janee

Inject credentials into proxied API requests with policy checks and audit logs.

30|7|Updated Feb 2, 2026
One-click install
npx skills add https://github.com/rsdouglas/janee --skill janee
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: janee
Source: https://github.com/rsdouglas/janee/tree/main
Command: npx skills add https://github.com/rsdouglas/janee --skill janee

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Janee addresses the risk of exposing API secrets to AI agents by acting as a local MCP-based proxy that injects credentials server-side and never reveals keys to agents.

Core Features & Use Cases

  • Zero-knowledge agents: secrets never leave storage; credentials are injected per request.
  • Audit and policy: full request audit trails with per-capability rules, TTLs, and ownership controls.
  • Exec mode support: run whitelisted CLI tools with credentials injected via environment variables.
  • Multi-agent and integration ready: works with GitHub, OpenAI, AWS services, and more, across stdio and HTTP transports.

Quick Start

Install Janee, run janee init to create a master key, add a service with janee add, and start the MCP server with janee serve so that agents can call execute() without ever seeing raw credentials.

Frequently Asked Questions about janee

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent AI agents from seeing raw API secrets?

To prevent API secret exposure, use a zero-knowledge proxy that injects credentials server-side so keys never reach AI agents. The proxy decrypts secrets per request, applies policy checks, and returns the API response without revealing raw credentials.

What is a zero-knowledge proxy for MCP credential injection?

A zero-knowledge proxy for MCP credential injection is a local server that decrypts secrets and applies them to proxied API calls on the agent's behalf. It coordinates with MCP tools, enforces per-capability access rules, and maintains audit trails.

How do I set up a local proxy to inject API secrets into agent requests?

Install the proxy, run the initialization command to generate a master key, add your services, and start the MCP server. Agents can then call execute() to route requests through the proxy, which handles credential injection and policy enforcement.

Can I run local CLI tools with credentials injected via environment variables?

Yes, you can run whitelisted local CLI tools using exec mode, which securely injects credentials into environment variables. The proxy enforces per-capability access rules and logs the event to maintain a full audit trail during execution.

Does this credential proxy work with GitHub, OpenAI, and AWS services?

Yes, the credential proxy works with GitHub, OpenAI, and AWS services across stdio and HTTP transports. It supports multi-agent integrations, applying per-capability access rules and audit logging to each proxied API request.

How are access policies and audit trails managed for proxied API calls?

Access policies and audit trails for proxied API calls are managed through per-capability rules, TTLs, and ownership controls. The proxy logs each decrypted credential request, applies policy checks, and records the event before returning the API response.