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.