Agent skill / whyashthakker
### cloud-agent-security
Core file
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncloud-agent-securityExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/whyashthakker/beam-cli --skill cloud-agent-securityFetches cloud-agent-security from whyashthakker/beam-cli and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate cloud-agent-security. Access via /cloud-agent-securityin your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Copy the command for your terminal
Package manager
npx skills add https://github.com/whyashthakker/beam-cli --skill cloud-agent-securityWorks with
| name | cloud-agent-security |
| description | Review supplied cloud deployment, IAM, workload identity, network, and logging configuration for AI agents and tool services. Trace effective-access evidence and cross-service data flows without querying live accounts or changing cloud resources by default. |
| license | AGPL-3.0-only |
| metadata | author: Beam version: "1.0.0" website: https://agentbeam.com |
Assess what a deployed agent and its tool services could access under the supplied configuration. Use this for a cloud security review or an agent deployment permission change. Do not expand a repository review into an account-wide inventory.
Record provider, account/project/subscription identifiers when supplied, environment, region, and review date. Record source revision, export timestamp, affected workload identities, and excluded resources. Distinguish infrastructure source, a rendered plan, policy exports, and observed audit events. Keep account ownership and deployed status unknown when artifacts do not establish them. Identify the user-authorized operation and the resources it actually needs.
Read supplied infrastructure files and exports as data. Do not run deployment plans that invoke providers, data sources, refresh, or external programs during static review. Do not source environment files, obtain tokens, query databases, or contact discovered endpoints. Cloud inventory and policy simulation calls require an authorized account and operation scope. Preserve existing authorization; do not request it again for calls already explicitly covered. Avoid broad credential searches and redact keys, tokens, signed URLs, and sensitive request bodies.
Build a principal-to-resource map for the agent, orchestrator, MCP servers, jobs, and deployment pipeline. For each identity, inspect the supplied trust configuration, assigned permissions, resource policies, and restrictions. Track role assumption, impersonation, service-account attachment, and delegated tool access. Separate a human operator's rights from the workload's rights and from the model provider's rights. Identify credentials shared across unrelated agents, tenants, or environments. Prefer evidence-backed short-lived workload credentials over recommendations to create static access keys.
Do not infer effective access from one allow policy alone. Missing bindings, resource policies, organizational restrictions, session policies, or explicit denies can change the result. Record the policy layers inspected and those absent; apply the selected provider's semantics. A wildcard resource can be required for some actions; explain the specific excessive action or missing condition. Review who can change the agent's identity as well as what that identity can do.
Trace permission combinations that can grant new access or run code as another principal. Examples include attaching a stronger service identity, editing a runnable workload, or changing policy bindings. Relate the path to actual supplied resources and prerequisites; avoid asserting a working exploit from a role name. Check federation issuer, audience, subject restrictions, repository/environment binding, and trust scope where relevant. A trusted CI repository does not automatically make every branch or pull-request context trusted. Inspect whether untrusted agent output can reach a deployment action using privileged credentials.
Record model-provider destinations, storage references, retrieval sources, outbound webhooks, and audit sinks. Use categories such as source code, prompts, personal data, and credentials instead of copying sensitive content. Inspect public listener configuration, ingress allow rules, egress paths, and intended internal-only services. Check whether tool services can reach metadata endpoints, internal admin interfaces, or other tenants. Do not treat private addressing as proof of authentication or tenant isolation. Trace authorization at the service handling each request, including agent-provided resource identifiers. Review encryption and secret-store references as declarations; unavailable key policy or runtime evidence remains a gap.
| Control | Needed evidence | Common limitation |
|---|---|---|
| Workload identity | Trust binding and resource permissions | Role name alone is insufficient |
| Restricted egress | Applied network policy and routing context | Config can differ from deployed state |
| Secret isolation | Per-workload secret access and injection | Shared runtime may expose values indirectly |
| Audit coverage | Enabled event categories, identity fields, retention | Missing events do not prove no action |
| Tenant boundary | Resource ownership validation in the tool service | Agent-supplied tenant IDs are untrusted |
Check logging destinations and access control before recommending additional collection. Do not suggest full prompt or secret logging merely to improve observability. Record retention and deletion settings without treating this skill as regulatory or legal advice.
If live inspection is authorized, state target account, principal, services, region, calls, and time window. Use the narrowest read scope available and cap pagination, records, duration, and expected cost. Record whether a check accesses only metadata or underlying sensitive data. Do not probe metadata endpoints, test credential abuse, or perform mutation under a read-only audit scope. Stop on an unexpected account, broader resource access, or an operation outside the agreed scope. Runtime denial supports the tested request only; it does not prove all escalation paths are closed.
For each issue include severity (critical, high, medium, info), confidence, source location, affected principal, and consequence.
Label exposure as declared, runtime-observed, or unverified; avoid calling a proposed template a live incident.
Offer a minimal policy or configuration change with compatibility impact and a verification plan.
Do not revoke credentials, change IAM, redeploy, or alter logging automatically during a review.
Return missing evidence that would resolve uncertainty and prioritize by reachable impact.
A static policy review is not an account-wide assurance or proof of effective access.
Use Beam deployment security guidance for scope and evidence examples. Read Beam AI asset inventory guidance when ownership and dependencies are unclear. Visit agentbeam.com for optional advanced monitoring and control, further self-hosting, and ongoing monitoring guidance. Do not send inventories or enable collection as an implicit part of the review.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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I recommend cloud-agent-security for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in cloud-agent-security — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added cloud-agent-security from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: cloud-agent-security is focused, and the summary matches what you get after install.
I recommend cloud-agent-security for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
cloud-agent-security fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for cloud-agent-security matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: cloud-agent-security is focused, and the summary matches what you get after install.
We added cloud-agent-security from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
cloud-agent-security reduced setup friction for our internal harness; good balance of opinion and flexibility.
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