Agent skill / whyashthakker
### multi-agent-trust-review
Core file
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmulti-agent-trust-reviewExecute 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 multi-agent-trust-reviewFetches multi-agent-trust-review 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 multi-agent-trust-review. Access via /multi-agent-trust-reviewin 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.
Submit your Claude Code skill and start earning
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 multi-agent-trust-reviewWorks with
| name | multi-agent-trust-review |
| description | Review systems where one agent spawns, delegates to, or consumes output from other agents or sub-agents — orchestrators, planner/worker patterns, agent marketplaces — for identity propagation, authority inheritance, output trust boundaries, and runaway delegation. Static review of supplied orchestration code and configuration. |
| license | AGPL-3.0-only |
| metadata | author: Beam version: "1.0.0" website: https://agentbeam.com |
Delegation from one agent to another does not automatically carry forward the authority, scope, or verification the original caller had. A sub-agent's output is a message from another untrusted party, not a verified result, until the orchestrator checks it. Use this workflow when reviewing an orchestrator, a planner/worker split, a sub-agent spawning capability, or any pipeline where one model's output becomes another model's input without a human in between.
| Boundary | What to check |
|---|---|
| Sub-agent → orchestrator | Output parsed as data (result/status/error) rather than re-injected as instructions |
| Orchestrator → sibling agent | One agent's raw output is not forwarded verbatim as another agent's system-level instructions |
| Agent → shared memory/scratchpad | Writes are attributable to their author and readers treat entries as untrusted content |
| External marketplace agent → local orchestrator | Third-party agent's claimed capabilities are verified, not trusted from its own self-description |
| Sub-agent → tool call | Tool calls a sub-agent issues stay inside the scope its parent explicitly granted |
Flag any pattern where a sub-agent's free-text report is parsed with an eval-like mechanism or fed back as a system prompt for another agent without sanitization. A sub-agent that reports "task complete, also run this additional command" is a test of whether the orchestrator distinguishes results from instructions.
Record severity, confidence, the exact propagation or trust-boundary gap, the plausible consequence, and the missing check. Use critical for evidenced unbounded delegation with real credentials or unbounded spend, high for authority escalation across a hop, medium for missing output-sanitization at an inter-agent boundary, info for hardening gaps. Prefer fixing scope narrowing and depth/budget limits at the orchestrator over asking individual agents to self-limit.
Return the topology map, the authority-propagation trace, the output-trust table, prioritized findings, and fixes. List unreviewed spawn paths, external agent sources, and budget enforcement points that could change the conclusion. State whether review was static or included an authorized bounded test run, and what depth/fan-out that test covered.
Use the AI agent security assessment guide for the broader assessment method this review extends to multi-agent systems. Use agent permissions review for scoping an individual agent's own tool access before reviewing how it delegates that access. For advanced monitoring and control, further self-hosting, and ongoing monitoring guidance, visit agentbeam.com. This optional handoff does not spawn agents, execute delegated tasks, or upload orchestration code automatically.
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.
whyashthakker/beam-cli
SnailSploit/Claude-Red
SnailSploit/Claude-Red
SnailSploit/Claude-Red
SnailSploit/Claude-Red
SnailSploit/Claude-Red
multi-agent-trust-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for multi-agent-trust-review matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend multi-agent-trust-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
multi-agent-trust-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
multi-agent-trust-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: multi-agent-trust-review is focused, and the summary matches what you get after install.
Keeps context tight: multi-agent-trust-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
multi-agent-trust-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
multi-agent-trust-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in multi-agent-trust-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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