kli-reflection

What problem does it solve?

This Skill addresses the challenge of systematically learning from AI task execution by providing a structured framework for evaluating pattern effectiveness and updating knowledge bases based on concrete evidence.

Core Features & Use Cases

• Evidence-Based Learning: Ensures all pattern evaluations are grounded in observable task data, moving beyond subjective opinions.
• Pattern Evaluation: Provides clear criteria and methodology for classifying patterns as helpful, harmful, or neutral based on observed outcomes.
• Harm Signal Tiers: Defines distinct levels of harm with corresponding response actions, from auto-correction to flagging for review.
• New Pattern Discovery: Guides the identification and documentation of novel, reusable approaches encountered during task execution.
• Use Case: After an AI agent attempts to refactor code using a new pattern, this Skill analyzes the observations from that task to determine if the pattern saved time, introduced errors, or was ineffective, then updates the agent's knowledge base accordingly.

Quick Start

Use the kli-reflection skill to analyze the observations from the last task and update pattern effectiveness ratings.