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>
122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
from __future__ import annotations
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from collections import Counter
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from datetime import datetime, timezone
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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_window_days(history: UserHistory) -> int:
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"""Infer the optimal lookback window from feedback event density.
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More events per day → a shorter window captures the user's current state
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accurately. Sparse feedback → widen the window to gather signal.
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"""
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n = len(history.events)
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if n >= 14:
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return 7
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if n >= 7:
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return 14
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return 30
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MANIFEST = AgentManifest(
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id="recent-patterns",
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version="1.1.0", # bumped: window_days InferredParam added (#116)
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description="Surfaces the user's reaction pattern from recent feedback.",
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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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"window_days": {
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"type": "integer",
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"minimum": 1,
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"maximum": 30,
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"default": 7,
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"description": "Lookback window for pattern analysis.",
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},
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},
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},
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context_schema=["tip_feedback", "profile.features"],
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required_consents=["data:core", "agent:recent-patterns"],
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output_contract={"type": "snippet", "format": "free_text"},
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ttl_sec=86_400,
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inferred_params=[
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InferredParam(
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key="window_days",
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ttl_sec=86_400, # recompute daily alongside snippet
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cold_start_default=7,
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min_history=5,
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infer=_infer_window_days,
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),
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],
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)
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class RecentPatternsAgent(BaseAgent):
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"""Surfaces the user's reaction pattern from recent feedback."""
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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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window_days = max(1, int(inp.agent_prefs.get("window_days", 7)))
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window_s = window_days * 86_400
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now_ts = inp.now.timestamp()
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recent = [
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f for f in inp.feedback_history
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if self._age_s(f.get("created_at", ""), now_ts) <= window_s
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]
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counts: Counter[str] = Counter(f.get("action") for f in recent)
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total = len(recent)
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dwell_ms = inp.profile.get("mean_dwell_ms_30d")
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if total == 0:
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prompt = f"No tip reactions recorded in the last {window_days} days."
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else:
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done = counts.get("done", 0)
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dismissed = counts.get("dismiss", 0)
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snoozed = counts.get("snooze", 0)
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parts = [
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f"Last {window_days} days: {total} tip reaction{'s' if total != 1 else ''} — "
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f"{done} completed, {dismissed} dismissed, {snoozed} snoozed."
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]
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if dwell_ms is not None:
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dwell_s = round(dwell_ms / 1000)
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if dwell_s < 15:
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parts.append(
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"Average dwell is very short — user may be acting on auto-pilot; vary tip content."
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)
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elif dwell_s < 60:
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parts.append(f"Average dwell {dwell_s}s — tips are being read.")
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else:
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parts.append(
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f"Average dwell {dwell_s}s — user deliberates; prefer tips that reward reflection."
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)
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prompt = " ".join(parts)
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snapshot = {
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"window_days": window_days,
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"recent_total": total,
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"action_counts": dict(counts),
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"mean_dwell_ms_30d": dwell_ms,
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}
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return self._make_output(inp, prompt, snapshot)
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@staticmethod
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def _age_s(iso: str, now_ts: float) -> float:
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if not iso:
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return float("inf")
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try:
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dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return now_ts - dt.timestamp()
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except Exception:
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return float("inf")
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