AgentSkillsCN

Patterns

模式

SKILL.md

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