autonomous intelligence

    1

    Secure your AI agent with a transaction-safe MCP layer for guarded, approval-gated local file operations.

    Free

    Security scanned

    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    autonomous intelligence

    Example session with this skill installed

    Set up a safe MCP config for Claude Code in C:\dev\project-alpha and show me the tool definitions.

    • Read your context and instructions
    • Compiled the autonomous intelligence

    I have initialized the workspace environment and generated the configuration for Claude Code. The system is now ready to interface with the trusted Broker for guarded file operations.

    AI Agent MCP Configuration

    To enable safe file operations, add the following to your claude_desktop_config.json:

    {
  "mcpServers": {
    "autonomous-intelligence": {
      "command": "C:\\Users\\Admin\\AppData\\Local\\AutonomousIntelligence\\runtime\\Scripts\\autonomous-intelligence-mcp.exe",
      "args": [
        "--workspace",
        "C:\\dev\\project-alpha"
      ],
      "env": {
        "PYTHONPATH": "C:\\Users\\Admin\\AppData\\Local\\AutonomousIntelligence\\runtime\\Lib\\site-packages"
      }
    }
  }
}

    Registered Tools

    ToolPurposeSecurity Constraint
    autonomous_read_fileRead UTF-8 contentRead-only, returns SHA-256 digest
    autonomous_write_fileCreate/Replace filesRequires HMAC approval from Broker
    autonomous_recover_incompleteReconcile attemptsIdempotent, no blind retries
    autonomous_get_attempt_statusCheck Engine stateRead-only access to SQLite journal
    autonomous_list_recent_operationsAudit historyNon-sensitive operation summaries

    Next steps

    1. Run autonomous-intelligence --workspace C:\dev\project-alpha broker in a separate terminal.
    2. Restart Claude Code to load the new MCP configuration.
    3. Attempt a read operation to verify the connection between the client and the Engine.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    AI agents often require file system access, but giving a LLM raw OS permissions is a significant security risk. This skill implements a transaction-safe local action layer that separates the untrusted planning agent from a trusted execution engine. It ensures that every file write is gated by a dedicated Broker that requires manual or policy-based approval, preventing unauthorized modifications while maintaining a durable audit trail.

    What it does

    • Guarded File Operations provides read and write tools that are strictly confined to a specific workspace directory.
    • Transaction Safety uses a durable SQLite-backed journaling system to track operation attempts and prevent corruption.
    • Approval-Gated Writes forces single-use, expiring HMAC approvals for any modification attempt.
    • Crash Recovery reconciles incomplete operations without blind retries, marking unproven states as UNCERTAIN to prevent data loss.
    • Multi-Client Support generates configuration for Claude Code, Cursor, Gemini CLI, and other MCP-compatible environments.

    How it works

    1. Initialize the Runtime by creating a Python virtual environment and installing the core package.
    2. Launch the Broker in a persistent terminal to manage the trust boundary and approval queue for the workspace.
    3. Configure the Client by generating the specific JSON or TOML configuration required for your preferred AI editor or CLI.
    4. Execute Guarded Tools through the agent to read files, request write approvals, or audit recent operations.

    Frameworks & tools

    Built for Python 3.11+ on Windows systems. It utilizes the Model Context Protocol (MCP) v2 for communication and SQLite for local state journaling.

    Why this beats prompting it yourself

    Prompting an agent to "be careful" with files does not prevent hallucinated commands or accidental recursive deletions. This skill enforces a hard architectural boundary where the agent physically cannot execute a write without a verified HMAC token from the Broker.

    Use cases

    • Granting an agent permission to refactor code within a specific project folder.
    • Setting up a crash-safe automated documentation agent that requires human sign-off for every change.
    • Providing safe file-reading capabilities to a research agent without risk of system-wide directory traversal.

    Known limitations

    Currently restricted to Windows due to named-pipe IPC and specific path semantics. Requires an active background Broker process to function.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Python

    Listed1 day ago

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