Menu
Identifies behavioral patterns from recent conversations and reaction feedback, updates learned-patterns.md when 3+ observations confirm a pattern.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | pattern-learning |
| description | Identifies behavioral patterns from recent conversations and reaction feedback, updates learned-patterns.md when 3+ observations confirm a pattern. |
| allowed-tools | Read, Write, Edit, Bash |
Review recent interactions and reaction feedback. Update agent-data/learned-patterns.md when you see something recurring 3+ times.
agent-data/memories/): jokes that landed/flopped, successful/failed approaches, repeated mistakesagent-data/reaction-feedback.jsonl): emoji reactions on your messages. Interpret emoji names semantically (e.g., "omegaLUL" = positive, "sadge" = negative). Compare GIF vs text reaction rates, roast vs earnest, per-person patterns.agent-data/learned-patterns.md): what's already capturedAdd when you see the same thing work/fail 3+ times with clear cause-and-effect. Don't add one-off observations, vague patterns, or things already in the file.
Categories in learned-patterns.md: Humor, Conversation, Per-Person, Games, Self-Corrections, What Lands, Timing, Meta-Patterns. Pick the right section.
Hypothesis creation and review is owned by the hypothesis-review skill — if you notice something that's a testable prediction rather than a confirmed pattern, note it in today's memory for hypothesis-review to pick up.
After significant interactions, when you notice repeated mistakes, or during idle reflection.
Cooldown: 24 hours
Tags transcript message threads with semantic concepts (roast, game-recommendation, etc.) so past conversations can be searched by idea, not just keywords.
Captures notable interactions missed during live conversation — new person info, game preferences, boundaries, jokes that landed/flopped, relationship insights.
The single owner of the hypothesis lifecycle — creates, tests, promotes, and kills hypotheses. Uses extensions as programmatic data collectors and reviews their output.
Consolidates impression files by applying weight decay, removing redundant entries, and enforcing per-user limits. Maintains relationship file summaries.
Consolidates old memories by removing duplicates, promoting recurring patterns to learned-patterns.md or relationship files, and archiving outdated entries.
Evaluates overall effectiveness — are idle behaviors producing value, are extensions working, what's blocking improvement. Higher-level thinking about approach, not individual conversations.