Design conversational commerce experiences across messaging platforms including chatbot flows, product cards, and conversion strategies. Use this skill when the user needs to sell through LINE, WhatsApp, Instagram DM, or other messaging channels, design chatbot purchasing flows, or integrate messaging into their sales funnel — even if they say 'sell through LINE', 'build a shopping chatbot', 'customers ask to buy in our DMs', or 'conversational sales strategy'.
npx skills add https://github.com/asgard-ai-platform/skills --skill ecom-conversational
IRON LAW: Conversation First, Commerce Second
Conversational commerce works because it meets customers WHERE THEY
ALREADY ARE (messaging apps). Forcing a sales pitch into a chat channel
kills trust. The conversation must provide genuine value (answering questions,
solving problems) BEFORE introducing products or purchases.
The sequence: Help → Trust → Recommend → Convert
| Platform | Users (Taiwan) | Commerce Features | Best For |
|----------|---------------|------------------|----------|
| LINE | 21M+ (95% penetration) | LINE Shopping, Rich Menu, LIFF, payment | Taiwan, Japan, Thailand primary channel |
| Instagram DM | ~10M | Shop tags, quick replies, product stickers | Visual products, younger demographic |
| Facebook Messenger | ~18M | Shops integration, automated responses | Broad reach, older demographic |
| WhatsApp | Limited in Taiwan | Catalog, cart, payment (select markets) | SEA, India, Brazil |
| WeChat | ~1M (Taiwan) | Mini Programs, WeChat Pay | China-connected businesses |
1. Entry Points — How customers start the conversation
2. Welcome Flow — First 3 messages
3. Product Discovery — Help them find what they need
4. Purchase Flow — Minimize friction
5. Post-Purchase — Retain and upsell
| Scenario | Handle with Bot | Hand off to Human |
|----------|----------------|------------------|
| FAQ (hours, shipping, returns) | ✓ | — |
| Product recommendations (simple) | ✓ | — |
| Complex product questions | — | ✓ |
| Complaints / issues | — | ✓ (immediately) |
| High-value purchases | Bot assists → human closes | ✓ |
Key metric: Bot containment rate (% resolved without human) — target 60-70% for mature bots.
# Conversational Commerce Plan: {Business}
## Channel Selection
- Primary: {platform} — rationale: {why}
- Entry points: {QR / ad / social / website}
## Conversation Flow
1. Welcome: {message template}
2. Discovery: {question flow}
3. Product Card: {template}
4. Checkout: {in-chat / redirect}
5. Post-purchase: {follow-up sequence}
## Bot vs Human Split
| Scenario | Handler | SLA |
|----------|---------|-----|
| {scenario} | Bot/Human | {response time} |
## KPIs
| Metric | Target |
|--------|--------|
| Response time | < {X} seconds (bot) / < {X} minutes (human) |
| Containment rate | > 60% |
| Conversation-to-purchase rate | > {X%} |
| Customer satisfaction (CSAT) | > 4.0/5 |
references/line-oa-setup.mdreferences/chatbot-design.mdAnalyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.
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Take asgard-ai-platform/ecom-conversational from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.