Agentic skill catalog scaling

Research indicates that the efficacy of skill selection in coding agent catalogs degrades sharply when the number of modules exceeds a threshold of approximately 15–20 Verified Answer #1. This decline is primarily caused by "semantic collision" and "context window crowding" Verified Answer #1. As catalogs scale to include dozens or hundreds of skills, the discriminative quality of metadata becomes the primary driver for correct selection, surpassing the model's inherent reasoning capability Verified Answer #1.

Critical Description Properties for Scaling

To maintain selection accuracy in large catalogs, research identifies three essential properties for skill descriptions Verified Answer #1.

Trigger-Oriented Logic

Generic functional summaries are often insufficient as semantic overlap increases between skills Verified Answer #1. High-performing descriptions prioritize conditional triggers that explicitly state the user intent, operational state, or specific context required for the skill to be appropriate Verified Answer #1.

Explicit Negative Constraints

The most common error in dense catalogs is the selection of the wrong tool Verified Answer #1. This issue is mitigated by including negative triggers that define boundaries, such as specifying when a skill should not be used Verified Answer #1. These constraints create hard semantic barriers that prevent the agent from defaulting to broader, less optimal tools Verified Answer #1.

Domain-Specific Vocabulary and Schema-Awareness

Effective scaling utilizes distinct, domain-specific naming conventions and incorporates detailed input/output schemas Verified Answer #1. This schema-aware documentation provides a secondary filter that allows models to reject incompatible tools before execution, significantly reducing hallucination rates Verified Answer #1.