self-evaluation

What problem does it solve?

This Skill addresses the challenge of AI agents operating in a vacuum, making predictions without learning from actual outcomes, leading to stagnant performance and persistent errors.

Core Features & Use Cases

• Automated Post-Mortem Analysis: Compares agent predictions against real-world post performance after a set period (7 days).
• Agent-Specific Feedback: Identifies which agents were accurate and which were not, providing specific lessons for improvement.
• Continuous Calibration: Feeds discrepancies and learnings back into the system, enabling all agents to become smarter over time.
• Use Case: After a social media post is published and gathers data for a week, this Skill analyzes its performance, determines if the predicted engagement was accurate, if the hashtags used were effective, and if the visual style resonated, then provides actionable feedback to the respective agents.

Quick Start

Initiate a post-mortem evaluation for a recently published piece of content.