Automate rule and skill evolution in the Forge system from feedback data.
npx skills add https://github.com/zxpmail/ReqForge --skill evolution-engine-zxpmail
Please help me install this Agent Skill. Skill: evolution-engine Source: https://github.com/zxpmail/ReqForge/tree/main/core/skills/evolution-engine Command: npx skills add https://github.com/zxpmail/ReqForge --skill evolution-engine-zxpmail
This Skill automates the evolution of rules and skills within the Forge system, ensuring continuous improvement and adaptation based on user feedback and performance data.
Use the evolution-engine skill to check for evolution suggestions by running the command '/evolution-engine'.
High-intent search queries and answers about installing and using this skill.
Automating rule and skill evolution involves scanning user feedback and performance data to propose upgrades, manage new skill creation, and graduate common patterns into official rules. This ensures continuous improvement based on real usage data.
Feedback analysis works by automatically scanning user feedback signals to identify recurring patterns and performance issues. It suggests adjustments to skills with consistently low scores and proposes new skills for unaddressed recurring patterns.
Rule graduation is the process of upgrading common feedback patterns into official rules. You need it when frequent user feedback indicates a stable, recurring issue that should be formally codified rather than handled ad-hoc.
Yes, running the evolution engine requires a Forge system environment and existing feedback data. It operates within this specific ecosystem to monitor signals and propose rule or skill upgrades.
The best way to trigger evolution suggestions is to run the dedicated command within your environment. This action checks current feedback signals against performance data and proposes necessary skill adjustments or new skill creation.
You should avoid automated skill evolution if your environment lacks the Forge system setup or sufficient user feedback data. Without consistent performance metrics and feedback signals, the engine cannot accurately propose meaningful rule or skill upgrades.
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