Block dangerous Git and shell commands from being executed by agents
A tool that prevents LLM-powered agents from executing dangerous Git and shell commands. It solves the problem of agents accidentally or maliciously running destructive commands like force pushes or rm -rf, protecting developers and CI/CD pipelines.
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Git-aware AI debugger that checks out old commits to fix production bugs
A tool that makes AI coding assistants (like Cursor or Claude Code) automatically checkout the git commit corresponding to a production error before debugging, preventing the agent from analyzing current code that has shifted. It solves the problem of AI agents hallucinating fixes because they look at the present state of files while the bug existed in a past commit.
Runeward: Sandbox AI agents with policy gates
Runeward is a sandboxing tool for AI agents that enforces policy gates to restrict agent actions. It uses LLMs to interpret and enforce user-defined policies, solving the problem of unsafe or unintended agent behavior for developers building autonomous AI systems.
Attestor: open-source zero-trust boundary for autonomous AI agents
Attestor is an open-source execution boundary that enforces zero-trust security for autonomous AI agents. It uses LLMs to define and enforce policies that restrict agent actions, preventing unauthorized access or data leaks. This solves the problem of safely deploying autonomous agents in production environments.
Local-first CLI that masks PII and secrets before sending to LLMs
LocalMask is a command-line tool that runs locally to detect and mask personally identifiable information (PII) and secrets before data is sent to large language models. It solves the privacy and compliance problem for developers and organizations that need to use LLMs without exposing sensitive data.
Ripple: open-source local-first authorization for AI coding agents
Ripple is an open-source, local-first authorization layer that restricts AI coding agents to only modify files they are explicitly permitted to change. It solves the problem of AI agents making unauthorized or unintended modifications across a codebase, giving developers control and trust in agentic coding workflows.