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secure-agent-design

Security architecture patterns for personal AI agents that touch real accounts. Use when designing an agent with access to inbox, calendar, finances, or shopping. Covers per-user isolated VMs, pre-action sentinel checks, least-privilege connectors, human approval gates, and single-use payment credentials.

Qu'est-ce que secure-agent-design ?

secure-agent-design is a Claude Code agent skill that security architecture patterns for personal AI agents that touch real accounts. Use when designing an agent with access to inbox, calendar, finances, or shopping. Covers per-user isolated VMs, pre-action sentinel checks, least-privilege connectors, human approval gates, and single-use payment credentials.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/unisone/openclaw-skill-suite/tree/main/skills/secure-agent-design

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Documentation

Que fait secure-agent-design ?

Distilled from the security architecture Meta disclosed for Muse (Sept 2026): each agent runs in its own secure VM, a separate "sentinel" system checks every action before it leaves the VM, and the agent never sees real passwords or card numbers. Security framed as architecture, not promises, was the adoption unlock.

The patterns

Per-user isolated compute

Each user's agent runs in its own virtual machine: an isolated computer dedicated to that user. Compromise of one agent cannot reach another user's data. The VM keeps working in the background even when the user is away.

Sentinel: pre-action verification

A separate system checks every action before anything leaves the VM. The agent proposes; the sentinel verifies against policy (allowed apps, spending limits, sensitivity rules) before execution. No action with side effects bypasses this gate.

Credential isolation

The agent never sees actual passwords or card numbers. Purchases go through single-use virtual card numbers issued per transaction, so the real payment credentials never enter the agent's context and can't leak through prompts, logs, or model outputs.

Human approval gates

The agent asks before sensitive actions: sending an email, spending money, deleting data. Approvals should be specific (this action, this amount, this recipient), not blanket. Routine low-risk actions can run autonomously once the user opts in.

Least-privilege connectors

The user decides exactly what the agent can touch: which apps are connected, whether access is read or read-write, and can revoke anytime. Default to the narrowest scope that completes the job; expand only on explicit request.

Transparency

The user can see everything the agent has done: a complete, human-readable action log. Include "forget" controls so users can delete what the agent has learned about them.

Messaging it

Lead with the architecture ("runs in its own secure VM, every action checked before it leaves"), not with adjectives ("bank-grade," "military-grade"). Name the components (VM, sentinel, single-use cards) so technical users can verify the claims. Acknowledge the tradeoff honestly: broader access means higher stakes, which is exactly why the architecture exists.

Individual skills in this repo

This repo contains 10 individual skills — each has its own dedicated page.

astra-operator

Delegate computer-use and browser tasks to OpenAI

design-inspo

Find design inspiration from curated gallery sites. Use when a user needs UI/UX references, design examples, or inspiration for specific components (navbars, CTAs, hero sections, landing pages, etc.), full websites, SaaS products, mobile apps, animations, icons, branding, or design systems. Helps select the right inspiration source and browse examples.

graphrag

Build a GraphRAG pipeline over documents. Use when plain vector search returns chunks without connections, when questions need multi-hop reasoning (

money-challenge

Design a public economic-outcome challenge for an AI product launch. Use when you want measurable proof that your agent creates or saves money. Covers the #musemoneychallenge mechanic: time-boxed, hashtag-tracked, user-submitted dollar outcomes with verification discipline.

mundane-demos

Demo strategy that leads with mundane real-life utility instead of benchmarks. Use when presenting an AI agent or product to non-technical audiences. Covers picking tasks the viewer did this morning, showing end-to-end completion, and letting speed be the wow factor.

release-notes

Generate polished release notes from git history. Use when cutting a release, before publishing a GitHub release, or when you need a changelog from commits between two tags. Groups changes by type, surfaces breaking changes, and drafts highlights.

remotion-product-demos

Create Apple-keynote-quality product demo videos with Remotion. Covers glass phone mockups, floating 3D spheres, typing animations, card UIs, ripple effects, and smooth scene transitions. Use when asked to create product demos, app showcase videos, UI walkthrough animations, or

repo-security-scan

Scan a repository for high-signal security issues and produce a report. Use when auditing a codebase for leaked secrets or vulnerable dependencies, before a release, or when onboarding an unfamiliar repo. Runs gitleaks secret scanning and osv-scanner dependency checks.

testimonial-launch

Turn user testimonials into launch distribution. Use when launching a product or feature and you want real user proof to carry the announcement instead of marketing copy. Covers soliciting specific testimonials, curating them, and amplifying via quote-posts.

validator-quotes

Earn and deploy third-party validator quotes for a launch. Use when you need credibility beyond your own claims. Covers who to approach, how to get honest reactions, and how to amplify them via quote-posts, based on the founder/CEO validators behind the Muse launch.

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