What problem does it solve? Learning sessions with an AI assistant produce valuable insights, decisions, and code artifacts that are easily lost once the conversation ends. This Skill converts a conversation into a permanent, structured Obsidian note so learnings, trade-offs, and open questions are preserved for future recall. ## Core Features & Use Cases - Session Summarization: Analyzes the full conversation and extracts 3-5 significant learnings using explicit significance criteria (new concepts, changed approaches, solved blockers, reusable tools, mental model shifts). - Structured Output Template: Generates a markdown file with YAML frontmatter (tags, date, related links, status, confidence), key learnings with code artifacts, a decisions-made table, remaining questions, and next-step actions. - Confidence Tagging: Labels each learning as Confirmed, Provisional, or Hypothesis so future readers know what to trust. - Use Case: After a long debugging session on Rust async patterns, invoke the skill to produce a note saved directly to your Obsidian vault, complete with code snippets, a decision table comparing Tokio vs async-std, and follow-up tasks. ## Quick Start Ask the assistant to create an Obsidian note from this session, optionally scoping it with a focus topic such as the Rust patterns discussed.