I picked the skill with more stars. Its repo had 46 skills tied for first place.

Description and use

Choosing a skill based on repository stars collapses the ranking, yielding a 46‑way tie. The fix is to evaluate skill content on four dimensions instead of a single overall score.

Problem

Choosing a skill based on repository stars collapses the ranking, making it impossible to distinguish individual skills within a popular repo.

Outcome

The original metadata‑based ranking collapsed, producing a 46‑way tie. A single overall LLM score produced a flat distribution (many 85s). Re‑engineering the prompt to score four independent dimensions yielded a more granular distribution, enabling better skill selection.

Workflow

• 1. Observe that repository stars are used as a proxy for skill quality.
• 2. Notice that 46 skills from the popular repo tie for first place in the ranking.
• 3. Realize that metadata signals are identical for skills within the same repo, causing the collapse.
• 4. Understand that content must be read to distinguish skills.
• 5. Implement a new scoring method that asks an LLM to evaluate four dimensions (specificity, actionability, completeness, distinctiveness) instead of a single overall score.
• 6. Sum the four dimension scores to obtain a final skill score.
• 7. Use the improved ranking to select skills more accurately.

Evidence

Forty-six skills from that repo, tied for first place.

Source

The system's honest answer to "which of these should I install?" was a list of 46 things, in no particular order.

Source

The obvious implementation: send the skill, ask for a 0–100 quality score, store it.

Source

Instead of one 0–100 score, the judge now scores four dimensions independently, 0–25 each, and the code sums them.

Source