git:20260803.e1cdc1b to git:20260823.27a83c1

---

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.

## 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.

```bash

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:

```bash

python scripts/tokens.py

```

### Module Validation

Check for structure and pattern compliance before deployment.

```bash

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.

```bash

# 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

```markdown

## Table of Contents

- [Section Name](#section-name)

- [Examples](#examples)

- [Troubleshooting](#troubleshooting)

```

## Resources

### Shared Modules: Cross-Skill Patterns

- **Enforcement Language**: See [enforcement-language.md](../shared-patterns/modules/workflow-patterns.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 `../../scripts/`.

- **Examples**: See `../../docs/examples/modular-skills/` for reference implementations.

## Exit Criteria

- [ ] Every module file produced is at or under 150 lines; any module exceeding 100 lines has a

Table of Contents immediately after its frontmatter.

- [ ] No `skills/shared/modules/` directory exists; all modules live under

`skills/<skill-name>/modules/`.

- [ ] `python scripts/abstract_validator.py --scan` exits 0 with no structural warnings on the

affected skill directory.

- [ ] `python scripts/tokens.py` reports total estimated tokens within the declared

`estimated_tokens` budget for the hub SKILL.md.