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Amplifier Learner Tools

Log AI executions and learn from feedback using JSONL logs and JSON recommendations.

1|Updated Oct 22, 2025
Search Tags:#decision making#personalization#AI learning#self-improving AI#feedback loops#recipe optimization#metacognition
One-click install
npx skills add https://github.com/imehr/skills --skill amplifier-learner-tools
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Amplifier Learner Tools
Source: https://github.com/imehr/skills/tree/main/skills/learning/learner-tools
Command: npx skills add https://github.com/imehr/skills --skill amplifier-learner-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python (>=3.11), pydantic.

What problem does it solve?

This skill provides a suite of metacognitive tools that enable AI recipes to learn from user feedback and execution data, solving the problem of static AI behavior. It allows AI to adapt, improve, and personalize its performance over time, reducing the need for constant manual adjustments and leading to more efficient, tailored outcomes.

Core Features & Use Cases

  • Decision Historian: Learns which strategic choices lead to the best outcomes in specific contexts.
  • Style Learner: Adapts AI's writing style and voice based on user edits and preferences.
  • Meta-Recipe Tuner: Identifies performance bottlenecks in multi-stage AI pipelines for optimization.
  • Knowledge Compressor: Learns to extract and prioritize information from source material based on feedback.
  • Use Case: "My AI-generated emails often need minor tone adjustments. Use the Style Learner to analyze my edits and automatically adapt the AI's writing style to match my preferences."

Quick Start

Use the Amplifier Learner Tools to log an AI decision and its outcome.

Frequently Asked Questions about Amplifier Learner Tools

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I make AI adapt to my feedback and improve its decisions over time?▼

Amplifier Learner Tools logs AI decisions and user feedback to learn patterns. It tracks which choices succeed in specific contexts, then adjusts future recommendations based on those outcomes, reducing manual tuning.

Can I automatically adjust AI writing style based on my edits?▼

The Style Learner analyzes your edits to understand tone and voice preferences, then adapts the AI's output to match your style without requiring manual configuration each time.

How do I identify bottlenecks in multi-stage AI pipelines?▼

Meta-Recipe Tuner logs execution data across pipeline stages and analyzes performance metrics to pinpoint which steps slow down processing, enabling targeted optimization.

Does this work with Python 3.11 and later versions?▼

Yes, Amplifier Learner Tools requires Python 3.11 or higher and Pydantic for data validation, making it compatible with recent Python environments.

How do I extract and prioritize information based on what users actually need?▼

Knowledge Compressor learns from feedback on which extracted facts prove most useful, then adjusts extraction rules to surface higher-priority information in future runs.

What's the best way to implement a learning feedback loop for AI recipes?▼

Log execution decisions and outcomes to JSONL files, feed user feedback into learner components, and let the system generate JSON recommendations that update behavior incrementally.

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