home / skills / athola / claude-night-market / progressive-loading

progressive-loading skill

/plugins/leyline/skills/progressive-loading

This skill enables context-aware progressive module loading using hub-and-spoke patterns to optimize token budgets and loading efficiency.

npx playbooks add skill athola/claude-night-market --skill progressive-loading

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SKILL.md
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---
name: progressive-loading
description: 'Context-aware progressive module loading with hub-and-spoke pattern
  for token optimization.


  progressive loading, lazy loading, hub-spoke, module selection.'
category: infrastructure
tags:
- progressive-disclosure
- context-management
- modularity
- token-optimization
- lazy-loading
dependencies:
- leyline:mecw-patterns
tools:
- mecw-monitor
- context-tracker
- module-selector
provides:
  infrastructure:
  - progressive-loading
  - context-based-selection
  - load-on-demand
  patterns:
  - hub-and-spoke
  - conditional-loading
  - context-awareness
usage_patterns:
- skill-optimization
- context-aware-loading
- dynamic-module-selection
- token-budget-management
complexity: intermediate
estimated_tokens: 800
progressive_loading: true
modules:
- modules/selection-strategies.md
- modules/loading-patterns.md
---
## Table of Contents

- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Basic Hub Pattern](#basic-hub-pattern)
- [Progressive Loading](#progressive-loading)
- [Context-Based Selection](#context-based-selection)
- [Hub-and-Spoke Architecture](#hub-and-spoke-architecture)
- [Hub Responsibilities](#hub-responsibilities)
- [Spoke Characteristics](#spoke-characteristics)
- [Selection Strategies](#selection-strategies)
- [Loading Patterns](#loading-patterns)
- [Common Use Cases](#common-use-cases)
- [Best Practices](#best-practices)
- [Module References](#module-references)
- [Integration with Other Skills](#integration-with-other-skills)
- [Exit Criteria](#exit-criteria)


# 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

## When NOT To Use

- Project doesn't use the leyline infrastructure patterns
- Simple scripts without service architecture needs

## Quick Start

### Basic Hub Pattern

```markdown
## Progressive Loading

**Context A**: Load `modules/loading-patterns.md` for scenario A
**Context B**: Load `modules/selection-strategies.md` for scenario B

**Always Available**: Core utilities, exit criteria, integration points
```
**Verification:** Run the command with `--help` flag to verify availability.

### 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()
)
```
**Verification:** Run the command with `--help` flag to verify availability.

## 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
```
**Verification:** Run the command with `--help` flag to verify availability.

## 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
## Troubleshooting

### Common Issues

**Command not found**
Ensure all dependencies are installed and in PATH

**Permission errors**
Check file permissions and run with appropriate privileges

**Unexpected behavior**
Enable verbose logging with `--verbose` flag

Overview

This skill implements context-aware progressive module loading using a hub-and-spoke pattern to optimize token usage and runtime footprint. It starts with a minimal core and expands modules based on intent, artifacts, and token budget. The design reduces unnecessary module loading while ensuring required functionality becomes available on demand.

How this skill works

A central hub detects context (intent, files, workflow) and selects spokes (modules) tagged for specific scenarios. The hub enforces budget-aware decisions, lazily loading modules, tiering disclosures (core → common → edge), and preemptively unloading under pressure. Selection strategies include intent-based, artifact-based, mutually-exclusive choices, and progressive expansion.

When to use it

  • Building multi-domain skills covering distinct workflows
  • When managing limited context/window (token) budgets is critical
  • When modules are largely mutually exclusive and can be isolated
  • For long-running sessions requiring MECW-style budget compliance
  • When you need predictable, testable module activation behavior

Best practices

  • Design the hub first: enumerate contexts, triggers, and exit criteria
  • Tag modules with clear context metadata and measured token cost
  • Prefer single-responsibility spokes that are independently testable
  • Implement lazy loading and tiered disclosure to prioritize common paths
  • Monitor real usage and token consumption, and validate all context paths

Example use cases

  • A catchup assistant that loads git, docs, or logs modules only when detected by artifacts
  • A multi-tool plugin that starts with core utilities then expands to deep analysis modules on demand
  • A long-running session manager that unloads rarely used modules to stay within token budget
  • A plugin infrastructure orchestrator that composes third-party spokes while enforcing compatibility

FAQ

How does the hub know which modules to load?

The hub uses context detection rules (intent, artifacts, metadata tags) and selection strategies to choose spokes, also checking measured token costs before loading.

How are token budgets enforced?

The hub consults a MECW-style budget monitor and applies budget-aware strategies: prefer core and common modules, defer or skip high-cost spokes, and unload modules when needed.