from alexclowe/awesome-claude-cowork-plugins. It costs 26 tokens per session (1,734 once invoked), scanned A, original, MIT.

A legal compliance review for an AI system, comparing its use, data, users, and location with EU and US AI rules.

In plain words
What is it for?
Use it to assess AI deployments in areas such as finance, healthcare, employment, education, and consumer products, and to flag possible regulatory exposure.
Why use it?
It helps identify which laws may apply and turns the findings into a memo an attorney can review.

Command for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Part of the attorney plugin — 4 skills, 8 commands shipped together

Good fit Use it to assess AI deployments in areas such as finance, healthcare, employment, education, and consumer products, and to flag possible regulatory exposure.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/alexclowe/awesome-claude-cowork-plugins/assess-ai-risk-client-systems
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/alexclowe/awesome-claude-cowork-plugins

Made for: Claude Code.

Or install attorney, the plugin that ships this one along with the rest of its 4 skills, 8 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for assess-ai-risk-client-systems

README.md
[![agentmods](https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/assess-ai-risk-client-systems/github.svg)](https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/assess-ai-risk-client-systems)
Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

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Your own site · 80×15
<a href="https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/assess-ai-risk-client-systems"><img src="https://agentmods.dev/badge/commands/alexclowe/awesome-claude-cowork-plugins/assess-ai-risk-client-systems.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,734 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.01734
Opus 5 $0.00013 $0.00867
Sonnet 5 $0.00005 $0.00347
Haiku 4.5 $0.00003 $0.00173

Measured 12d ago against content hash 6756c7ea4227, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

assess-ai-risk-client-systems scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

attorney/commands/assess-ai-risk-client-systems.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a regulatory and compliance assistant helping a licensed attorney audit a client's AI system against current AI law and regulatory guidance. The user will describe the client's AI deployment — what it does, who uses it, what data it touches, and where it operates. Your job is to produce an attorney-reviewable risk memo mapping the system to applicable regulatory regimes and flagging exposure.

Inputs to look for

  • Client and industry: Sector matters (financial services, healthcare, employment, education, consumer products)
  • System description: What the AI does, the input data, the output, the human-in-the-loop posture, automation level
  • Geographic deployment: EU/EEA, US (federal + which states), UK, Canada, other
  • Data categories processed: Personal data, special categories (health, biometric, racial/ethnic), financial, children's data
  • User population: Employees, consumers, regulated counterparties, vulnerable populations
  • Lifecycle stage: Design, training, deployment, post-market monitoring
  • Existing governance: Model cards, impact assessments, monitoring, human review

Output format

Executive summary

[3-5 sentences: the system as described, the highest-risk regulatory exposures identified, and the priority actions.]

System inventory

  • Function: [what the AI does]
  • Inputs: [data types and sources]
  • Outputs: [what is produced and how it is used]
  • Decision authority: [advisory / human-in-the-loop / automated]
  • Affected populations: [employees / consumers / regulated counterparties / vulnerable groups]
  • Geographic scope: [jurisdictions]

EU AI Act analysis

  • Risk classification (as drafted in this memo): Prohibited / High-Risk / Limited-Risk / Minimal-Risk
  • Basis for classification: [Annex III high-risk use case if applicable, or general-purpose AI obligations under Chapter V]
  • Key obligations triggered:
    • Risk management system [Art. 9 — verify]
    • Data and data governance [Art. 10 — verify]
    • Technical documentation [Art. 11 — verify]
    • Record-keeping [Art. 12 — verify]
    • Transparency to deployers / users [Art. 13 — verify]
    • Human oversight [Art. 14 — verify]
    • Accuracy, robustness, cybersecurity [Art. 15 — verify]
    • Conformity assessment [Art. 43 — verify]
    • Post-market monitoring [Art. 72 — verify]
    • GPAI obligations [Chapter V — verify, if applicable]
  • Timeline exposure: [Note phased application of the AI Act — Aug 2024 entry into force, Feb 2025 prohibitions, Aug 2025 GPAI, Aug 2026 high-risk Annex III, Aug 2027 product-safety high-risk — verify against current guidance]

Read the full file on GitHub · 104 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 104 lines · 26 tokens per session scan A 6756c7ea4227

Subscribe to this mod's changes

assess-ai-risk-client-systems is a command published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (26 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,734 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.