MECW Patterns

Overview

Maximum Effective Context Window (MECW) patterns provide the theoretical foundations and practical utilities for managing context window usage to prevent hallucinations. The core principle: Never use more than 50% of total context window for input content.

When to Use

• Need to prevent hallucinations in long-running sessions
• Managing context-heavy workflows
• Building systems that process large amounts of data
• Want proactive context pressure monitoring
• Require safe token budget calculation

Core Principle: The 50% Rule

Context pressure increases non-linearly as usage approaches limits. Exceeding 50% of context window significantly increases hallucination risk.

Pressure Levels

Level	Usage	Effect	Action
LOW	<30%	Optimal performance, high accuracy	Continue normally
MODERATE	30-50%	Good performance, within MECW	Monitor closely
HIGH	50-70%	Degraded performance, risk zone	Optimize immediately
CRITICAL	>70%	Severe degradation, high hallucination	Reset context

Quick Start

Basic Pressure Check

from leyline import calculate_context_pressure

pressure = calculate_context_pressure(
    current_tokens=80000,
    max_tokens=200000
)
print(pressure)  # "MODERATE"

Full Compliance Check

from leyline import check_mecw_compliance

result = check_mecw_compliance(
    current_tokens=120000,
    max_tokens=200000
)

if not result['compliant']:
    print(f"Overage: {result['overage']:,} tokens")
    print(f"Action: {result['action']}")

Continuous Monitoring

from leyline import MECWMonitor

monitor = MECWMonitor(max_context=200000)

# Track usage throughout session
monitor.track_usage(80000)
status = monitor.get_status()

if status.warnings:
    for warning in status.warnings:
        print(f"[WARN] {warning}")

if status.recommendations:
    print("\nRecommended actions:")
    for rec in status.recommendations:
        print(f"  • {rec}")

Detailed Topics

For detailed implementation patterns:

• MECW Theory - Theoretical foundations, research basis, thresholds
• Monitoring Patterns - Integration patterns, quota management, token estimation
• Prevention Strategies - Early detection, compression, delegation, progressive disclosure

Best Practices

1. Plan for 40%: Design workflows to use ~40% of context, leaving buffer
2. Buffer for Response: Leave 50% for model reasoning + response generation
3. Monitor Continuously: Check context at each major step
4. Fail Fast: Abort and restructure when approaching limits
5. Document Aggressively: Keep summaries for context recovery after reset

Integration with Other Skills

This skill provides foundational utilities referenced by:

• conservation:context-optimization - Uses MECW for optimization decisions
• conjure:delegation-core - Uses MECW for delegation triggers
• Plugin authors building context-aware systems

Reference in your skill's frontmatter:

dependencies: [leyline:mecw-patterns]

Exit Criteria

• Context pressure monitored before major operations
• MECW compliance checked when loading large content
• Safe budget calculated before batch operations
• Recommendations followed when warnings issued
• Context reset triggered before CRITICAL threshold