quantified_self
OfficialMake AI growth measurable and actionable.
Data & Analytics#data-visualization#local-storage#learning-outcomes#growth-tracking#quantified-self#task-statistics#efficiency-analysis
AuthorSJTU-IPADS
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Quantified Self provides a data-driven framework to quantify AI growth, task completion, learning outcomes, and efficiency, turning subjective progress into concrete numbers that guide improvement.
Core Features & Use Cases
- Growth tracking across knowledge, capability, quality, and efficiency with clear percentile progress and trend visuals.
- Task statistics including completion rates, average quality, and distribution by task type.
- Learning outcomes cataloging new concepts, mastery levels, and knowledge graphs to show learning progression.
- Efficiency analysis covering response times, iteration counts, and tool usage to optimize workflows.
- Achievements and goals tracking with milestones, highlights, and progress updates.
- Local, private data storage and configurable reporting to protect privacy.
Quick Start
Initialize the quantified_self data model and begin automatic tracking of growth, tasks, and learning.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: quantified_self Download link: https://github.com/SJTU-IPADS/SkVM-data/archive/main.zip#quantified-self Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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