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Build composable skill modules with hub-and-spoke loading.
Passed 19 of 22 test cases with the skill, and 5 of 22 without it.
and 17 more in the full report
15 fixed5 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which function should be imported from `abstract.skill_tools` to analyze skill modularity programmatically in Python?
| Checked | Without skill | With skill |
|---|---|---|
| analyze_skill | Missed | Passed |
The instructions your model reads when the skill loads.
abstract:skill-authoring)leyline:progressive-loading)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.
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/description-writing.md). The validator flags any remaining skills/shared/ directories.
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.
test cases passed, vs 5 / 22 without
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