Skill: athola/nm-abstract-modular-skills (version 1.8.5)

--- name: modular-skills description: Build composable skill modules with hub-and-spoke loading version: 1.9.5 triggers: - architecture - modularity - tokens - skills - design-patterns - skill-design - token-optimization - token budget is tight - complexity is high metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/abstract", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: abstract --- > **Night Market Skill** — ported from [claude-night-market/abstract](https://github.com/athola/claude-night-market/tree/master/plugins/abstract). For the full experience with agents, hooks, and commands, install the Claude Code plugin. ## 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/anti-rationalization.md`). The validator flags any remaining `skills/shared/` directories. ## Quick Start ### Skill Analysis Analyze modularity using `scripts/analyze.py`. You can set a custom threshold for line counts to identify files that need splitting. ```bash python scripts/analyze.py --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 Standard patterns for triggers, enforcement language, and anti-rationalization: - **Trigger Patterns**: See [trigger-patterns.md](modules/enforcement-patterns.md) - **Enforcement Language**: See [enforcement-language.md](../shared-patterns/modules/workflow-patterns.md) - **Anti-Rationalization**: See [anti-rationalization.md](../skill-authoring/modules/anti-rationalization.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.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)