amerob/ultimate-prompt-engineering-playbook

A collection of 114 notebooks covering modern prompt engineering techniques. Hands-on, runnable examples with real-world use cases, benchmarks, and failure analysis. Supports multiple LLMs, practical experimentation and systematic prompt optimization.

22
Stars
9
Forks
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Radar Score
Declining
Momentum

Scoring Breakdown

Impact0.0
Velocity0.0
Health0.0

Growth Metrics

7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Jupyter Notebook

Why it ranks

Momentum is cooling relative to the rest of the index.

LLM Frameworks · inference engine

🛡️ Enterprise Readiness & Commercial Safety

Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Permissive (Commercial Friendly)
Safe for commercial use, modification, and distribution.
Health Grade
C
Based on maintenance, issue response, and release cadence.
Radar Score
0.0
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.

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