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pattern-detect

Analyzes historical task, energy, and sleep data to detect recurring behavior patterns.

Updated May 1, 2026
Search Tags:#pattern detection#weekly review#memory profiling#habit analysis#energy scheduling#correlation mining#decision insights
One-click install
npx skills add https://github.com/picsky/flowos --skill pattern-detect-picsky
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pattern-detect
Source: https://github.com/picsky/flowos/tree/main/templates/skills/pattern-detect
Command: npx skills add https://github.com/picsky/flowos --skill pattern-detect-picsky

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you discover recurring behavior, energy-based cycles, and trend signals from your historical data, so you can understand your habits instead of guessing.

Core Features & Use Cases

  • Behavior pattern detection: Identifies periodic, trend, and correlation patterns (e.g., task delay tendencies on specific weekdays, energy shifts across weeks, and relationships between sleep/energy and follow-up performance).
  • Evidence-backed ranking: Produces each pattern with evidence and a credibility level (high/medium/low), including handling contradictions with your self-understanding.
  • Profile knowledge update: Updates your saved pattern record so future weekly/monthly reviews and related triggers can reuse the findings.
  • Use cases: Weekly or monthly review analysis, user-triggered questions like “What patterns do I have?”, and analyst-led verification when suspicious patterns emerge.

Quick Start

Use pattern-detect to analyze your last several weeks of activity and return the newly found behavior patterns with evidence and confidence levels.

Frequently Asked Questions about pattern-detect

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

FAQPage Schema
How do I detect recurring behavior patterns from my weekly review data?▼

You can discover recurring habits by analyzing historical task, energy, sleep, and decision records to identify periodic, trend, and correlation patterns across your weekly or monthly review data.

What kind of habit analysis can I do with task delay and energy scheduling records?▼

Habit analysis can identify task delay tendencies on specific weekdays, energy shifts across weeks, and relationships between sleep, energy levels, and your follow-up performance.

How does correlation mining work for personal energy and sleep data?▼

Correlation mining examines your historical records to find relationships between variables like sleep and energy, computing evidence and confidence levels to rank newly found patterns.

Can I use memory profiling to remember previously detected behavior patterns?▼

Memory profiling updates your stored pattern record by filtering out previously recorded patterns and writing new findings back to profile memory for future review reuse.

What data do I need for decision insights and pattern detection?▼

Decision insights require historical task records, energy logs, sleep data, and decision entries to perform data-driven detection across periodic, trend, and correlation dimensions.

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