ai-analyst-lab/.claude/skills/patterns

Skill: Patterns

Purpose

Browse and search recurring patterns discovered across analyses. Patterns are auto-extracted after each analysis archive and represent behaviors that appear consistently in the data.

When to Use

• User says /patterns or "what patterns have we seen?"
• During analysis, to check if a finding matches a known pattern
• At session start, to remind the user of established behaviors

Invocation

/patterns — list patterns for the active dataset /patterns --global — list patterns across all datasets /patterns search={term} — search patterns by keyword /patterns {id} — show full details for a specific pattern

Instructions

Step 1: Load Patterns

1. Read .knowledge/analyses/_patterns.yaml for the active dataset.
2. If --global flag: also read .knowledge/global/cross_dataset_observations.yaml.
3. If empty: "No patterns recorded yet. Complete a few analyses and patterns will emerge."

Step 2: Execute Command

List patterns (/patterns):

• Filter to active dataset (unless --global)
• Sort by occurrences descending (most established first)
• Display as a table: type, description, occurrences, confidence, last seen
• Show total count

Show specific (/patterns {id}):

• Display: description, type, all evidence (with analysis IDs), dimensions, metrics, suggested investigation
• Offer: "Want to investigate this pattern further?"

Search (/patterns search={term}):

• Search across description, dimensions, metrics, tags
• Display matching patterns as a table

Global (/patterns --global):

• Include cross-dataset observations alongside per-dataset patterns
• Note which dataset each pattern was observed in

Step 3: Contextual Suggestions

After displaying patterns:

• "Want to check if {pattern} still holds in the current data?"
• "Want to use {pattern} as context for a new analysis?"
• "This pattern was last seen {N} days ago — may need revalidation."

Pattern Extraction (Auto)

After each analysis archive (triggered by archive-analysis skill), scan the new analysis for potential patterns:

1. Compare new findings to existing patterns:
   • If a finding matches an existing pattern → increment occurrences, update last_seen
   • If a finding is new but could extend a pattern → add as evidence
2. Look for NEW patterns:
   • Same metric behavior across 2+ analyses → candidate pattern
   • Same segment consistently outperforming → candidate pattern
   • Recurring anomaly at similar times → candidate pattern
3. Write updated patterns back to _patterns.yaml

Minimum 2 occurrences to create a pattern. Single-occurrence findings are just findings, not patterns.

Edge Cases

• No patterns: Suggest running more analyses
• Stale patterns (last_seen >60 days): Flag as potentially outdated
• Contradictory patterns: Flag and suggest investigation
• Too many patterns (>50): Show top 20 by occurrences, offer pagination