feat: epoch turnover notation for loopstat cycles ⟳WW.D:NNN (#496)

scripts/cycle_retro.py

@@ -4,6 +4,21 @@
 Called after each cycle completes (success or failure).
 Appends a structured entry to .loop/retro/cycles.jsonl.
 
+EPOCH NOTATION (turnover system):
+  Each cycle carries a symbolic epoch tag alongside the raw integer:
+
+      ⟳WW.D:NNN
+
+  ⟳   turnover glyph — marks epoch-aware cycles
+  WW  ISO week-of-year (01–53)
+  D   ISO weekday (1=Mon … 7=Sun)
+  NNN daily cycle counter, zero-padded, resets at midnight UTC
+
+  Example: ⟳12.3:042 — Week 12, Wednesday, 42nd cycle of the day.
+
+  The raw `cycle` integer is preserved for backward compatibility.
+  The `epoch` field carries the symbolic notation.
+
 SUCCESS DEFINITION:
   A cycle is only "success" if BOTH conditions are met:
     1. The hermes process exited cleanly (exit code 0)
@@ -36,11 +51,52 @@ from pathlib import Path
 REPO_ROOT = Path(__file__).resolve().parent.parent
 RETRO_FILE = REPO_ROOT / ".loop" / "retro" / "cycles.jsonl"
 SUMMARY_FILE = REPO_ROOT / ".loop" / "retro" / "summary.json"
+EPOCH_COUNTER_FILE = REPO_ROOT / ".loop" / "retro" / ".epoch_counter"
 
 # How many recent entries to include in rolling summary
 SUMMARY_WINDOW = 50
 
 
+# ── Epoch turnover ────────────────────────────────────────────────────────
+
+def _epoch_tag(now: datetime | None = None) -> tuple[str, dict]:
+    """Generate the symbolic epoch tag and advance the daily counter.
+
+    Returns (epoch_string, epoch_parts) where epoch_parts is a dict with
+    week, weekday, daily_n for structured storage.
+
+    The daily counter persists in .epoch_counter as a two-line file:
+        line 1: ISO date (YYYY-MM-DD) of the current epoch day
+        line 2: integer count
+    When the date rolls over, the counter resets to 1.
+    """
+    if now is None:
+        now = datetime.now(timezone.utc)
+
+    iso_cal = now.isocalendar()          # (year, week, weekday)
+    week = iso_cal[1]
+    weekday = iso_cal[2]
+    today_str = now.strftime("%Y-%m-%d")
+
+    # Read / reset daily counter
+    daily_n = 1
+    EPOCH_COUNTER_FILE.parent.mkdir(parents=True, exist_ok=True)
+    if EPOCH_COUNTER_FILE.exists():
+        try:
+            lines = EPOCH_COUNTER_FILE.read_text().strip().splitlines()
+            if len(lines) == 2 and lines[0] == today_str:
+                daily_n = int(lines[1]) + 1
+        except (ValueError, IndexError):
+            pass  # corrupt file — reset
+
+    # Persist
+    EPOCH_COUNTER_FILE.write_text(f"{today_str}\n{daily_n}\n")
+
+    tag = f"\u27f3{week:02d}.{weekday}:{daily_n:03d}"
+    parts = {"week": week, "weekday": weekday, "daily_n": daily_n}
+    return tag, parts
+
+
 def parse_args() -> argparse.Namespace:
     p = argparse.ArgumentParser(description="Log a cycle retrospective")
     p.add_argument("--cycle", type=int, required=True)
@@ -123,8 +179,30 @@ def update_summary() -> None:
             issue_failures[e["issue"]] = issue_failures.get(e["issue"], 0) + 1
     quarantine_candidates = {k: v for k, v in issue_failures.items() if v >= 2}
 
+    # Epoch turnover stats — cycles per week/day from epoch-tagged entries
+    epoch_entries = [e for e in recent if e.get("epoch")]
+    by_week: dict[int, int] = {}
+    by_weekday: dict[int, int] = {}
+    for e in epoch_entries:
+        w = e.get("epoch_week")
+        d = e.get("epoch_weekday")
+        if w is not None:
+            by_week[w] = by_week.get(w, 0) + 1
+        if d is not None:
+            by_weekday[d] = by_weekday.get(d, 0) + 1
+
+    # Current epoch — latest entry's epoch tag
+    current_epoch = epoch_entries[-1].get("epoch", "") if epoch_entries else ""
+
+    # Weekday names for display
+    weekday_glyphs = {1: "Mon", 2: "Tue", 3: "Wed", 4: "Thu",
+                      5: "Fri", 6: "Sat", 7: "Sun"}
+    by_weekday_named = {weekday_glyphs.get(k, str(k)): v
+                        for k, v in sorted(by_weekday.items())}
+
     summary = {
         "updated_at": datetime.now(timezone.utc).isoformat(),
+        "current_epoch": current_epoch,
         "window": len(recent),
         "measured_cycles": len(measured),
         "total_cycles": len(entries),
@@ -136,9 +214,12 @@ def update_summary() -> None:
         "total_lines_removed": sum(e.get("lines_removed", 0) for e in recent),
         "total_prs_merged": sum(1 for e in recent if e.get("pr")),
         "by_type": type_stats,
+        "by_week": dict(sorted(by_week.items())),
+        "by_weekday": by_weekday_named,
         "quarantine_candidates": quarantine_candidates,
         "recent_failures": [
-            {"cycle": e["cycle"], "issue": e.get("issue"), "reason": e.get("reason", "")}
+            {"cycle": e["cycle"], "epoch": e.get("epoch", ""),
+             "issue": e.get("issue"), "reason": e.get("reason", "")}
             for e in failures[-5:]
         ],
     }
@@ -157,9 +238,17 @@ def main() -> None:
     # A cycle is only truly successful if hermes exited clean AND main is green
     truly_success = args.success and args.main_green
 
+    # Generate epoch turnover tag
+    now = datetime.now(timezone.utc)
+    epoch_tag, epoch_parts = _epoch_tag(now)
+
     entry = {
-        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "timestamp": now.isoformat(),
         "cycle": args.cycle,
+        "epoch": epoch_tag,
+        "epoch_week": epoch_parts["week"],
+        "epoch_weekday": epoch_parts["weekday"],
+        "epoch_daily_n": epoch_parts["daily_n"],
         "issue": args.issue,
         "type": args.type,
         "success": truly_success,
@@ -184,7 +273,7 @@ def main() -> None:
     update_summary()
 
     status = "✓ SUCCESS" if args.success else "✗ FAILURE"
-    print(f"[retro] Cycle {args.cycle} {status}", end="")
+    print(f"[retro] {epoch_tag}  Cycle {args.cycle} {status}", end="")
     if args.issue:
         print(f" (#{args.issue} {args.type})", end="")
     if args.duration:

scripts/loop_introspect.py

@@ -0,0 +1,407 @@
+#!/usr/bin/env python3
+"""Loop introspection — the self-improvement engine.
+
+Analyzes retro data across time windows to detect trends, extract patterns,
+and produce structured recommendations. Output is consumed by deep_triage
+and injected into the loop prompt context.
+
+This is the piece that closes the feedback loop:
+  cycle_retro → introspect → deep_triage → loop behavior changes
+
+Run:  python3 scripts/loop_introspect.py
+Output: .loop/retro/insights.json (structured insights + recommendations)
+        Prints human-readable summary to stdout.
+
+Called by: deep_triage.sh (before the LLM triage), timmy-loop.sh (every 50 cycles)
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from collections import defaultdict
+from datetime import datetime, timezone, timedelta
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+CYCLES_FILE = REPO_ROOT / ".loop" / "retro" / "cycles.jsonl"
+DEEP_TRIAGE_FILE = REPO_ROOT / ".loop" / "retro" / "deep-triage.jsonl"
+TRIAGE_FILE = REPO_ROOT / ".loop" / "retro" / "triage.jsonl"
+QUARANTINE_FILE = REPO_ROOT / ".loop" / "quarantine.json"
+INSIGHTS_FILE = REPO_ROOT / ".loop" / "retro" / "insights.json"
+
+# ── Helpers ──────────────────────────────────────────────────────────────
+
+def load_jsonl(path: Path) -> list[dict]:
+    """Load a JSONL file, skipping bad lines."""
+    if not path.exists():
+        return []
+    entries = []
+    for line in path.read_text().strip().splitlines():
+        try:
+            entries.append(json.loads(line))
+        except (json.JSONDecodeError, ValueError):
+            continue
+    return entries
+
+
+def parse_ts(ts_str: str) -> datetime | None:
+    """Parse an ISO timestamp, tolerating missing tz."""
+    if not ts_str:
+        return None
+    try:
+        dt = datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
+        if dt.tzinfo is None:
+            dt = dt.replace(tzinfo=timezone.utc)
+        return dt
+    except (ValueError, TypeError):
+        return None
+
+
+def window(entries: list[dict], days: int) -> list[dict]:
+    """Filter entries to the last N days."""
+    cutoff = datetime.now(timezone.utc) - timedelta(days=days)
+    result = []
+    for e in entries:
+        ts = parse_ts(e.get("timestamp", ""))
+        if ts and ts >= cutoff:
+            result.append(e)
+    return result
+
+
+# ── Analysis functions ───────────────────────────────────────────────────
+
+def compute_trends(cycles: list[dict]) -> dict:
+    """Compare recent window (last 7d) vs older window (7-14d ago)."""
+    recent = window(cycles, 7)
+    older = window(cycles, 14)
+    # Remove recent from older to get the 7-14d window
+    recent_set = {(e.get("cycle"), e.get("timestamp")) for e in recent}
+    older = [e for e in older if (e.get("cycle"), e.get("timestamp")) not in recent_set]
+
+    def stats(entries):
+        if not entries:
+            return {"count": 0, "success_rate": None, "avg_duration": None,
+                    "lines_net": 0, "prs_merged": 0}
+        successes = sum(1 for e in entries if e.get("success"))
+        durations = [e["duration"] for e in entries if e.get("duration", 0) > 0]
+        return {
+            "count": len(entries),
+            "success_rate": round(successes / len(entries), 3) if entries else None,
+            "avg_duration": round(sum(durations) / len(durations)) if durations else None,
+            "lines_net": sum(e.get("lines_added", 0) - e.get("lines_removed", 0) for e in entries),
+            "prs_merged": sum(1 for e in entries if e.get("pr")),
+        }
+
+    recent_stats = stats(recent)
+    older_stats = stats(older)
+
+    trend = {
+        "recent_7d": recent_stats,
+        "previous_7d": older_stats,
+        "velocity_change": None,
+        "success_rate_change": None,
+        "duration_change": None,
+    }
+
+    if recent_stats["count"] and older_stats["count"]:
+        trend["velocity_change"] = recent_stats["count"] - older_stats["count"]
+    if recent_stats["success_rate"] is not None and older_stats["success_rate"] is not None:
+        trend["success_rate_change"] = round(
+            recent_stats["success_rate"] - older_stats["success_rate"], 3
+        )
+    if recent_stats["avg_duration"] is not None and older_stats["avg_duration"] is not None:
+        trend["duration_change"] = recent_stats["avg_duration"] - older_stats["avg_duration"]
+
+    return trend
+
+
+def type_analysis(cycles: list[dict]) -> dict:
+    """Per-type success rates and durations."""
+    by_type: dict[str, list[dict]] = defaultdict(list)
+    for c in cycles:
+        by_type[c.get("type", "unknown")].append(c)
+
+    result = {}
+    for t, entries in by_type.items():
+        durations = [e["duration"] for e in entries if e.get("duration", 0) > 0]
+        successes = sum(1 for e in entries if e.get("success"))
+        result[t] = {
+            "count": len(entries),
+            "success_rate": round(successes / len(entries), 3) if entries else 0,
+            "avg_duration": round(sum(durations) / len(durations)) if durations else 0,
+            "max_duration": max(durations) if durations else 0,
+        }
+    return result
+
+
+def repeat_failures(cycles: list[dict]) -> list[dict]:
+    """Issues that have failed multiple times — quarantine candidates."""
+    failures: dict[int, list] = defaultdict(list)
+    for c in cycles:
+        if not c.get("success") and c.get("issue"):
+            failures[c["issue"]].append({
+                "cycle": c.get("cycle"),
+                "reason": c.get("reason", ""),
+                "duration": c.get("duration", 0),
+            })
+    # Only issues with 2+ failures
+    return [
+        {"issue": k, "failure_count": len(v), "attempts": v}
+        for k, v in sorted(failures.items(), key=lambda x: -len(x[1]))
+        if len(v) >= 2
+    ]
+
+
+def duration_outliers(cycles: list[dict], threshold_multiple: float = 3.0) -> list[dict]:
+    """Cycles that took way longer than average — something went wrong."""
+    durations = [c["duration"] for c in cycles if c.get("duration", 0) > 0]
+    if len(durations) < 5:
+        return []
+    avg = sum(durations) / len(durations)
+    threshold = avg * threshold_multiple
+
+    outliers = []
+    for c in cycles:
+        dur = c.get("duration", 0)
+        if dur > threshold:
+            outliers.append({
+                "cycle": c.get("cycle"),
+                "issue": c.get("issue"),
+                "type": c.get("type"),
+                "duration": dur,
+                "avg_duration": round(avg),
+                "multiple": round(dur / avg, 1) if avg > 0 else 0,
+                "reason": c.get("reason", ""),
+            })
+    return outliers
+
+
+def triage_effectiveness(deep_triages: list[dict]) -> dict:
+    """How well is the deep triage performing?"""
+    if not deep_triages:
+        return {"runs": 0, "note": "No deep triage data yet"}
+
+    total_reviewed = sum(d.get("issues_reviewed", 0) for d in deep_triages)
+    total_refined = sum(len(d.get("issues_refined", [])) for d in deep_triages)
+    total_created = sum(len(d.get("issues_created", [])) for d in deep_triages)
+    total_closed = sum(len(d.get("issues_closed", [])) for d in deep_triages)
+    timmy_available = sum(1 for d in deep_triages if d.get("timmy_available"))
+
+    # Extract Timmy's feedback themes
+    timmy_themes = []
+    for d in deep_triages:
+        fb = d.get("timmy_feedback", "")
+        if fb:
+            timmy_themes.append(fb[:200])
+
+    return {
+        "runs": len(deep_triages),
+        "total_reviewed": total_reviewed,
+        "total_refined": total_refined,
+        "total_created": total_created,
+        "total_closed": total_closed,
+        "timmy_consultation_rate": round(timmy_available / len(deep_triages), 2),
+        "timmy_recent_feedback": timmy_themes[-1] if timmy_themes else "",
+        "timmy_feedback_history": timmy_themes,
+    }
+
+
+def generate_recommendations(
+    trends: dict,
+    types: dict,
+    repeats: list,
+    outliers: list,
+    triage_eff: dict,
+) -> list[dict]:
+    """Produce actionable recommendations from the analysis."""
+    recs = []
+
+    # 1. Success rate declining?
+    src = trends.get("success_rate_change")
+    if src is not None and src < -0.1:
+        recs.append({
+            "severity": "high",
+            "category": "reliability",
+            "finding": f"Success rate dropped {abs(src)*100:.0f}pp in the last 7 days",
+            "recommendation": "Review recent failures. Are issues poorly scoped? "
+                              "Is main unstable? Check if triage is producing bad work items.",
+        })
+
+    # 2. Velocity dropping?
+    vc = trends.get("velocity_change")
+    if vc is not None and vc < -5:
+        recs.append({
+            "severity": "medium",
+            "category": "throughput",
+            "finding": f"Velocity dropped by {abs(vc)} cycles vs previous week",
+            "recommendation": "Check for loop stalls, long-running cycles, or queue starvation.",
+        })
+
+    # 3. Duration creep?
+    dc = trends.get("duration_change")
+    if dc is not None and dc > 120:  # 2+ minutes longer
+        recs.append({
+            "severity": "medium",
+            "category": "efficiency",
+            "finding": f"Average cycle duration increased by {dc}s vs previous week",
+            "recommendation": "Issues may be growing in scope. Enforce tighter decomposition "
+                              "in deep triage. Check if tests are getting slower.",
+        })
+
+    # 4. Type-specific problems
+    for t, info in types.items():
+        if info["count"] >= 3 and info["success_rate"] < 0.5:
+            recs.append({
+                "severity": "high",
+                "category": "type_reliability",
+                "finding": f"'{t}' issues fail {(1-info['success_rate'])*100:.0f}% of the time "
+                           f"({info['count']} attempts)",
+                "recommendation": f"'{t}' issues need better scoping or different approach. "
+                                  f"Consider: tighter acceptance criteria, smaller scope, "
+                                  f"or delegating to Kimi with more context.",
+            })
+        if info["avg_duration"] > 600 and info["count"] >= 3:  # >10 min avg
+            recs.append({
+                "severity": "medium",
+                "category": "type_efficiency",
+                "finding": f"'{t}' issues average {info['avg_duration']//60}m{info['avg_duration']%60}s "
+                           f"(max {info['max_duration']//60}m)",
+                "recommendation": f"Break '{t}' issues into smaller pieces. Target <5 min per cycle.",
+            })
+
+    # 5. Repeat failures
+    for rf in repeats[:3]:
+        recs.append({
+            "severity": "high",
+            "category": "repeat_failure",
+            "finding": f"Issue #{rf['issue']} has failed {rf['failure_count']} times",
+            "recommendation": "Quarantine or rewrite this issue. Repeated failure = "
+                              "bad scope or missing prerequisite.",
+        })
+
+    # 6. Outliers
+    if len(outliers) > 2:
+        recs.append({
+            "severity": "medium",
+            "category": "outliers",
+            "finding": f"{len(outliers)} cycles took {outliers[0].get('multiple', '?')}x+ "
+                       f"longer than average",
+            "recommendation": "Long cycles waste resources. Add timeout enforcement or "
+                              "break complex issues earlier.",
+        })
+
+    # 7. Code growth
+    recent = trends.get("recent_7d", {})
+    net = recent.get("lines_net", 0)
+    if net > 500:
+        recs.append({
+            "severity": "low",
+            "category": "code_health",
+            "finding": f"Net +{net} lines added in the last 7 days",
+            "recommendation": "Lines of code is a liability. Balance feature work with "
+                              "refactoring. Target net-zero or negative line growth.",
+        })
+
+    # 8. Triage health
+    if triage_eff.get("runs", 0) == 0:
+        recs.append({
+            "severity": "high",
+            "category": "triage",
+            "finding": "Deep triage has never run",
+            "recommendation": "Enable deep triage (every 20 cycles). The loop needs "
+                              "LLM-driven issue refinement to stay effective.",
+        })
+
+    # No recommendations = things are healthy
+    if not recs:
+        recs.append({
+            "severity": "info",
+            "category": "health",
+            "finding": "No significant issues detected",
+            "recommendation": "System is healthy. Continue current patterns.",
+        })
+
+    return recs
+
+
+# ── Main ─────────────────────────────────────────────────────────────────
+
+def main() -> None:
+    cycles = load_jsonl(CYCLES_FILE)
+    deep_triages = load_jsonl(DEEP_TRIAGE_FILE)
+
+    if not cycles:
+        print("[introspect] No cycle data found. Nothing to analyze.")
+        return
+
+    # Run all analyses
+    trends = compute_trends(cycles)
+    types = type_analysis(cycles)
+    repeats = repeat_failures(cycles)
+    outliers = duration_outliers(cycles)
+    triage_eff = triage_effectiveness(deep_triages)
+    recommendations = generate_recommendations(trends, types, repeats, outliers, triage_eff)
+
+    insights = {
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "total_cycles_analyzed": len(cycles),
+        "trends": trends,
+        "by_type": types,
+        "repeat_failures": repeats[:5],
+        "duration_outliers": outliers[:5],
+        "triage_effectiveness": triage_eff,
+        "recommendations": recommendations,
+    }
+
+    # Write insights
+    INSIGHTS_FILE.parent.mkdir(parents=True, exist_ok=True)
+    INSIGHTS_FILE.write_text(json.dumps(insights, indent=2) + "\n")
+
+    # Current epoch from latest entry
+    latest_epoch = ""
+    for c in reversed(cycles):
+        if c.get("epoch"):
+            latest_epoch = c["epoch"]
+            break
+
+    # Human-readable output
+    header = f"[introspect] Analyzed {len(cycles)} cycles"
+    if latest_epoch:
+        header += f"  ·  current epoch: {latest_epoch}"
+    print(header)
+
+    print(f"\n  TRENDS (7d vs previous 7d):")
+    r7 = trends["recent_7d"]
+    p7 = trends["previous_7d"]
+    print(f"    Cycles:       {r7['count']:>3d}  (was {p7['count']})")
+    if r7["success_rate"] is not None:
+        arrow = "↑" if (trends["success_rate_change"] or 0) > 0 else "↓" if (trends["success_rate_change"] or 0) < 0 else "→"
+        print(f"    Success rate: {r7['success_rate']*100:>4.0f}%  {arrow}")
+    if r7["avg_duration"] is not None:
+        print(f"    Avg duration: {r7['avg_duration']//60}m{r7['avg_duration']%60:02d}s")
+    print(f"    PRs merged:   {r7['prs_merged']:>3d}  (was {p7['prs_merged']})")
+    print(f"    Lines net:    {r7['lines_net']:>+5d}")
+
+    print(f"\n  BY TYPE:")
+    for t, info in sorted(types.items(), key=lambda x: -x[1]["count"]):
+        print(f"    {t:12s}  n={info['count']:>2d}  "
+              f"ok={info['success_rate']*100:>3.0f}%  "
+              f"avg={info['avg_duration']//60}m{info['avg_duration']%60:02d}s")
+
+    if repeats:
+        print(f"\n  REPEAT FAILURES:")
+        for rf in repeats[:3]:
+            print(f"    #{rf['issue']}  failed {rf['failure_count']}x")
+
+    print(f"\n  RECOMMENDATIONS ({len(recommendations)}):")
+    for i, rec in enumerate(recommendations, 1):
+        sev = {"high": "🔴", "medium": "🟡", "low": "🟢", "info": "ℹ️ "}.get(rec["severity"], "?")
+        print(f"    {sev} {rec['finding']}")
+        print(f"       → {rec['recommendation']}")
+
+    print(f"\n  Written to: {INSIGHTS_FILE}")
+
+
+if __name__ == "__main__":
+    main()