account-research
Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect…
\"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
Claude Code
$ npx -y skills add charlieviettq/awesome-agent-skill --skill ecom-conversational --agent claude-code
Codex
$ npx skills add charlieviettq/awesome-agent-skill --skill ecom-conversational -a codex -y
Cursor
$ npx skills add charlieviettq/awesome-agent-skill --skill ecom-conversational -a cursor -y
Gemini CLI
$ npx skills add charlieviettq/awesome-agent-skill --skill ecom-conversational -a gemini-cli -y
GitHub Copilot
$ npx skills add charlieviettq/awesome-agent-skill --skill ecom-conversational -a github-copilot -y
OpenCode
$ npx skills add charlieviettq/awesome-agent-skill --skill ecom-conversational -a opencode -y
Windsurf
$ npx skills add charlieviettq/awesome-agent-skill --skill ecom-conversational -a windsurf -y
name: "\"ecom-conversational\"" description: "\"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'.\"." allowed-tools: Read, Glob, Grep
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 |Curated skill pack for LLM agents in engineer and science workflow (Cursor & Claude ready).
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