Cluster recurring coding patterns into project-specific rules for review and promotion.
npx skills add https://github.com/jhlee0409/all-for-claudecode --skill afc-learner
Please help me install this Agent Skill. Skill: afc:learner Source: https://github.com/jhlee0409/all-for-claudecode/tree/main/skills/learner Command: npx skills add https://github.com/jhlee0409/all-for-claudecode --skill afc-learner
This Skill automates the process of identifying, reviewing, and promoting recurring coding patterns and corrections observed during development sessions into reusable project rules, thereby standardizing code quality and consistency.
.claude/rules/afc-learned.md.const over let for immutable variables, /afc:learner can review these suggestions, and if approved, add a permanent rule to enforce this best practice across the project.Review pending learned patterns and promote approved ones to project rules.
High-intent search queries and answers about installing and using this skill.
You can promote learned coding patterns into project rules by using a mechanism that detects recurring corrections from user sessions, clusters them into actionable rules, and saves approved patterns to a project-specific rule file.
Pattern learning works by analyzing detected signals from development sessions and clustering recurring coding corrections and preferences into actionable rules for review and promotion to project standards.
You can review pending learned patterns by checking the learner status, examining the clustered signals from your sessions, and approving the corrections to be saved as permanent rules in your project rule file.
Yes, you can disable signal collection by using the management commands to turn off the learning mechanism, clear the pending queue of detected patterns, or check the current status of the learner.
Promoted coding standards and rules are stored in a project-specific rule file located at `.claude/rules/afc-learned.md`, ensuring that approved patterns are enforced consistently across the entire project.
The best way to standardize code quality is to let the AI detect recurring corrections during sessions, cluster them into rules, and promote the approved best practices into a permanent project rule file for consistent enforcement.
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