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feat: improve 5 lowest-scoring skill definitions#86

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rohan-tessl wants to merge 1 commit intodatabricks:mainfrom
rohan-tessl:improve/skill-review-optimization
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feat: improve 5 lowest-scoring skill definitions#86
rohan-tessl wants to merge 1 commit intodatabricks:mainfrom
rohan-tessl:improve/skill-review-optimization

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Hey @philip 👋

I ran your skills through tessl skill review at work and found targeted improvements in your skills. Here's the before/after:

Skill Before After Change
vercel-cli 68% 97% +29%
building-components 68% 93% +25%
databricks-core 76% 100% +24%
vercel-composition-patterns 74% 97% +23%
seo-audit 70% 86% +16%

These were easy changes to bring the skill's structure and activation in line with what performs well against Anthropic's best practices.

What changed in vercel-cli and vercel-composition-patterns

Added quoted descriptions with "Use when" clauses listing specific triggers. Added concrete workflows with validation checkpoints. For composition-patterns, added 3 before/after TSX code examples (boolean props to compound components, explicit variants, React 19 forwardRef removal). Consolidated rule lists into compact priority tables.

What changed in building-components

Quoted the description string. Added a 7-step workflow with explicit validation checkpoint. Added a concrete TSX quick example (Button component with data attributes, ARIA, forwardRef). Added a decision tree for routing to the correct reference file.

What changed in seo-audit

Cut from 451 lines to 93 lines (~80% reduction). Removed all explanations of concepts Claude already knows (E-E-A-T definitions, what title tags are, HTTPS basics). Kept only thresholds (LCP under 2.5s, title 50-60 chars), the schema detection limitation, and actionable checklists.

What changed in databricks-core

Rewrote the description with a leading "Use when" clause listing specific triggers (CLI commands, authentication, workspace configuration, profile management, Unity Catalog, DAB deployment).

In addition, I stress-tested your databricks-dabs skill against a few real-world scenarios, and it held up really well. This means that your skill meaningfully improves agent steering and contributes to stronger output quality. Kudos for that!

Honest disclosure, I work at @tesslio where we build tooling around skills like these. Not a pitch, just saw room for improvement and wanted to contribute.

If you want to self-improve your skills, or define your own scenarios to pressure test, just ask your agent (Claude Code, Codex, etc.) to evaluate and optimize your skill with Tessl. Ping me @rohan-tessl, if you hit any snags.

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