Ducklake Temporal Introspection
Version: 1.0.0 Status: Production Ready Created: 2025-12-21 Canonical Seed: 0x6475636b6c616b65
Overview
Loads temporal analysis from Subagent 1 (Data Collector) and provides introspection functions for analyzing ducklake mentions across Claude conversation history.
Purpose
Enable temporal navigation and clustering analysis of ducklake mentions with:
- 18 total mentions across 12 sessions
- 7-day span (2025-12-15 to 2025-12-22)
- Peak activity detection
- Session duration analysis
- Project distribution tracking
Data Sources
- Primary:
/Users/bob/ies/ducklake_temporal_analysis.json - Secondary:
~/.claude/history.jsonl - Schema: VERS_DUCKLAKE_SCHEMA.sql
Functions
query_by_timestamp(range: str) -> dict
Query mentions within a timestamp range.
# Example
result = query_by_timestamp("2025-12-21 19:00:00", "2025-12-21 23:00:00")
# Returns: {"mentions": 5, "sessions": 3, "lines": [1035, 1036, 1067, 1073, 1093]}
Implementation:
import json
from datetime import datetime
def query_by_timestamp(start: str, end: str) -> dict:
with open("/Users/bob/ies/ducklake_temporal_analysis.json") as f:
data = json.load(f)
start_dt = datetime.fromisoformat(start)
end_dt = datetime.fromisoformat(end)
mentions = []
for sample in data["detailed_samples"]:
ts = datetime.fromisoformat(sample["timestamp"])
if start_dt <= ts <= end_dt:
mentions.append(sample)
return {
"mentions": len(mentions),
"sessions": len(set(m["sessionId"] for m in mentions)),
"lines": [m["line_number"] for m in mentions],
"contexts": [m["display_text"][:100] for m in mentions]
}
session_timeline(session_id: str) -> dict
Get timeline for a specific session.
result = session_timeline("2847f140-bff5-4f82-8fc7-6f6abd269d8f")
# Returns: {
# "duration_minutes": 84.25,
# "mention_count": 2,
# "first_mention": "2025-12-21 19:45:19",
# "last_mention": "2025-12-21 21:09:34"
# }
project_distribution() -> dict
Get mention distribution by project.
result = project_distribution()
# Returns: {
# "/Users/bob/ies": {"count": 9, "percentage": 50.0},
# "/Users/bob/ies/music-topos": {"count": 8, "percentage": 44.4},
# "/Users/bob/ies/citadel_protocol": {"count": 1, "percentage": 5.6}
# }
peak_activity_analysis() -> dict
Identify temporal clustering patterns.
result = peak_activity_analysis()
# Returns: {
# "peak_hour": "2025-12-22 00:00",
# "peak_hour_count": 4,
# "peak_day": "2025-12-22",
# "peak_day_count": 6,
# "total_active_hours": 12,
# "total_active_days": 5
# }
Usage Example
from skills.ducklake_temporal_introspection import *
# Find all mentions in peak hour
peak = peak_activity_analysis()
mentions = query_by_timestamp(f"{peak['peak_hour']}:00:00", f"{peak['peak_hour']}:59:59")
print(f"Peak activity: {mentions['mentions']} mentions")
for context in mentions['contexts']:
print(f" - {context}")
# Analyze session durations
for session in get_all_sessions():
timeline = session_timeline(session['sessionId'])
if timeline['duration_minutes'] > 60:
print(f"Long session: {timeline['duration_minutes']:.1f} min")
Skills Dependencies
- mcp-builder (MCP tool integration)
- skill-creator (skill scaffolding)
Integration Points
- DuckDB: Query ducklake.db for enhanced temporal analysis
- VERS System: Cross-reference with verse_agent_health
- Color Retromap: Map timestamps to battery cycle colors
Key Statistics
- Total mentions: 18
- Unique sessions: 12
- Unique projects: 3
- Date range: 7 days
- Average mentions per active day: 3.6
- Average mentions per active hour: 1.5
Next Steps
- Integrate with DuckDB temporal versioning
- Add reafferent detection filtering (GAY_SEED=1069)
- Cross-reference with VERS agent events
- Build time-travel query interface
Canonical Seeds
const GAY_SEED = UInt64(1069)
const DUCKLAKE_SEED = 0x6475636b6c616b65
GF(3) Distribution
This skill operates in the RED (GF3=0) temporal navigation category:
- 27.8% of mentions
- Focus: Temporal versioning, time-travel, history navigation
Skill Type: Temporal Analysis Color: RED Polarity: GF(3) = 0 Access Pattern: Read-only introspection