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by awslabs · MCP Server · ★ 456
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IAM Policy Autopilot An open source Model Context Protocol (MCP) server and command-line tool that helps your AI coding assistants quickly create baseline IAM policies that you can refine as your application evolves, so you can build faster. IAM Policy Autopilot analyzes your application code locally to generate identity-based policies for application roles, enabling faster IAM policy creation and reducing access troubleshooting time. IAM Policy Autopilot supports applications built in Python, Go, and TypeScript. We want to hear from you. Ask questions or share ideas in Discussions, report bugs through Issues, or contribute directly with a Pull Request. Table of Contents Who is IAM Policy Autopilot for? [How is
| Stars | 456 |
| Forks | 53 |
| Language | Rust |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 69.4377850268335/100 |
| Open Issues | 35 |
| Last Updated | 2026-09-21 |
| Created | 2025-10-15 |
| Platforms | aws, cli, mcp, rust |
| Est. Tokens | ~26k |
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iam-policy-autopilot is IAM Policy Autopilot is an open source static code analysis tool that helps you quickly create baseline AWS IAM policies that you can refine as your application evolves. This tool is available as a co. It is categorized as a MCP Server with 456 GitHub stars.
iam-policy-autopilot is primarily written in Rust. It covers topics such as aws, aws-iam, aws-iam-policies.
You can find installation instructions and usage details in the iam-policy-autopilot GitHub repository at github.com/awslabs/iam-policy-autopilot. The project has 456 stars and 53 forks, indicating an active community.
iam-policy-autopilot is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to iam-policy-autopilot on Agent Skills Hub include roam-code, ai-trader, agentic-radar. 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: