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by sondera-ai · Agent Tool · ★ 225
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Coding Agent Hooks by Sondera Released as part of the Hooking Coding Agents with the Cedar Policy Language talk at Unprompted 2026. A reference monitor for AI coding agents. Rust hook binaries and Cedar policies intercept every shell command, file operation, and web request to forbid exfiltration and destructive behaviors, and enforce information flow control. YARA signatures and Cedar policy evaluation are deterministic; the optional LLM-based classifiers (data sensitivity, secure code policy) are probabilistic. Works with Claude Code, Cursor, GitHub Copilot, and Gemini CLI. Getting Started Prerequisites Install Rust and Cargo using rustup: The YARA signature engine and Cedar policies work without any external dependencies. The LLM-based classifiers (data sensitivity and secure code policy) require Ollama with the model: bash Install Ollama — see https://ollama.com/download for other platforms brew install ollama Pull the model (12 GB) ollama pull gpt-oss-safeguard
| Stars | 225 |
| Forks | 32 |
| Language | Rust |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 70.11159016061/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-22 |
| Created | 2026-02-27 |
| Platforms | claude-code, gemini, rust |
| Est. Tokens | ~24k |
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sondera-coding-agent-hooks is Hooking implementations and supporting tools for various coding agents (Claude, Cursor, Gemini, etc). It is categorized as a Agent Tool with 225 GitHub stars.
sondera-coding-agent-hooks is primarily written in Rust.
You can find installation instructions and usage details in the sondera-coding-agent-hooks GitHub repository at github.com/sondera-ai/sondera-coding-agent-hooks. The project has 225 stars and 32 forks, indicating an active community.
sondera-coding-agent-hooks is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to sondera-coding-agent-hooks on Agent Skills Hub include awesome-android-agent-skills, claude-code-skill-factory, agent-skills. 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: