# openenv.yaml — Environment manifest

name: exec-assist
version: "1.0.0"
description: >
  Executive Assistant environment where AI agents learn to manage email and calendar.
  Agents must draft professional replies, schedule meetings, resolve conflicts, and
  handle multi-party coordination. Tests real-world assistant capabilities across
  three difficulty levels.

author: Devanshu(Team PsPredator)
repository: https://huggingface.co/spaces/DevanshuDon/exec-assist

tasks:
  - name: easy
    description: >
      Simple meeting request with clear calendar availability.
      Agent must draft polite reply and book the meeting correctly.
      Score = 50% email quality + 50% scheduling correctness.
    difficulty: easy
    max_score: 1.0
    action_schema:
      email_reply: "Professional email response to sender"
      calendar_action: "book | propose_alternatives | reschedule | decline"
      meeting_details: "MeetingDetails object with time, participants, subject"

  - name: medium
    description: >
      Scheduling conflict — requested time is already booked.
      Agent must identify conflict, propose 2-3 alternative slots, and
      explain professionally in email.
      Score = 30% email quality + 40% conflict resolution + 30% scheduling.
    difficulty: medium
    max_score: 1.0
    action_schema:
      email_reply: "Professional email explaining conflict and proposing alternatives"
      calendar_action: "propose_alternatives"
      meeting_details: "MeetingDetails with proposed_alternatives list"

  - name: hard
    description: >
      Multi-party coordination with priority conflicts.
      3 emails requesting meetings, some overlapping, one high-priority requiring
      reshuffling existing meetings. Agent must prioritize, reschedule, and notify.
      Score = 25% email + 25% scheduling + 25% conflict + 25% task completion.
    difficulty: hard
    max_score: 1.0
    action_schema:
      email_reply: "Professional emails to multiple parties"
      calendar_action: "Multiple actions coordinated"
      meeting_details: "Complete coordination plan"

endpoints:
  reset: POST /reset
  step: POST /step
  state: GET /state
  tasks: GET /tasks
  health: GET /health
  metadata: GET /metadata
  schema: GET /schema
  mcp: POST /mcp

environment:
  python_version: "3.10"
  framework: fastapi
  deployment: huggingface_spaces