skill trigger optimizer

Skill Trigger Optimizer solves the library-level failure modes that single-skill auditing tools miss. When you manage a catalog of dozens or hundreds of AI agent skills, individual description quality isn't enough to prevent routing errors. This tool identifies combinatorial problems where multiple skills compete for the same user prompt, or where specific request categories fall through the cracks because no description vocabulary captures them.

What it does

• Detect collisions by identifying overlapping phrases across multiple skill descriptions that cause router ambiguity.
• Identify coverage gaps where common user requests fail to trigger any installed skill despite relevant functionality existing.
• Surface invisible skills that never fire because their frontmatter lacks the vocabulary needed to match their intended use cases.
• Audit frontmatter by analyzing only the name and description fields that routers actually see during selection.
• Generate fix briefs that provide specific instructions for skill-creator or humans to resolve identified overlaps and gaps.

How it works

1. Enumerate library by collecting the name and description from every installed SKILL.md in the system.
2. Build prompt samples using realistic user phrasing sourced independently from the skill descriptions themselves.
3. Cross-match analysis by running the prompt sample against the entire library to find multiple matches or total misses.
4. Rank findings based on the real-world likelihood of the problematic phrasing being typed by a user.

Frameworks & tools

Works with any agent framework using a SKILL.md frontmatter architecture, specifically optimized for Claude Code, Cursor, and systems using progressive-disclosure routing models.

Why this beats prompting it yourself

Manual audits are prone to confirmation bias and fail to scale as the library grows. This skill uses a falsifiable, evidence-based methodology that quotes exact phrase overlaps and identifies "invisible" skills that a human auditor might assume are working correctly.

Use cases

• Auditing a marketplace catalog before a bulk installation to prevent routing conflicts.
• Sanity-checking a newly developed skill against a production library to ensure it doesn't steal traffic.
• Identifying why specific installed tools are never being invoked by the agent during live sessions.
• Generating structured feedback for skill developers to improve their trigger metadata.

Known limitations

Does not analyze skill bodies or logic, only frontmatter triggers. Findings are static text-level diagnoses, not empirical runtime confirmation of routing.