Identify conversation patterns and update agent context after user confirmation.
npx skills add https://github.com/flesler/dotfiles --skill learn-flesler
Please help me install this Agent Skill. Skill: learn Source: https://github.com/flesler/dotfiles/tree/main/home/.cursor/skills/learn Command: npx skills add https://github.com/flesler/dotfiles --skill learn-flesler
This Skill helps capture and apply learnings from conversation to update the agent's context with user preferences, corrections, and patterns, enabling more accurate and concise responses over time.
Learn from this conversation and update the agent's context with relevant patterns after user confirmation.
High-intent search queries and answers about installing and using this skill.
Conversation context is saved by identifying actionable learnings like recurring corrections and preferences. The agent assesses history, lists update candidates, reports findings, and executes minimal context changes only after obtaining user confirmation.
The memory update workflow involves assessing existing skills, listing learning candidates, reporting findings to the user, and executing targeted context updates. It enforces minimal changes with token efficiency and safety in mind to refine future interactions.
Capture learnings when clear patterns, preferences, or corrections occur that should influence ongoing behavior. Context augmentation is beneficial when memory of user intent helps produce more accurate and concise responses over time.
Updating context does not require automatically introducing new skills. The workflow emphasizes minimal changes, selective reading, and rare introduction of new skills to maintain token efficiency and safety while refining existing behavior.
You can review proposed changes because the workflow mandates reporting findings to the user and obtaining confirmation before executing. This ensures targeted context updates happen only with explicit user approval.
The best way to refine context is through minimal, targeted updates based on identified patterns. The agent selectively reads history, reports concise findings, and executes only confirmed changes to maximize token efficiency and safety.
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