v1.0: The Gatekeeper (Initial Release)

⚙️ Mastery Decision Engine v1.0 — GATEKEEPER

The official v1.0 release of the Mastery Decision Engine. This project implements a flawless, deterministic decision model for tracking learner mastery, built entirely using the B.L.A.S.T Protocol and A.N.T. 3-Layer Architecture.

🚀 Key Architectural Features

• 70/30 Recency-Weighted Scoring (Sᵥ): Mathematically respects continuous improvement while distrusting historical failures.
• Circuit Breaker: Hard 3-attempt lock to prevent brute-forcing and "lucky guessing".
• Ironclad Mastery Gate: Fixed MASTERY_THRESHOLD = 85. The gate strictly enforces that trending upward is not the same as mastery.
• Pure Determinism: 100% Python standard library logic. No APIs, no randomness, no false positives.

🐳 Containerization

The engine is production-ready via Docker using a python:3.11-slim base image with absolutely zero external dependencies.

📋 Scenarios Validated

1. Sudden Drop: Correctly identifies algorithmic collapse (STATUS: RETRY)
2. Steady Climb: Correctly blocks learners who fall short of true mastery despite improving (STATUS: PIVOT)
3. Stagnant: Correctly halts flat growth trajectories (STATUS: PIVOT)

Developed under strict standard operating procedures and locked constitution invariants (spec v3).