Architect skills as modular blocks to control token usage and complexity. Use when creating skills >150 lines, breaking down monolithic skills, or planning new architecture. Do not use for evaluating existing skills (use skills-eval) or writing human-facing prose (use writing-clearly-and-concisely). Check this skill before starting any new skill development.
Architects complex skills as modular components to manage token usage and enforce project standards.
/plugin marketplace add athola/claude-night-market/plugin install abstract@claude-night-marketThis skill inherits all available tools. When active, it can use any tool Claude has access to.
README.mdguide.mdmodules/antipatterns-and-migration.mdmodules/core-workflow.mdmodules/design-philosophy.mdmodules/enforcement-patterns.mdmodules/implementation-patterns.mdmodules/optimization-techniques.mdmodules/troubleshooting.mdscripts/analyze.pyscripts/module_validatorscripts/tokens.pyThis 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.
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.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.
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/anti-rationalization.md). The validator flags any remaining skills/shared/ directories.
Analyze modularity using scripts/analyze.py. You can set a custom threshold for line counts to identify files that need splitting.
python scripts/analyze.py --threshold 100
From Python, use analyze_skill from abstract.skill_tools.
Estimate token consumption to verify your skill stays within budget. Run this from the skill directory:
python scripts/tokens.py
Check for structure and pattern compliance before deployment.
python scripts/abstract_validator.py --scan
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.
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'
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.
## Table of Contents
- [Section Name](#section-name)
- [Examples](#examples)
- [Troubleshooting](#troubleshooting)
Standard patterns for triggers, enforcement language, and anti-rationalization:
Detailed guides for implementation and maintenance:
modules/enforcement-patterns.mdmodules/core-workflow.mdmodules/implementation-patterns.mdmodules/antipatterns-and-migration.mdmodules/design-philosophy.mdmodules/troubleshooting.mdmodules/optimization-techniques.md - reducing large skill file sizes through externalization, consolidation, and progressive loadingskill_analyzer.py, token_estimator.py, and abstract_validator.py in ../../scripts/.../../docs/examples/modular-skills/ for reference implementations.Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.
Search, retrieve, and install Agent Skills from the prompts.chat registry using MCP tools. Use when the user asks to find skills, browse skill catalogs, install a skill for Claude, or extend Claude's capabilities with reusable AI agent components.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.