No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by aws-deadline · MCP Server · ★ 58
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AWS Deadline Cloud Client User guide Deadline Cloud on GitHub AWS Deadline Cloud client is a multi-purpose python library and command line tool for interacting with and submitting [Open Job Description (OpenJD)][openjd] jobs to [AWS Deadline Cloud][deadline-cloud]. To support building workflows on top of AWS Deadline Cloud, it implements its own user interaction, job creation, file upload/download, and other useful helpers around the service's API. It can function as a pipeline tool, a standalone GUI application, or even be embedded within other applications' runtimes. Notable features include: A command-line interface with subcommands for querying your AWS Deadline Cloud resources, and submitting jobs to your AWS Deadline Cl
| Stars | 58 |
| Forks | 71 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 72.5829087408823/100 |
| Open Issues | 11 |
| Last Updated | 2026-10-02 |
| Created | 2023-08-18 |
| Platforms | aws, mcp, python |
| Est. Tokens | ~18k |
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deadline-cloud is Submit and manage rendering jobs on AWS Deadline Cloud, the AWS managed render farm service, from Python, the command line, or third party software.. It is categorized as a MCP Server with 58 GitHub stars.
deadline-cloud is primarily written in Python. It covers topics such as animation, aws, aws-deadline-cloud.
You can find installation instructions and usage details in the deadline-cloud GitHub repository at github.com/aws-deadline/deadline-cloud. The project has 58 stars and 71 forks, indicating an active community.
deadline-cloud is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to deadline-cloud on Agent Skills Hub include dcc-mcp-maya, sample-getting-started-with-strands-agents-course, claude-context-local. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: