# Progressive Loading

> Context-aware progressive module loading with hub-and-spoke pattern for token optimization. Triggers: progressive loading, lazy loading, hub-spoke, module selection Use when: optimizing skill loading or reducing upfront context usage

- Skill: `majiayu000/progressive-loading-3` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/progressive-loading-3`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/progressive-loading-3/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/majiayu000/progressive-loading-3

---


# 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

```markdown
## 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

```python
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**: Verify 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**: Verify 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
- `conserve: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:
```yaml
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