425 04 Packaging 37ce51c3

Packaging Guide

Skill Seekers v3.1.0
Export skills to AI platforms and vector databases

Overview

Packaging converts your skill directory into a platform-specific format:

output/my-skill/ ──▶ Packager ──▶ output/my-skill-{platform}.{format}
    ↓                                ↓
(SKILL.md +        Platform-specific  (ZIP, tar.gz,
 references)        formatting        directories,
                                     FAISS index)

Supported Platforms

Basic Packaging

Package for Claude (Default)

# Default packaging
skill-seekers package output/my-skill/

# Explicit target
skill-seekers package output/my-skill/ --target claude

# Output: output/my-skill-claude.zip

Package for Other Platforms

# Google Gemini
skill-seekers package output/my-skill/ --target gemini
# Output: output/my-skill-gemini.tar.gz

# OpenAI
skill-seekers package output/my-skill/ --target openai
# Output: output/my-skill-openai.zip

# LangChain
skill-seekers package output/my-skill/ --target langchain
# Output: output/my-skill-langchain/ directory

# ChromaDB
skill-seekers package output/my-skill/ --target chroma
# Output: output/my-skill-chroma.zip

Multi-Platform Packaging

Package for All Platforms

# Create skill once
skill-seekers create <source>

# Package for multiple platforms
for platform in claude gemini openai langchain; do
  echo "Packaging for $platform..."
  skill-seekers package output/my-skill/ --target $platform
done

# Results:
# output/my-skill-claude.zip
# output/my-skill-gemini.tar.gz
# output/my-skill-openai.zip
# output/my-skill-langchain/

Batch Packaging Script

#!/bin/bash
SKILL_DIR="output/my-skill"
PLATFORMS="claude gemini openai langchain llama-index chroma"

for platform in $PLATFORMS; do
  echo "▶️ Packaging for $platform..."
  skill-seekers package "$SKILL_DIR" --target "$platform"
  
  if [ $? -eq 0 ]; then
    echo "✅ $platform done"
  else
    echo "❌ $platform failed"
 fi
done

echo "🎉 All platforms packaged!"

Packaging Options

Skip Quality Check

# Skip validation (faster)
skill-seekers package output/my-skill/ --skip-quality-check

Don't Open Output Folder

# Prevent opening folder after packaging
skill-seekers package output/my-skill/ --no-open

Auto-Upload After Packaging

# Package and upload
export ANTHROPIC_API_KEY=sk-ant-...
skill-seekers package output/my-skill/ --target claude --upload

Streaming Mode

For very large skills, use streaming to reduce memory usage:

# Enable streaming
skill-seekers package output/large-skill/ --streaming

# Custom chunk size
skill-seekers package output/large-skill/ \
  --streaming \
  --streaming-chunk-chars 2000 \
  --streaming-overlap-chars 100

When to use:

• Skills > 500 pages
• Limited RAM (< 8GB)
• Batch processing many skills

RAG Chunking

Optimize for Retrieval-Augmented Generation:

# Enable semantic chunking
skill-seekers package output/my-skill/ \
  --target langchain \
  --chunk-for-rag \
  --chunk-tokens 512

# Custom chunk size
skill-seekers package output/my-skill/ \
  --target chroma \
  --chunk-tokens 256 \
  --chunk-overlap-tokens 50

Chunking Options:

Platform-Specific Details

Claude AI

skill-seekers package output/my-skill/ --target claude

Upload:

# Auto-upload
skill-seekers package output/my-skill/ --target claude --upload

# Manual upload
skill-seekers upload output/my-skill-claude.zip --target claude

Format:

• ZIP archive
• Contains SKILL.md + references/
• Includes YAML manifest

Google Gemini

skill-seekers package output/my-skill/ --target gemini

Upload:

export GOOGLE_API_KEY=AIza...
skill-seekers upload output/my-skill-gemini.tar.gz --target gemini

Format:

• tar.gz archive
• Optimized for Gemini's format

OpenAI ChatGPT

skill-seekers package output/my-skill/ --target openai

Upload:

export OPENAI_API_KEY=sk-...
skill-seekers upload output/my-skill-openai.zip --target openai

Format:

• ZIP with vector embeddings
• Ready for Assistants API

LangChain

skill-seekers package output/my-skill/ --target langchain

Usage:

from langchain.document_loaders import DirectoryLoader

loader = DirectoryLoader("output/my-skill-langchain/")
docs = loader.load()

# Use in RAG pipeline

Format:

• Directory of Document objects
• JSON metadata

ChromaDB

skill-seekers package output/my-skill/ --target chroma

Upload:

# Local ChromaDB
skill-seekers upload output/my-skill-chroma.zip --target chroma

# With custom URL
skill-seekers upload output/my-skill-chroma.zip \
  --target chroma \
  --chroma-url http://localhost:8000

Usage:

import chromadb

client = chromadb.HttpClient(host="localhost", port=8000)
collection = client.get_collection("my-skill")

Weaviate

skill-seekers package output/my-skill/ --target weaviate

Upload:

# Local Weaviate
skill-seekers upload output/my-skill-weaviate.zip --target weaviate

# Weaviate Cloud
skill-seekers upload output/my-skill-weaviate.zip \
  --target weaviate \
  --use-cloud \
  --cluster-url https://xxx.weaviate.network

Cursor IDE

# Package (actually creates .cursorrules file)
skill-seekers package output/my-skill/ --target cursor

# Or install directly
skill-seekers install-agent output/my-skill/ --agent cursor

Result: .cursorrules file in your project root.

Windsurf IDE

skill-seekers install-agent output/my-skill/ --agent windsurf

Result: .windsurfrules file in your project root.

Quality Check

Before packaging, skills are validated:

# Check quality
skill-seekers quality output/my-skill/

# Detailed report
skill-seekers quality output/my-skill/ --report

# Set minimum threshold
skill-seekers quality output/my-skill/ --threshold 7.0

Quality Metrics:

• SKILL.md completeness
• Code example coverage
• Navigation structure
• Reference file organization

Output Structure

After Packaging

output/
├── my-skill/                    # Source skill
│   ├── SKILL.md
│   └── references/
│
├── my-skill-claude.zip          # Claude package
├── my-skill-gemini.tar.gz       # Gemini package
├── my-skill-openai.zip          # OpenAI package
├── my-skill-langchain/          # LangChain directory
├── my-skill-chroma.zip          # ChromaDB package
└── my-skill-weaviate.zip        # Weaviate package

Troubleshooting

"Package validation failed"

Problem: SKILL.md is missing or malformed

Solution:

# Check skill structure
ls output/my-skill/

# Rebuild if needed
skill-seekers create --config my-config --skip-scrape

# Or recreate
skill-seekers create <source>

"Target platform not supported"

Problem: Typo in target name

Solution:

# Check available targets
skill-seekers package --help

# Common targets: claude, gemini, openai, langchain, chroma, weaviate

"Upload failed"

Problem: Missing API key

Solution:

# Set API key
export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=AIza...
export OPENAI_API_KEY=sk-...

# Try again
skill-seekers upload output/my-skill-claude.zip --target claude

"Out of memory"

Problem: Skill too large for memory

Solution:

# Use streaming mode
skill-seekers package output/my-skill/ --streaming

# Smaller chunks
skill-seekers package output/my-skill/ --streaming --streaming-chunk-chars 1000

Best Practices

1. Package Once, Use Everywhere

# Create once
skill-seekers create <source>

# Package for all needed platforms
for platform in claude gemini langchain; do
  skill-seekers package output/my-skill/ --target $platform
done

2. Check Quality Before Packaging

# Validate first
skill-seekers quality output/my-skill/ --threshold 6.0

# Then package
skill-seekers package output/my-skill/

3. Use Streaming for Large Skills

# Automatically detected, but can force
skill-seekers package output/large-skill/ --streaming

4. Keep Original Skill Directory

Don't delete output/my-skill/ after packaging - you might want to:

• Re-package for other platforms
• Apply different workflows
• Update and re-enhance

Next Steps

• Workflows Guide - Apply workflows before packaging
• MCP Reference - Package via MCP
• Vector DB Integrations - Platform-specific guides