Compare AI agent predictions against seven-day content performance data.
npx skills add https://github.com/teodorboev/socialai --skill self-evaluation
Please help me install this Agent Skill. Skill: self-evaluation Source: https://github.com/teodorboev/socialai/tree/main/.opencode/skills/self-evaluation Command: npx skills add https://github.com/teodorboev/socialai --skill self-evaluation
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.
Initiate a post-mortem evaluation for a recently published piece of content.
High-intent search queries and answers about installing and using this skill.
Evaluating AI agent predictions against actual content performance data requires automating a post-mortem analysis that compares predicted engagement with real-world results collected over a seven-day period. This identifies discrepancies and quantifies agent accuracy.
Automated post-mortem analysis for machine learning agents is a process that compares agent predictions against actual post performance after a set period. It identifies which agents were accurate and generates specific lessons for continuous calibration.
Continuous improvement feedback loops require integration with content publishing platforms and performance data APIs. The system feeds discrepancies and learnings back into the pipeline, enabling all agents to become smarter over time.
Starting a self-evaluation cycle involves initiating a post-mortem evaluation for a recently published piece of content. After gathering data for a week, the system analyzes performance, determines prediction accuracy, and provides actionable feedback to agents.
The best way to automate feedback loops for AI agent continuous improvement is using a system that identifies discrepancies between predictions and outcomes, then automatically feeds those learnings back into the entire agent pipeline for continuous calibration.
Performance data APIs are required to analyze AI agent accuracy over a seven-day period. The evaluation process depends on integrating with these APIs to collect actual content performance data and compare it against agent predictions.
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