/modular-skills

name: modular-skills
description: 'Build composable skill modules with hub-and-spoke loading. Use when token budget is tight.'
alwaysApply: false
category: workflow-optimization
tags:
- architecture
- modularity
- tokens
- skills
- design-patterns
- skill-design
- token-optimization
dependencies: []
tools: []
usage_patterns:
- skill-design
- architecture-review
- token-optimization
- refactoring-workflows
complexity: intermediate
model_hint: standard
estimated_tokens: 1200
modules:
- modules/antipatterns-and-migration.md
- modules/core-workflow.md
- modules/design-philosophy.md
- modules/enforcement-patterns.md
- modules/implementation-patterns.md
- modules/optimization-techniques.md
- modules/troubleshooting.md
- modules/design-patterns.md

When NOT To Use

• A single-file skill already inside its token budget (use

`abstract:skill-authoring`)

• The lazy-loading contract itself (use `leyline:progressive-loading`)

Table of Contents

• [Overview](#overview)
• [Quick Start](#quick-start)
• [Workflow and Tasks](#workflow-and-tasks)
• [Quality Checks](#quality-checks)
• [Resources](#resources)

Modular Skills Design

Overview

This framework breaks complex skills into focused modules to keep token usage predictable and avoid monolithic files. We use progressive disclosure: starting with essentials and loading deeper technical details via `@include` or `Load:` statements only when needed. This approach prevents hitting context limits during long-running tasks.

Modular design keeps file sizes within recommended limits, typically under 150 lines. Shallow dependencies and clear boundaries simplify testing and maintenance. The hub-and-spoke model allows the project to grow without bloating primary skill files, making focused modules easier to verify in isolation and faster to parse.

Core Components

Three tools support modular skill development:

• `skill-analyzer`: Checks complexity and suggests where to split code.
• `token-estimator`: Forecasts usage and suggests optimizations.
• `module_validator`: Verifies that structure complies with project standards.

Design Principles

We design skills around single responsibility and loose coupling. Each module focuses on one task, minimizing dependencies to keep the architecture cohesive. Clear boundaries and well-defined interfaces prevent changes in one module from breaking others. This follows Anthropic's Agent Skills best practices: provide a high-level overview first, then surface details as needed to maintain context efficiency.

Module Ownership (IMPORTANT)

**Deprecated**: `skills/shared/modules/` directories. This pattern caused orphaned references when shared modules were updated or removed.

**Current pattern**: Each skill owns its modules at `skills/<skill-name>/modules/`. When multiple skills need the same content, the primary owner holds the module and others reference it via relative path (e.g., `../skill-authoring/modules/description-writing.md`). The validator flags any remaining `skills/shared/` directories.

Quick Start

Skill Analysis

Analyze modularity using `scripts/skill_analyzer.py`. You can set a custom threshold for line counts to identify files that need splitting.

python scripts/skill_analyzer.py --file path/to/SKILL.md --threshold 100

From Python, use `analyze_skill` from `abstract.skill_tools`.

Token Usage Planning

Estimate token consumption to verify your skill stays within budget. Run this from the skill directory:

python scripts/tokens.py

Module Validation

Check for structure and pattern compliance before deployment.

python scripts/abstract_validator.py --scan

Workflow and Tasks

Start by assessing complexity with `skill_analyzer.py`. If a skill exceeds 150 lines, break it into focused modules following the patterns in `../../docs/examples/modular-skills/`. Use `token_estimator.py` to check efficiency and `abstract_validator.py` to verify the final structure. This iterative process maintains module maintainability and token efficiency.

Quality Checks

Identify modules needing attention by checking line counts and missing Table of Contents. Any module over 100 lines requires a TOC after the frontmatter to aid navigation.

# Find modules exceeding 100 lines
find modules -name "*.md" -exec wc -l {} + | awk '$1 > 100'

Standards Compliance

Our standards prioritize concrete examples and a consistent voice. Always provide actual commands in Quick Start sections instead of abstract descriptions. Use third-person perspective (e.g., "the project", "developers") rather than "you" or "your". Each code example should be followed by a validation command. For discoverability, descriptions must include at least five specific trigger phrases.

TOC Template

## Table of Contents

- [Section Name](#section-name)
- [Examples](#examples)
- [Troubleshooting](#troubleshooting)

Resources

Shared Modules: Cross-Skill Patterns

Standard patterns for triggers and for deciding whether a skill applies:

• **Trigger Patterns**: See [enforcement-patterns.md](modules/enforcement-patterns.md)
• **Skill Selection**: See [skill-selection-judgment.md](../../shared-modules/skill-selection-judgment.md)

Skill-Specific Modules

Detailed guides for implementation and maintenance:

• **Enforcement Patterns**: See `modules/enforcement-patterns.md`
• **Core Workflow**: See `modules/core-workflow.md`
• **Implementation Patterns**: See `modules/implementation-patterns.md`
• **Migration Guide**: See `modules/antipatterns-and-migration.md`
• **Design Philosophy**: See `modules/design-philosophy.md`
• **Troubleshooting**: See `modules/troubleshooting.md`
• **Optimization Techniques**: See `modules/optimization-techniques.md` - reducing large skill file sizes through externalization, consolidation, and progressive loading

Tools and Examples

• **Tools**: `skill_analyzer.py`, `token_estimator.py`, and `abstract_validator.py` in