Agent skill · operations · aiskillstore
learn
Extract and persist insights from the current conversation to the knowledge base
Why this skill is useful
Adds a structured process for extracting and documenting insights that the AI wouldn't generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Learn Extract insights from the current conversation and persist them to the project's knowledge base. Usage --deep Mode When --deep is specified, the extraction runs in a forked context using the Explore agent: More thorough codebase analysis to find related patterns Cross-references with existing knowledge Validates discoveries against actual code Keeps analysis chatter out of your main conversation Use --deep when you've had a significant debugging session or made architectural decisions you want fully documented. What This Does Analyzes the conversation context to identify: Patterns: Approaches that worked well in this project Quirks: Project-specific oddities or non-standard behaviors discovered Decisions: Architectural or implementation choices made with their rationale These insights survive session boundaries and context compaction, building a persistent understanding of the project over time. Instructions 1. Analyze the conversation looking for: Successful problem-solving approaches that could apply again Unusual behaviors or gotchas discovered about the codebase Decisions made and why (architectural choices, library selections, patterns chosen) 2. Categorize each insight as pattern, quirk, or decision 3. Format and append to the appropriate file in knowledge/learnings/: patterns.md - What works well quirks.md - Unexpected behaviors decisions.md - Choices with rationale 4. Update metadata in each file's frontmatter (entrycount, lastupdated) 5. Update state in knowledge/state.json: Set lastextraction to current timestamp Increment extractioncount Reset queriessinceextraction to 0 6. Report what was learned to the user Entry Format Pattern Entry Quirk Entry Decision Entry Confidence Levels high: Clear, verified insight with strong evidence medium: Reasonable inference, likely correct low: Tentative observation, needs validation Only high and medium confidence insights influence routing decisions. Steps 1. Review the conversation for extractable insights 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/learn