Progressive Loading Patterns

Overview

Progressive loading provides standardized patterns for building skills that load modules dynamically based on context, user intent, and available token budget. This prevents loading unnecessary content while ensuring required functionality is available when needed.

The core principle: Start minimal, expand intelligently, monitor continuously.

When to Use

Use progressive loading when building skills that:

• Cover multiple distinct workflows or domains
• Need to manage context window efficiently
• Have modules that are mutually exclusive based on context
• Require MECW compliance for long-running sessions
• Want to optimize for common paths while supporting edge cases

Quick Start

Basic Hub Pattern

## Progressive Loading

**Context A**: Load `modules/context-a-workflow.md` for scenario A
**Context B**: Load `modules/context-b-workflow.md` for scenario B

**Always Available**: Core utilities, exit criteria, integration points

Context-Based Selection

from leyline import ModuleSelector, MECWMonitor

selector = ModuleSelector(skill_path="my-skill/")
modules = selector.select_modules(
    context={"intent": "git-catchup", "artifacts": ["git", "python"]},
    max_tokens=MECWMonitor().get_safe_budget()
)

Hub-and-Spoke Architecture

Hub Responsibilities

1. Context Detection: Identify user intent, artifacts, workflow type
2. Module Selection: Choose which modules to load based on context
3. Budget Management: Ensure MECW compliance before loading
4. Integration Coordination: Provide integration points with other skills
5. Exit Criteria: Define completion criteria across all paths

Spoke Characteristics

1. Single Responsibility: Each module serves one workflow or domain
2. Self-Contained: Modules don't depend on other modules
3. Context-Tagged: Clear indicators of when module applies
4. Token-Budgeted: Known token cost for selection decisions
5. Independently Testable: Can be evaluated in isolation

Selection Strategies

See modules/selection-strategies.md for detailed strategies:

• Intent-based: Load based on detected user goals
• Artifact-based: Load based on detected files/systems
• Budget-aware: Load within available token budget
• Progressive: Load core first, expand as needed
• Mutually-exclusive: Load one path from multiple options

Loading Patterns

See modules/loading-patterns.md for implementation patterns:

• Conditional includes: Dynamic module references
• Lazy loading: Load on first use
• Tiered disclosure: Core → common → edge cases
• Context switching: Change loaded modules mid-session
• Preemptive unloading: Remove unused modules under pressure

Common Use Cases

• Multi-Domain Skills: imbue:catchup loads git/docs/logs modules by context
• Context-Heavy Analysis: Load relevant modules only, defer deep-dives, unload completed
• Plugin Infrastructure: Mix-and-match infrastructure modules with version checks

Best Practices

1. Design Hub First: Define all possible contexts and module boundaries
2. Tag Modules Clearly: Use YAML frontmatter to indicate context triggers
3. Measure Token Cost: Know the cost of each module for selection
4. Monitor Loading: Track which modules are actually used
5. Validate Paths: Ensure all context paths have required modules
6. Document Triggers: Make context detection logic transparent

Module References

• Selection Strategies: See modules/selection-strategies.md for choosing modules
• Loading Patterns: See modules/loading-patterns.md for implementation techniques

Integration with Other Skills

This skill provides foundational patterns referenced by:

• abstract:modular-skills - Uses progressive loading for skill design
• conservation:context-optimization - Uses for MECW-compliant loading
• imbue:catchup - Uses for context-based module selection
• Plugin authors building multi-workflow skills

Reference in your skill's frontmatter:

dependencies: [leyline:progressive-loading, leyline:mecw-patterns]
progressive_loading: true

Exit Criteria

• Hub clearly defines all module loading contexts
• Each module is tagged with activation context
• Module selection respects MECW constraints
• Token costs measured for all modules
• Context detection logic documented
• Loading paths validated for completeness