Embed Content

Overview

Manage embeddings for project-local content including skills, agents, commands, and MCP tools. Enables semantic search and discovery of project-specific items.

Core principle: Make project content discoverable via semantic search.

Trigger:

/popkit:project embed

Arguments

--status

--force

--export

--type <type>

skill

agent

command

tool

Process

Step 1: Discover Content

Scan project for embeddable items:

import sys
from pathlib import Path

# No longer needed - install popkit-shared instead
from embedding_project import scan_project_items

# Scan locations:
# - .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
# - MCP tool definitions            → mcp-tool

items = scan_project_items(Path.cwd())
print(f"Found {len(items)} embeddable items")

Step 2: Check Status

Compare against embedding store:

from embedding_project import get_project_embedding_status

status = get_project_embedding_status()

# Returns:
# - total_items: Number of items found
# - embedded_items: Number already embedded
# - stale_items: Number with changed content
# - missing_items: Number not yet embedded
# - by_type: Breakdown by source type

Step 3: Compute Embeddings

Via Voyage AI with rate limiting:

from embedding_project import embed_project_items

# Respects rate limits:
# - 3 requests per minute (21s delay between batches)
# - Up to 50 items per batch
# - Progress reporting

result = embed_project_items(
    project_root=Path.cwd(),
    force=False  # Set True to re-embed unchanged items
)

print(f"Embedded: {result['embedded']}")
print(f"Skipped: {result['skipped']}")
print(f"Errors: {result['errors']}")

Step 4: Export for MCP

Optionally export embeddings for MCP server:

import json
from datetime import datetime
from pathlib import Path

def export_tool_embeddings(tools: list, output_path: str):
    """Export embeddings for MCP semantic search."""
    output = {
        "generated_at": datetime.now().isoformat(),
        "model": "voyage-3.5",
        "dimension": len(tools[0]["embedding"]) if tools else 0,
        "tools": tools
    }

    Path(output_path).parent.mkdir(parents=True, exist_ok=True)
    Path(output_path).write_text(json.dumps(output, indent=2))

Output Formats

Status Mode (

--status

)

Project Embedding Status
═══════════════════════

Location: /path/to/project
API Available: Yes (VOYAGE_API_KEY set)

Content Found:
  Skills:   4 (2 embedded, 2 new)
  Agents:   1 (1 embedded)
  Commands: 3 (3 embedded)
  Tools:    8 (8 embedded)
  ─────────────────────────
  Total:   16 items

Needs Embedding: 2 items
  - .claude/skills/new-skill/SKILL.md
  - .claude/skills/updated-skill/SKILL.md (content changed)

Run `/popkit:project embed` to update.

Embed Mode (default)

Embedding Project Content
═════════════════════════

Discovering content...
  Found 16 items (14 current, 2 need update)

Embedding 2 items...
  ████████████████████ 100%

  ✓ project-skill:new-skill
  ✓ project-skill:updated-skill

Summary:
  Embedded:  2
  Skipped:  14 (already current)
  Errors:    0

Embeddings stored in global database.

Export Mode (

--export

)

Exporting Tool Embeddings
═════════════════════════

Collecting MCP tool embeddings...
  Found 8 tools with embeddings

Exporting to .claude/tool_embeddings.json...
✓ Exported 8 tool embeddings

File: .claude/tool_embeddings.json
Size: 245 KB
Model: voyage-3.5
Dimension: 1024

Rate Limiting

The skill respects Voyage AI rate limits:

Typical Times:

• 10 items: ~5 seconds (1 batch)
• 30 items: ~25 seconds (1 batch + delay)
• 100 items: ~1 minute (2 batches + delays)

Error Handling

Integration

Uses:

• hooks/utils/embedding_project.py

  - Core embedding logic

• hooks/utils/voyage_client.py

  - Voyage API client

• hooks/utils/embedding_store.py

  - SQLite storage

Called by:

• /popkit:project embed

  - Manual invocation

• /popkit:project generate

  - Final pipeline step

• pop-skill-generator

  - After creating skills

• pop-mcp-generator

  - After creating MCP server

Enables:

• Semantic search for project content
• Natural language tool discovery
• Cross-project skill discovery
• Priority boost for project items in routing

Examples

# Check what needs embedding
/popkit:project embed --status

# Embed all new/changed items
/popkit:project embed

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

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

# Export for MCP server
/popkit:project embed --export

# Combine: embed and export
/popkit:project embed --export