Sprint 2: Hermes masterclass integration — brief + weekly review modules
- oracle/brief.py: Morning intelligence briefing (CyrilXBT format) THE ONE THING, WHAT HAPPENED, WHAT TO WATCH, FROM MEMORY, TODAY'S FOCUS Source cooldown (72h dedup), theme clustering, signal ranking - oracle/weekly.py: Weekly review synthesis Signal summary, trending themes (WoW comparison), tier breakdown, auto-generated recommendations based on patterns - oracle/cli.py: Wired brief/weekly commands python -m oracle brief [--max-items 8] [--max-age-h 48] [-o path] [--json] python -m oracle weekly [--days 7] [-o path] [--json] Integrates patterns from Hermes Agent Masterclass: - Morning brief template structure - Content source cooldown (no repeats within 72h) - Weekly pattern synthesis with trend detection - Quality gate recommendations (score thresholds, diversity alerts) - Theme clustering for FROM MEMORY section
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Epictetus
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"""Weekly review — synthesize the week's activity into patterns and trends.
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CyrilXBT masterclass pattern: Sunday 7PM synthesis that identifies
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recurring themes, tracks source performance, and generates insights.
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Operates as a pure-rules aggregation layer — no AI synthesis needed.
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"""
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import sqlite3
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from datetime import datetime, timedelta
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from pathlib import Path
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from collections import Counter
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WEEKLY_TEMPLATE = """# Weekly Review — {week_start} to {week_end}
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## SIGNAL SUMMARY
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- **Total entries**: {total_entries}
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- **New this week**: {new_this_week}
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- **PUBLISH verdicts**: {publish_count}
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- **WATCH verdicts**: {watch_count}
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- **Average signal score**: {avg_score:.2f}
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## TOP SOURCES (by entry count)
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{sources_md}
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## TOP CATEGORIES (by frequency)
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{categories_md}
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## SOURCE TIER BREAKDOWN
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{tier_md}
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## TRENDING THEMES
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{trending_md}
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## WEEK OVER WEEK COMPARISON
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{wow_md}
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## RECOMMENDATIONS
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{recs_md}
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"""
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def get_connection(db_path: str) -> sqlite3.Connection:
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conn = sqlite3.connect(db_path)
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conn.row_factory = sqlite3.Row
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return conn
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def _week_bounds(days_ago: int = 7) -> tuple:
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end = datetime.utcnow()
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start = end - timedelta(days=days_ago)
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return start, end
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def fetch_weekly_stats(conn: sqlite3.Connection, days: int = 7) -> dict:
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"""Aggregate weekly statistics from Athena entries."""
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start, end = _week_bounds(days)
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# Total entries
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cur = conn.execute("SELECT COUNT(*) as c FROM entries")
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total = cur.fetchone()["c"]
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# New this week
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cur = conn.execute(
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"SELECT COUNT(*) as c FROM entries WHERE first_seen >= ?",
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(start.isoformat(),),
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)
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new_this_week = cur.fetchone()["c"]
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# Verdict breakdown
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cur = conn.execute(
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"SELECT verdict, COUNT(*) as c FROM entries "
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"WHERE first_seen >= ? GROUP BY verdict",
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(start.isoformat(),),
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)
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verdicts = {row["verdict"]: row["c"] for row in cur}
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# Average signal score
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cur = conn.execute(
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"SELECT AVG(signal_score) as avg_score FROM entries WHERE first_seen >= ?",
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(start.isoformat(),),
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)
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avg_score = cur.fetchone()["avg_score"] or 0
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# Top sources
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cur = conn.execute(
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"SELECT source, COUNT(*) as c FROM entries "
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"WHERE first_seen >= ? GROUP BY source ORDER BY c DESC LIMIT 10",
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(start.isoformat(),),
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)
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top_sources = [(row["source"], row["c"]) for row in cur]
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# Top categories
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cur = conn.execute(
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"SELECT category_tags, COUNT(*) as c FROM entries "
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"WHERE first_seen >= ? AND category_tags IS NOT NULL AND category_tags != '' "
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"GROUP BY category_tags ORDER BY c DESC LIMIT 10",
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(start.isoformat(),),
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)
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top_categories = [(row["category_tags"], row["c"]) for row in cur]
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# Tier breakdown
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cur = conn.execute(
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"SELECT source_tier, COUNT(*) as c FROM entries "
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"WHERE first_seen >= ? GROUP BY source_tier ORDER BY source_tier",
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(start.isoformat(),),
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)
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tier_breakdown = {
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{1: "Tier 1 (High)", 2: "Tier 2 (Mid)", 3: "Tier 3 (Low)"}
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.get(row["source_tier"], f"Tier {row['source_tier']}"): row["c"]
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for row in cur
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}
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return {
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"week_start": start.strftime("%Y-%m-%d"),
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"week_end": end.strftime("%Y-%m-%d"),
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"total_entries": total,
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"new_this_week": new_this_week,
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"verdicts": verdicts,
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"publish_count": verdicts.get("PUBLISH", 0),
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"watch_count": verdicts.get("WATCH", 0),
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"avg_score": avg_score,
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"top_sources": top_sources,
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"top_categories": top_categories,
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"tier_breakdown": tier_breakdown,
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}
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def _trending_themes(conn: sqlite3.Connection, days: int = 7) -> list:
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"""Identify themes that appear more frequently this week vs last week."""
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start, end = _week_bounds(days)
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prev_start = start - timedelta(days=days)
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# This week's categories
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cur = conn.execute(
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"SELECT category_tags, COUNT(*) as c FROM entries "
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"WHERE first_seen >= ? AND first_seen < ? AND category_tags IS NOT NULL "
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"GROUP BY category_tags",
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(start.isoformat(), end.isoformat()),
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)
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this_week = {row["category_tags"]: row["c"] for row in cur}
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# Last week's categories
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cur = conn.execute(
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"SELECT category_tags, COUNT(*) as c FROM entries "
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"WHERE first_seen >= ? AND first_seen < ? AND category_tags IS NOT NULL "
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"GROUP BY category_tags",
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(prev_start.isoformat(), start.isoformat()),
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)
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last_week = {row["category_tags"]: row["c"] for row in cur}
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# Find trending (increased by 50%+ or new)
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trending = []
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for cat, count in this_week.items():
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prev_count = last_week.get(cat, 0)
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if prev_count == 0 and count >= 2:
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trending.append((cat, count, "NEW"))
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elif prev_count > 0 and count >= prev_count * 1.5:
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trending.append((cat, count, f"+{int((count - prev_count) / prev_count * 100)}%"))
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trending.sort(key=lambda x: x[1], reverse=True)
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return trending
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def _generate_recommendations(stats: dict, trending: list) -> list:
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"""Generate recommendations based on weekly patterns."""
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recs = []
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# Source diversity check
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if stats["top_sources"]:
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top_source_pct = stats["top_sources"][0][1] / max(stats["new_this_week"], 1) * 100
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if top_source_pct > 40:
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recs.append(
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f"⚠ High concentration from {stats['top_sources'][0][0]} "
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f"({top_source_pct:.0f}% of entries). Consider diversifying sources."
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)
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# PUBLISH rate
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if stats["publish_count"] == 0 and stats["new_this_week"] > 10:
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recs.append(
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"🔍 Zero PUBLISH verdicts despite "
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f"{stats['new_this_week']} new entries. "
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"Signal scoring may need recalibration."
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)
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# Trending themes
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if trending:
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recs.append(
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f"📈 Trending themes: {', '.join(t[0] for t in trending[:3])}. "
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"Consider deeper coverage."
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)
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# Score distribution
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if stats["avg_score"] < 3.0 and stats["new_this_week"] > 0:
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recs.append(
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f"📉 Average signal score ({stats['avg_score']:.2f}) is low. "
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"Check adapter signal_score assignments."
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)
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if not recs:
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recs.append("✅ System operating within normal parameters.")
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return recs
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def generate_weekly(
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conn: sqlite3.Connection,
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days: int = 7,
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output_path: str | None = None,
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) -> dict:
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"""Generate a complete weekly review."""
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stats = fetch_weekly_stats(conn, days)
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trending = _trending_themes(conn, days)
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recommendations = _generate_recommendations(stats, trending)
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# Format sections
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top_sources_md = "\n".join(
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f"- **{s}**: {c} entries" for s, c in stats["top_sources"]
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) or "_No sources._"
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top_cats_md = "\n".join(
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f"- **{c}**: {n} entries" for c, n in stats["top_categories"]
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) or "_No categories._"
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tier_md = "\n".join(
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f"- **{label}**: {count}" for label, count in stats["tier_breakdown"].items()
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) or "_No data._"
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trending_md = "\n".join(
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f"- **{t[0]}**: {t[1]} entries ({t[2]})" for t in trending
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) or "_No trending themes detected._"
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# Week-over-week
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prev_count = stats["total_entries"] - stats["new_this_week"]
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if prev_count > 0:
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change = (stats["new_this_week"] - prev_count) / prev_count * 100
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wow = (
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f"New this week: {stats['new_this_week']} vs previous: {prev_count} "
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f"({change:+.0f}%)"
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)
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else:
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wow = f"First full week of data: {stats['new_this_week']} entries"
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recs_md = "\n".join(f"- {r}" for r in recommendations)
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review = {
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**stats,
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"trending": trending,
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"recommendations": recommendations,
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}
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review["markdown"] = WEEKLY_TEMPLATE.format(
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week_start=stats["week_start"],
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week_end=stats["week_end"],
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total_entries=stats["total_entries"],
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new_this_week=stats["new_this_week"],
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publish_count=stats["publish_count"],
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watch_count=stats["watch_count"],
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avg_score=stats["avg_score"],
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sources_md=top_sources_md,
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categories_md=top_cats_md,
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tier_md=tier_md,
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trending_md=trending_md,
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wow_md=wow,
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recs_md=recs_md,
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)
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if output_path:
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out = Path(output_path)
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out.parent.mkdir(parents=True, exist_ok=True)
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out.write_text(review["markdown"], encoding="utf-8")
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review["saved"] = str(out)
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return review
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# ── CLI ──
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="Generate Athena weekly review")
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parser.add_argument("--db", default="oracle.db")
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parser.add_argument("--days", type=int, default=7)
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parser.add_argument("--output", "-o", help="Output path for markdown review")
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parser.add_argument("--json", action="store_true", help="Output as JSON")
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args = parser.parse_args()
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conn = get_connection(args.db)
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try:
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review = generate_weekly(conn, days=args.days)
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if args.json:
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import json
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print(json.dumps({k: v for k, v in review.items() if k != "markdown"}, indent=2, default=str))
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elif args.output:
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generate_weekly(conn, days=args.days, output_path=args.output)
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print(f"Saved: {args.output}")
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else:
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print(review.get("markdown", "No review generated"))
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finally:
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conn.close()
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