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Human Time-to-Completion Evaluation CLI

Project description

hte-cli

Human Time-to-Completion Evaluation CLI - A tool for running assigned cybersecurity tasks with timing and result tracking.

Installation

# Recommended (pipx)
pipx install hte-cli

# Or with pip
pip install hte-cli

Quick Start

  1. Login (get credentials from your coordinator):

    hte-cli auth login
    
  2. View your assigned tasks:

    hte-cli tasks list
    
  3. Run a task:

    hte-cli tasks run
    

Commands

  • hte-cli auth login - Authenticate with the API
  • hte-cli auth status - Check authentication status
  • hte-cli tasks list - List your pending tasks
  • hte-cli tasks run [TASK_ID] - Run a task (defaults to highest priority)
  • hte-cli tasks pull-images - Pre-pull Docker images for upcoming tasks
  • hte-cli version - Show version info

System Requirements

All Platforms

  • Python 3.11+
  • Docker with Docker Compose v2

Windows

  • Docker Desktop with WSL2 backend enabled
  • WSL2 installed and configured (Microsoft docs)

macOS

  • Docker Desktop (Intel or Apple Silicon)
  • Note: Apple Silicon (M1/M2/M3) runs x86 containers via emulation - expect slightly slower performance

Linux

  • Docker Engine 20.10+
  • User added to docker group: sudo usermod -aG docker $USER (log out and back in after)

Verify Docker Setup

# Check Docker is running
docker --version

# Check Docker Compose v2
docker compose version

# Test container can start
docker run --rm hello-world

Configuration

Set HTE_API_URL environment variable to use a custom API endpoint:

export HTE_API_URL="http://your-server.com/api/v1/cli"

Support

For issues, contact your study coordinator or open an issue at: https://github.com/sean-peters-au/lyptus-mono


Developer Notes

This CLI is a thin wrapper with no consequential research decisions. It:

  • Wraps Inspect AI's human_cli agent for task execution
  • Syncs results to the backend API
  • Handles authentication via OAuth-style code exchange

The research-relevant code lives elsewhere:

  • Task sampling: scripts/sample_tasks_for_trials.py
  • Scoring criteria: src/human_ttc_eval/datasets/*/
  • Methodology: docs/methodology/human-expert-methodology-guide.md

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