intelligence-learning

name	intelligence-learning
description	Learning cycle for the Adaptive Intelligence Engine. Implements the Observe-Retrieve-Judge-Distill-Consolidate-Apply cycle for detecting and applying user preference patterns.

Overview

The learning system detects patterns in user behavior and plugin outcomes, then injects learned preferences into future plugin runs. It operates in three tiers shipped sequentially:

1. Taste Learning (Tier 1) — output preference detection
2. Workflow Optimization (Tier 2) — plugin chain detection
3. Confidence-Gated Autonomy (Tier 3) — graduated trust

The Cycle

OBSERVE → RETRIEVE → JUDGE → DISTILL → CONSOLIDATE → APPLY
   ↑                                                    │
   └────────────────── feedback ─────────────────────────┘

Stage	What Happens	When
Observe	Hooks capture events	During command run
Retrieve	Pull relevant past patterns	Before next run
Judge	Compare outcome to patterns	After post_command
Distill	Extract generalizable insight	After 3+ observations
Consolidate	Store with confidence score	After distillation
Apply	Inject as instruction	Before next matching run

Confidence Mechanics

• Formula: confirmations / max(1, confirmations + rejections * 2)
• New patterns start at confidence 0.0 (candidate)
• Applied when confidence >= threshold AND observations >= 3
• Rejections weigh double to prevent bad patterns from persisting

Pattern Lifecycle

candidate (conf < 0.5) → active (conf >= 0.5, auto-applied with notice)
                       → approved (user /intel:approve, conf locked at 1.0)
                       → rejected (user rejected or conf < 0.3)

Applying Patterns

When a plugin command runs:

1. Query patterns table for matching plugin + command (or cross-plugin patterns)
2. Filter to status IN ('active', 'approved')
3. For each matching pattern, inject its instruction text into the command context
4. Notify user: [Intel] Applying learned preference: "{description}"

Cross-Plugin Promotion

Patterns referencing a Company or Contact are promoted to the Memory Engine:

• taste + company → Memory Engine category preference
• workflow → Memory Engine category workflow
• Confidence conversion: round(confidence * 100)