skill-search-optimizer

# Skill Search Optimizer

Optimize skills for discoverability on the ClawdHub registry. Covers how search works, how to write descriptions that rank well, content strategies for semantic matching, testing visibility, and competitive positioning.

## When to Use

- A published skill isn't appearing in relevant searches
- Writing a skill description for maximum discoverability
- Understanding how ClawdHub's search indexes and ranks skills
- Comparing your skill's visibility against competitors
- Improving an existing skill's search performance

## How ClawdHub Search Works

### Architecture

ClawdHub uses **vector-based semantic search**, not keyword matching:

```
User query → OpenAI embedding → Vector similarity search → Ranked results
             (text-embedding-*)    (Convex vector index)
```

Key implications:
1. **Meaning matters more than exact keywords** — "container debugging" matches "Docker troubleshooting"
2. **But keywords still help** — the embedding model encodes specific terms with high signal
3. **Description is the primary indexed field** — content may contribute but description is dominant
4. **Short queries match broad descriptions** — "docker" matches skills about containers generally
5. **Specific queries match specific descriptions** — "debug crashed Docker container" favors skills that mention debugging and crashes

### What Gets Indexed

```
PRIMARY:   description field (frontmatter)
SECONDARY: name/slug field
TERTIARY:  skill content (body markdown) — likely summarized or truncated before embedding

The description field is your search ranking. Everything else is secondary.
```

### Search API

```bash
# How search is called internally
# POST https://clawdhub.com/api/cli/search
# Body: { "query": "user search terms", "limit": 10 }
# Returns: ranked list of skills with similarity scores

# CLI search
npx molthub@latest search "your query"
```

## Description Optimization

### The anatomy of a high-ranking description

```yaml
# Pattern:
# [Ac...