Aeon

Aeon is a community skill for building time-aware AI agent systems, covering temporal reasoning, scheduling logic, deadline tracking, time-series analysis, and calendar-aware task management for agents that understand and operate within time constraints.

What Is This?

Overview

Aeon provides patterns for creating AI agents that incorporate temporal awareness into their decision-making and operations. It covers time-aware prompt construction that includes current date and deadline context, scheduling algorithms for task prioritization based on urgency and dependencies, recurrence handling for periodic tasks and reminders, time-series data analysis for identifying trends, and timezone management for distributed team coordination. The skill enables developers to build agents that reason about time naturally and manage temporal workflows.

Who Should Use This

This skill serves developers building AI assistants that manage schedules and deadlines, teams creating project management agents that track task timelines, and engineers designing monitoring agents that analyze time-series data.

Why Use It?

Problems It Solves

Standard AI agents lack awareness of current time, deadlines, and temporal relationships between tasks. Scheduling logic requires complex priority calculations that account for urgency, dependencies, and resource availability. Timezone conversions across distributed teams introduce errors in meeting coordination. Recurring task management needs pattern detection that simple reminders cannot provide.

Core Highlights

Time-aware prompts inject current timestamps and deadline context into agent requests. Priority scheduling ranks tasks by urgency, dependency order, and estimated duration. Recurrence patterns define periodic tasks with configurable intervals and end conditions. Timezone handling converts and displays times correctly for distributed participants.

How to Use It?

Basic Usage

from dataclasses import dataclass, field
from datetime import datetime, timedelta

@dataclass
class TimeTask:
    name: str
    deadline: str
    priority: int = 0
    dependencies: list[str] = field(
        default_factory=list)
    completed: bool = False

class TaskScheduler:
    def __init__(self):
        self.tasks: dict[str, TimeTask] = {}

    def add_task(self, task: TimeTask):
        self.tasks[task.name] = task

    def get_overdue(self, now: str = ""
                    ) -> list[TimeTask]:
        current = now or datetime.now().isoformat()
        return [t for t in self.tasks.values()
                if not t.completed
                and t.deadline < current]

    def get_ready(self) -> list[TimeTask]:
        ready = []
        for task in self.tasks.values():
            if task.completed:
                continue
            deps_met = all(
                self.tasks.get(d, TimeTask(
                    name=d, deadline="")).completed
                for d in task.dependencies)
            if deps_met:
                ready.append(task)
        return sorted(ready,
                       key=lambda t: t.deadline)

Real-World Examples

from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class RecurrencePattern:
    interval_days: int
    start_date: str
    end_date: str = ""
    occurrences: int = 0

class TimeAwareAgent:
    def __init__(self, scheduler: TaskScheduler):
        self.scheduler = scheduler
        self.patterns: dict[str, RecurrencePattern] = {}

    def add_recurring(self, task_name: str,
                      pattern: RecurrencePattern):
        self.patterns[task_name] = pattern

    def generate_occurrences(self, task_name: str,
                             count: int = 5
                             ) -> list[str]:
        pattern = self.patterns.get(task_name)
        if not pattern:
            return []
        dates = []
        current = datetime.fromisoformat(
            pattern.start_date)
        delta = timedelta(days=pattern.interval_days)
        for _ in range(count):
            dates.append(current.isoformat())
            current += delta
        return dates

    def daily_briefing(self, now: str = ""
                       ) -> dict:
        overdue = self.scheduler.get_overdue(now)
        ready = self.scheduler.get_ready()
        return {"overdue": [t.name for t in overdue],
                "ready": [t.name for t in ready],
                "total_pending": len([
                    t for t in self.scheduler.tasks.values()
                    if not t.completed])}

Advanced Tips

Include the current date and time in agent system prompts so the model can reason about temporal relationships accurately. Use dependency graphs to automatically unblock tasks when their prerequisites are completed. Build daily briefing summaries that highlight overdue items, upcoming deadlines, and available tasks.

When to Use It?

Use Cases

Build a project management assistant that tracks task deadlines and alerts the team about overdue items. Create a scheduling agent that coordinates meetings across timezones and resolves conflicts automatically. Deploy a monitoring agent that analyzes time-series metrics and triggers alerts when trends indicate problems.

Related Topics

Task scheduling algorithms, temporal reasoning, calendar API integration, time-series analysis, and project management automation.

Important Notes

Requirements

Access to system clock or time API for current timestamps. Persistent storage for task definitions and recurrence patterns. Calendar integration for scheduling features that coordinate with existing systems.

Usage Recommendations

Do: always include timezone information when storing and comparing timestamps. Use ISO 8601 format for date strings to ensure consistent parsing across systems. Test scheduling logic with edge cases like daylight saving transitions.

Don't: compare date strings without normalizing timezones first. Assume all users are in the same timezone when scheduling across distributed teams. Store timestamps without timezone identifiers, which causes ambiguity in multi-region deployments.

Limitations

Temporal reasoning in language models can be unreliable for complex date calculations. Recurrence patterns with exceptions and holidays need custom logic beyond simple interval repetition. Real-time scheduling requires event-driven architecture that adds system complexity.