alvis
772bb6e194
- integrations.ts: grant data:<provider> on OAuth callback, revoke on disconnect - Backfill migration: INSERT OR IGNORE data:<provider> for all active tokens - Agent manifests: drop agent:<id> from required_consents (momentum, time-of-day, overdue-task, recent-patterns, health-vitals) — per-agent control is a preference - eligibility.ts: update comment to reflect data:-only consent model - test_manifest.py: assert no agent: consents remain in any manifest - migrations.test.ts: backfill idempotency tests for issue #127 - Dockerfile.api: drop --offline flag (fixes ERR_PNPM_NO_OFFLINE_META) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
267 lines
9.1 KiB
Python
267 lines
9.1 KiB
Python
from __future__ import annotations
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import statistics
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from collections import Counter
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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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_DOW_NAMES = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
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# min_history required before quiet/peak inference is meaningful (issue #112)
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_MIN_HISTORY = 50
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def _infer_preferred_hour(history: UserHistory) -> int:
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"""Mode hour of day across all 'done' feedback events; falls back to 9."""
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done_hours = [e.hour for e in history.events if e.action == "done"]
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if not done_hours:
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return 9
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return Counter(done_hours).most_common(1)[0][0]
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def _quiet_window_hours(history: UserHistory) -> tuple[int, int]:
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"""Return (start_hour, end_hour) of the longest below-baseline quiet window.
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Counts all engagement events by hour. Baseline = mean hourly count.
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Finds the longest contiguous run of below-baseline hours on the circular
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clock; that run defines the quiet window.
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"""
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by_hour: Counter[int] = Counter(e.hour for e in history.events)
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total = sum(by_hour.values())
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baseline = total / 24
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# Mark each of the 24 hours as below-baseline (True = quiet)
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quiet: list[bool] = [by_hour.get(h, 0) < baseline for h in range(24)]
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# Find longest contiguous run in circular array
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best_start, best_len = 0, 0
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run_start, run_len = 0, 0
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# Double the sequence to handle wrap-around
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for i in range(48):
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h = i % 24
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if quiet[h]:
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if run_len == 0:
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run_start = i
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run_len += 1
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if run_len > best_len:
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best_len = run_len
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best_start = run_start
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else:
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run_len = 0
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if best_len == 0:
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return (22, 7) # fallback
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start = best_start % 24
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end = (best_start + best_len) % 24
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return (start, end)
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def _infer_quiet_start(history: UserHistory) -> str:
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start, _ = _quiet_window_hours(history)
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return f"{start:02d}:00"
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def _infer_quiet_end(history: UserHistory) -> str:
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_, end = _quiet_window_hours(history)
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return f"{end:02d}:00"
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def _infer_peak_hours(history: UserHistory) -> list[int]:
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"""Top-quartile hours by done-event count.
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Computes done_count per hour, then returns hours above the 75th percentile
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of non-zero hourly counts, sorted ascending.
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"""
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done_by_hour: Counter[int] = Counter(
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e.hour for e in history.events if e.action == "done"
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)
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if not done_by_hour:
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return [9, 14, 20]
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counts = list(done_by_hour.values())
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threshold = statistics.quantiles(counts, n=4)[-1] # 75th percentile
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return sorted(h for h, c in done_by_hour.items() if c >= threshold)
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MANIFEST = AgentManifest(
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id="time-of-day",
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version="1.2.0", # #112: quiet_start/end + peak_hours + tz inference
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description="Frames the current moment relative to the user's productive peak and quiet hours.",
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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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"quiet_start": {
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"type": "string",
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"pattern": "^([01][0-9]|2[0-3]):[0-5][0-9]$",
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"description": "HH:MM start of quiet hours (24h, user's local TZ).",
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},
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"quiet_end": {
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"type": "string",
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"pattern": "^([01][0-9]|2[0-3]):[0-5][0-9]$",
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"description": "HH:MM end of quiet hours.",
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},
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"peak_hours": {
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"type": "array",
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"items": {"type": "integer", "minimum": 0, "maximum": 23},
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"default": [9, 14, 20],
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"description": "Hours (0–23) with top-quartile completion density.",
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},
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"tz": {
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"type": "string",
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"default": "UTC",
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"description": "IANA timezone; populated from auth provider, fallback UTC.",
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},
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"preferred_hour": {
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"type": "integer",
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"minimum": 0,
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"maximum": 23,
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"description": "Mode done-hour (legacy; superseded by peak_hours).",
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},
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},
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},
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context_schema=["profile.features"],
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required_consents=["data:core"],
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output_contract={"type": "snippet", "format": "free_text"},
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ttl_sec=900,
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inferred_params=[
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InferredParam(
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key="preferred_hour",
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ttl_sec=3_600,
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cold_start_default=None,
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min_history=10,
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infer=_infer_preferred_hour,
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),
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InferredParam(
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key="quiet_start",
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ttl_sec=86_400,
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cold_start_default="22:00",
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min_history=_MIN_HISTORY,
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infer=_infer_quiet_start,
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),
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InferredParam(
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key="quiet_end",
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ttl_sec=86_400,
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cold_start_default="07:00",
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min_history=_MIN_HISTORY,
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infer=_infer_quiet_end,
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),
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InferredParam(
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key="peak_hours",
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ttl_sec=86_400,
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cold_start_default=[9, 14, 20],
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min_history=_MIN_HISTORY,
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infer=_infer_peak_hours,
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),
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# tz is populated from the auth provider; no infer function.
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InferredParam(
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key="tz",
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ttl_sec=86_400,
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cold_start_default="UTC",
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min_history=999_999, # effectively never inferred — always cold_start
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infer=None,
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),
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],
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)
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class TimeOfDayAgent(BaseAgent):
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"""Frames the current moment relative to the user's productive peak."""
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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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hour = inp.now.hour
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dow = inp.now.weekday()
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is_weekend = dow >= 5
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preferred_raw = inp.agent_prefs.get("preferred_hour", inp.profile.get("preferred_hour"))
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preferred = int(preferred_raw) if preferred_raw is not None else None
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quiet_start: str | None = inp.agent_prefs.get("quiet_start")
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quiet_end: str | None = inp.agent_prefs.get("quiet_end")
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peak_hours: list[int] = inp.agent_prefs.get("peak_hours", [])
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tz: str = inp.agent_prefs.get("tz", "UTC")
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in_quiet = self._in_quiet_window(hour, quiet_start, quiet_end)
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in_peak = hour in peak_hours
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parts = [f"It is {hour:02d}:00 on {_DOW_NAMES[dow]} ({self._label(hour)})."]
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if tz != "UTC":
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parts[0] = f"It is {hour:02d}:00 ({tz}) on {_DOW_NAMES[dow]} ({self._label(hour)})."
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if is_weekend:
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parts.append("Weekend context — prefer personal or reflective tips over work tasks.")
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if in_quiet:
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parts.append(
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f"User is in their quiet window ({quiet_start}–{quiet_end}) — "
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"avoid urgent or demanding tips."
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)
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elif in_peak:
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parts.append(
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f"Hour {hour:02d}:00 is a peak productivity hour for this user — "
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"a high-impact or challenging tip is appropriate."
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)
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elif peak_hours:
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# Report nearest peak so orchestrator can time advice accordingly.
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nearest = min(peak_hours, key=lambda p: min(abs(p - hour), 24 - abs(p - hour)))
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delta = min(abs(nearest - hour), 24 - abs(nearest - hour))
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if delta <= 2:
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parts.append(f"Approaching peak productivity window ({nearest:02d}:00).")
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elif preferred is not None:
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delta = min(abs(hour - preferred), 24 - abs(hour - preferred))
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if delta == 0:
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parts.append(
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f"This is the user's peak productivity hour ({preferred:02d}:00) — "
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"a high-impact tip is appropriate."
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)
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elif delta <= 2:
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parts.append(f"Approaching the user's peak productivity window ({preferred:02d}:00).")
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else:
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parts.append("No preferred-hour data yet.")
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prompt = " ".join(parts)
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snapshot = {
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"hour": hour,
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"day_of_week": dow,
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"preferred_hour": preferred,
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"quiet_start": quiet_start,
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"quiet_end": quiet_end,
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"peak_hours": peak_hours,
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"in_quiet": in_quiet,
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"in_peak": in_peak,
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"tz": tz,
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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 _in_quiet_window(hour: int, start: str | None, end: str | None) -> bool:
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if not start or not end:
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return False
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try:
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sh = int(start.split(":")[0])
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eh = int(end.split(":")[0])
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except (ValueError, IndexError):
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return False
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if sh <= eh:
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return sh <= hour < eh
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# wraps midnight e.g. 22:00–07:00
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return hour >= sh or hour < eh
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@staticmethod
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def _label(hour: int) -> str:
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if 5 <= hour < 12:
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return "morning"
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if 12 <= hour < 17:
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return "afternoon"
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if 17 <= hour < 21:
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return "evening"
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return "night"
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