Tracks and analyzes the Autonomy Score using local markdown files.
npx skills add https://github.com/mentilead/growthOS --skill score
Please help me install this Agent Skill. Skill: score Source: https://github.com/mentilead/growthOS/tree/main/skills/autonomy-tracker Command: npx skills add https://github.com/mentilead/growthOS --skill score
This Skill tracks and analyzes the Autonomy Score, a key performance indicator measuring the percentage of operational decisions made without human intervention, helping to optimize AI efficiency and identify areas for improvement.
Use the autonomy tracker skill to update the log with 5 additional autonomous decisions for the current month.
High-intent search queries and answers about installing and using this skill.
AI autonomy tracking measures the percentage of operational decisions made without human intervention by logging autonomous decisions, human escalations, and agent failures over a rolling period. This helps identify efficiency gaps and areas for improvement.
Log autonomous decisions and human escalations by updating decision counts in local markdown files. This persistent state tracking records operational choices and agent failures, enabling accurate trend analysis and ongoing performance evaluation.
AI performance trend analysis visualizes the autonomy score's progression over time and identifies top escalation drivers across months. This contextual review highlights recurring operational bottlenecks affecting autonomous decision rates.
Yes, AI metrics tracking utilizes local markdown files for persistent state and decision logging. This lightweight approach ensures operational decision data is stored locally without requiring external databases or complex infrastructure.
Review the current Autonomy Score with chapter-appropriate context during experimentation phases to evaluate whether AI agents meet efficiency targets. This snapshot contextualizes performance within specific development milestones.
Decision tracking matters for AI agent efficiency because it quantifies the autonomy percentage, revealing how often agents operate without human input. This data identifies escalation drivers and guides optimization for better performance.
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