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.
git clone --depth 1 https://github.com/alexclowe/awesome-claude-cowork-pluginsWrote 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.
[](https://agentmods.dev/commands/alexclowe/awesome-claude-cowork-plugins/assess-ai-risk-client-systems)<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/github.svg" alt="Measured on agentmods" height="20"></a>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.
<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>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.
| Model | Per session | Once 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 |
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.
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]
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.
- 12d ago First seen · 104 lines · 26 tokens per session scan A 6756c7ea4227
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.
Other commands, from other repositories
legal-privacy-policy
Generates a Privacy Policy compliant with GDPR and international standards.
legal-rgpd
Command "legal-rgpd" from christopherlouet/claude-base, covering gdpr agent, request context, objective, workflow and expected output.
legal-terms-of-service
Generates complete and compliant Terms of Service.
legal-goal
Define a checkable legal success condition for /legal-loop. Accepts a named profile (citations-clean, draft-passes-gate, adversarial-converge, nda-batch-clean, reg-watch, timeline-sourced) or free-text objective. Produces a persisted Goal Record — never starts work itself.
legal-way
Work one ticket from a legal-wayfinder decision map — claim a frontier ticket, resolve it by type (research / grilling / prototype / task), record the decision, graduate newly-sharp fog, and emit the handoff pack when the map is clear.
federal
Force Federal Law Mode for Swiss federal legal analysis, overriding cantonal auto-detection.