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FeaturesSidekickUpdated March 11, 2026

Semantic Skill Search

Semantic Skill Search

Sidekick's skill search understands natural language. Instead of requiring exact keyword matches, it uses AI embeddings to match what you're trying to do against what each skill is capable of — so you can find the right skill by describing your intent in plain English.

Overview

When you type a query into the skill search bar, Sidekick:

  1. Converts your query into a vector embedding using a language model
  2. Compares that embedding against pre-indexed embeddings for every skill's name, description, and capability metadata
  3. Returns results ranked by semantic similarity

This means you don't need to remember exact skill names or use the same words a skill author used. You just describe what you want to accomplish.

Where It Works

Semantic search is available in two places:

  • Installed Skills — search across skills you've already connected to your Sidekick account
  • Skill Marketplace — discover new skills from the marketplace by describing your use case

Example Queries

What you typeWhat gets found
Help me schedule meetingsCalendar & Scheduling skills
Keep an eye on my pull requestsGitHub / GitLab monitoring skills
Summarise my emails in the morningEmail triage and digest skills
Turn off the lights when I leaveSmart home automation skills

ClawHub Compatibility

Semantic search works across all skills, including the 13,000+ community skills available via ClawHub. Skills imported from ClawHub are indexed the same way as native Sidekick skills — their SKILL.md capability descriptions are embedded and made searchable immediately on import.

Tips for Better Results

  • Describe the outcome, not the tool. Remind me about important emails works better than email plugin.
  • Use natural phrases. Full sentences like Help me manage my calendar are understood as well as short keywords.
  • Combine concepts. Queries like automatically reply to Slack messages when I'm busy can surface multi-capability skills.

Fallback Behaviour

If semantic matching returns low-confidence results, Sidekick falls back to keyword-based filtering as a secondary signal to ensure precision. Both signals are combined in the final ranking.