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>
86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
from __future__ import annotations
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from collections import defaultdict
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from typing import ClassVar
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from .base import BaseAgent, AgentInput, AgentOutput
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from .manifest import AgentManifest
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MANIFEST = AgentManifest(
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id="focus-area",
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version="1.1.0", # bumped: preferred_areas pref is now honoured in compute (#113)
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description="Identifies the most congested project/area in the user's task list.",
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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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"preferred_areas": {
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"type": "array",
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"items": {"type": "string"},
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"default": [],
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"description": "Project / label names to prioritise when multiple areas tie.",
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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:focus-area"],
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output_contract={"type": "snippet", "format": "free_text"},
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ttl_sec=43_200,
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# No inferred_params: preferred_areas requires project-level feedback linkage
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# that isn't available in feedback_history alone. Revisit with #78 (signal
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# abstraction) once per-task reactions can be traced back to a project.
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)
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class FocusAreaAgent(BaseAgent):
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"""Identifies the most congested project/area in the user's task list."""
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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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preferred: list[str] = inp.agent_prefs.get("preferred_areas", [])
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by_project: dict[str, list[dict]] = defaultdict(list)
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for task in inp.tasks:
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project = task.get("project_id") or task.get("project") or "default"
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by_project[project].append(task)
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if not by_project:
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prompt = "No tasks available to identify a focus area."
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return self._make_output(inp, prompt, {"project_count": 0})
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def score(project: str, tasks: list[dict]) -> tuple[float, bool]:
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base = sum(2.0 if t.get("is_overdue") else 1.0 for t in tasks)
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# Boost preferred areas to break ties in their favour
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boosted = project in preferred or any(p in project for p in preferred)
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return (base + (0.5 if boosted else 0.0), boosted)
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top_project, top_tasks = max(
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by_project.items(),
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key=lambda kv: score(kv[0], kv[1]),
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)
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overdue_in_top = sum(1 for t in top_tasks if t.get("is_overdue"))
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label = "the default project" if top_project == "default" else f'"{top_project}"'
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n = len(top_tasks)
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boosted = top_project in preferred or any(p in top_project for p in preferred)
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parts = [
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f"The user's most congested area is {label} "
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f"({n} task{'s' if n != 1 else ''}, {overdue_in_top} overdue)."
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]
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if boosted:
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parts.append("This area matches the user's stated focus preferences.")
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if overdue_in_top >= 3:
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parts.append("Consider surfacing an action from this area.")
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prompt = " ".join(parts)
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snapshot = {
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"top_project": top_project,
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"top_task_count": n,
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"top_overdue_count": overdue_in_top,
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"project_count": len(by_project),
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"preferred_areas": preferred,
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}
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return self._make_output(inp, prompt, snapshot)
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