alvis
afb0e9b0cb
All four agents bumped to v1.1.0. momentum (#114): infers engagement_trend ('up'|'stable'|'down') by comparing done-rate in the last 7 days vs the prior 7 days. Agent surfaces the trend in its snippet ("trending up — build on the momentum"). overdue-task (#115): infers lateness_tolerance_days (0/1/2) from snooze rate. Agent now filters tasks against the tolerance so low-urgency users aren't nagged about tasks that are only hours overdue. recent-patterns (#116): infers window_days (7/14/30) from feedback event density — sparse users get a wider window so the snippet isn't always empty. focus-area (#113): no inferred params (project-level feedback linkage needed, tracked under #78). preferred_areas pref was declared but ignored; agent now honours it as a tiebreaker and mentions it in the snippet. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
from __future__ import annotations
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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from .inference.history import UserHistory
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from .manifest import AgentManifest, InferredParam
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def _infer_lateness_tolerance(history: UserHistory) -> int:
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"""Estimate how many days past due a task needs to be before the user acts.
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High snooze rate → user doesn't act immediately → raise tolerance so the
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agent doesn't nag them about tasks they'll handle in their own time.
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"""
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total = len(history.events)
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if total == 0:
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return 0
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snooze_rate = sum(1 for e in history.events if e.action == "snooze") / total
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if snooze_rate > 0.40:
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return 2
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if snooze_rate > 0.20:
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return 1
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return 0
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MANIFEST = AgentManifest(
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id="overdue-task",
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version="1.1.0", # bumped: lateness_tolerance_days InferredParam added (#115)
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description="Reports the user's overdue tasks by count and age.",
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pref_schema={
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"type": "object",
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"additionalProperties": False,
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"properties": {
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"lateness_tolerance_days": {
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"type": "integer",
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"minimum": 0,
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"default": 0,
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"description": "Days past due before a task is considered overdue. 0 = the moment it's late.",
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},
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},
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},
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context_schema=["todoist.tasks"],
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required_consents=["data:core", "data:todoist", "agent:overdue-task"],
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output_contract={"type": "snippet", "format": "free_text"},
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ttl_sec=3600,
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silenced_in_contexts=["vacation"],
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inferred_params=[
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InferredParam(
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key="lateness_tolerance_days",
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ttl_sec=86_400, # recompute daily — snooze pattern shifts slowly
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cold_start_default=0,
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min_history=10,
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infer=_infer_lateness_tolerance,
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),
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],
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)
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class OverdueTaskAgent(BaseAgent):
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"""Reports the user's overdue tasks by count and age."""
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agent_id: ClassVar[str] = MANIFEST.id
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ttl_seconds: ClassVar[int] = MANIFEST.ttl_sec
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version: ClassVar[str] = MANIFEST.version
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def compute(self, inp: AgentInput) -> AgentOutput:
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tolerance = max(0, int(inp.agent_prefs.get("lateness_tolerance_days", 0)))
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overdue = [
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t for t in inp.tasks
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if t.get("is_overdue") and t.get("task_age_days", 0) >= tolerance
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]
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top = sorted(overdue, key=lambda t: -t.get("task_age_days", 0))[:3]
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if not overdue:
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prompt = "The user has no overdue tasks at this time."
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elif len(overdue) == 1:
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t = top[0]
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age = round(t.get("task_age_days", 0))
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prompt = (
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f'The user has 1 overdue task: "{t["content"]}" '
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f"({age} day{'s' if age != 1 else ''} overdue)."
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)
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else:
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items = ", ".join(
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f'"{t["content"]}" ({round(t.get("task_age_days", 0))}d)'
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for t in top
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)
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prompt = (
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f"The user has {len(overdue)} overdue tasks. "
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f"Top {len(top)}: {items}."
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)
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snapshot = {
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"overdue_count": len(overdue),
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"lateness_tolerance_days": tolerance,
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"top_overdue": [
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{"content": t["content"], "task_age_days": t.get("task_age_days", 0)}
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for t in top
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],
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}
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return self._make_output(inp, prompt, snapshot)
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