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temporal-circadian-patterns

Analyzes timestamped activity data to detect circadian rhythms, weekly cycles, bursts, and engagement styles.

13|2|Updated Feb 10, 2026
Search Tags:#time series#pandas#activity analysis#circadian rhythm#seasonal decomposition#engagement patterns#burst detection
One-click install
npx skills add https://github.com/aaddrick/written-voice-replication --skill temporal-circadian-patterns-aaddrick
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: temporal-circadian-patterns
Source: https://github.com/aaddrick/written-voice-replication/tree/main/.claude/skills/temporal-circadian-patterns
Command: npx skills add https://github.com/aaddrick/written-voice-replication --skill temporal-circadian-patterns-aaddrick

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, statsmodels, scikit-learn.

What problem does it solve? Raw timestamps in user activity data hide behavioral rhythms that are hard to see without structured analysis. This Skill aggregates timestamps into hourly, daily, weekly, and seasonal bins to reconstruct activity profiles, measure circadian regularity, detect bursts, and classify engagement style. ## Core Features & Use Cases - Multi-Granularity Binning: Aggregates timestamps into hour-of-day, day-of-week, daily, weekly, and monthly distributions, including a 7x24 activity heatmap matrix. - Statistical Pattern Detection: Measures circadian regularity via cross-day cosine similarity, tests weekday-vs-weekend differences with Kruskal-Wallis and Mann-Whitney U, and runs STL seasonal decomposition. - Burst Detection & Engagement Classification: Identifies activity spikes above a rolling baseline and classifies users as routine-driven, binge-pattern, weekend-warrior, steady-contributor, and more. - Use Case: Given a Reddit export or commit log with thousands of timestamps, produce a report showing peak activity hours, weekly cycles, burst periods, and an overall engagement style classification. ## Quick Start Analyze the timestamps in my dataset to reconstruct the activity profile, detect circadian and weekly patterns, and write the findings report.

Frequently Asked Questions about temporal-circadian-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze hourly activity patterns from timestamps in Python?▼

Convert timestamps with pandas to_datetime, normalize timezones using tz_convert, then bin with dt.hour and dt.dayofweek value counts. Build a 7x24 day-of-week by hour-of-day matrix as the primary heatmap visualization.

How to detect activity bursts in time series data?▼

Resample timestamps into fixed windows, compute a rolling mean and standard deviation baseline, and flag windows exceeding mean plus 2 standard deviations. Report burst rate as a fraction of total windows for interpretability.

Why is timezone normalization required before hour-of-day analysis?▼

Timestamps stored in UTC misalign circadian patterns by the local offset, so a 3 AM UTC activity spike may actually be 7 PM local time. Always use tz_convert with zoneinfo or pytz rather than manual timedelta arithmetic, which breaks at DST transitions.

What is the minimum data needed for temporal pattern analysis?▼

At least 20 timestamps are required for any analysis, 50 or more across 7 distinct days for hourly bins, 4 full weeks for weekly cycle detection, and 2 full seasonal cycles for STL decomposition. Below these thresholds, report raw counts with low-confidence flags.

Can activity timing reveal a user's location or sleep schedule?▼

No. Activity timing shows when actions occurred, not where the user is or whether they were sleeping. Low-activity windows have many explanations including timezone differences and night shifts, so location and health inferences are explicitly out of scope.

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