name: embed-project description: "Embed project-local skills, agents, and commands for semantic search. Use after creating items manually or to update embeddings." premium: true required_tier: pro

Embed Project Items

Compute vector embeddings for project-local items to enable semantic discovery and routing.

Premium Feature

This is a Pro tier feature. Free tier users can use basic file search instead.

Free Tier Fallback: Basic Search

When a free tier user invokes this skill, provide basic search capabilities:

## Basic Search (Free Tier)

Project embeddings require PopKit Pro. However, you can still search your project!

### Available Search Methods

**Find skills:**
```bash
ls .claude/skills/*/SKILL.md 2>/dev/null

Find agents:

ls .claude/agents/*/AGENT.md 2>/dev/null

Find commands:

ls .claude/commands/*.md 2>/dev/null

Search by keyword:

grep -r "keyword" .claude/

What Embeddings Would Provide

With PopKit Pro, you'd get semantic embeddings including:

✨ Semantic search - Find by meaning, not just keywords
🔍 Smart routing - Auto-select best agent for task
⚡ Cross-project discovery - Find patterns across codebases
📊 Relevance ranking - Results sorted by semantic similarity

Run /popkit:upgrade to unlock project embeddings.


### Fallback Implementation

```python
import sys
sys.path.insert(0, "hooks/utils")
from premium_checker import check_entitlement

result = check_entitlement("pop-embed-project")
if not result.allowed:
    # Provide basic search alternatives
    print("## Basic Search (Free Tier)")
    print("\nProject embeddings require PopKit Pro.")
    print("Use basic file search: `grep -r 'keyword' .claude/`")
    print("\nRun `/popkit:upgrade` to unlock project embeddings.")
    return

When to Use

After creating skills/agents/commands manually (not via generators)
To check embedding status for the project
To force re-embedding after content changes
Before using semantic search features

Process

Step 1: Parse Arguments

Check for flags in the user's command:

--status: Show status only, don't embed
--force or -f: Re-embed all items even if unchanged
--type <type>: Filter to specific type (skill, agent, command)

Step 2: Execute Based on Mode

If --status flag:

# Use the embedding_project module
import sys
sys.path.insert(0, "hooks/utils")
from embedding_project import get_project_embedding_status

status = get_project_embedding_status()

# Display results
print(f"Project: {status['project_path']}")
print(f"API Available: {status['api_available']}")
print()
print(f"Items Found:    {status['items_found']}")
print(f"Items Embedded: {status['items_embedded']}")
print(f"Items Stale:    {status['items_stale']}")
print(f"Items Missing:  {status['items_missing']}")

if status['by_type']:
    print("\nBy Type:")
    for stype, counts in status['by_type'].items():
        print(f"  {stype}: {counts['embedded']}/{counts['found']}")

If embedding (default):

import sys
sys.path.insert(0, "hooks/utils")
from embedding_project import embed_project_items, scan_project_items

# Map --type flag to source types
type_map = {
    "skill": ["project-skill", "generated-skill"],
    "agent": ["project-agent", "generated-agent"],
    "command": ["project-command"],
}

source_types = None
if args.type:
    source_types = type_map.get(args.type, [f"project-{args.type}"])

# First scan to show what we found
items = scan_project_items()
print(f"Found {len(items)} items")

# Embed items
result = embed_project_items(
    force=args.force,
    source_types=source_types,
    verbose=True
)

# Report results
if result["status"] == "success":
    print(f"\nEmbedding complete!")
    print(f"  Embedded: {result['embedded']}")
    print(f"  Skipped: {result['skipped']}")
    if result['errors']:
        print(f"  Errors: {result['errors']}")
elif result["status"] == "no_items":
    print("No embeddable items found in project.")
elif result["status"] == "error":
    print(f"Error: {result.get('error', 'Unknown error')}")

Step 3: Handle Rate Limiting

The embedding module automatically handles rate limiting:

Batches up to 50 items per API call
Waits 21 seconds between batches (Voyage 3 RPM limit)
Displays progress during wait

Project Locations Scanned

Location	Source Type
`.claude/skills/*/SKILL.md`	project-skill
`.claude/agents/*/AGENT.md`	project-agent
`.claude/commands/*.md`	project-command
`.generated/skills/*/SKILL.md`	generated-skill
`.generated/agents/*/AGENT.md`	generated-agent

Requirements

VOYAGE_API_KEY environment variable set
Items must have description in YAML frontmatter

Example Usage

# Check current status
/popkit:project embed --status

# Embed all items (skips unchanged)
/popkit:project embed

# Force re-embed everything
/popkit:project embed --force

# Embed only skills
/popkit:project embed --type skill

Output Format

Embedding Progress

Scanning project: /path/to/project
Found 8 items

Embedding 5 new/changed items...
Waiting 21s for rate limit...

Embedding complete!
  Embedded: 5
  Skipped: 3 (unchanged)
  Errors: 0

Status Report

Project: /path/to/project
API Available: Yes

Items Found:    8
Items Embedded: 8
Items Stale:    0
Items Missing:  0

By Type:
  project-skill: 3/3
  project-agent: 2/2
  project-command: 3/3

Integration

This skill integrates with:

hooks/utils/embedding_project.py - Core embedding logic
hooks/utils/embedding_store.py - Database storage
hooks/utils/voyage_client.py - Voyage API client
hooks/utils/semantic_router.py - Routing using embeddings

/popkit:project skills generate - Creates skills then auto-embeds
/popkit:project mcp - Creates MCP server with semantic search

embed-project