03/03更新： 刚拿到credit！两张Platinum卡都报销了，分享一下自动化经验。
成功的客服：Austin - 超级配合！问了几个clarification的问题（确认是从ouraring.com买的，确认只买了ring没买subscription），然后就说可以manually issue credit。我礼貌地问能不能两张卡都处理，他直接就同意了。
购买信息：
- 日期：2月15号
- 交易：PAYPAL *OURARING
- 卡：两张Platinum
自动化流程：
我用Claude + Playwright MCP自动化了整个chat过程。先去论坛扒了Raymond和Rio的名字（都是好评客服），然后让AI自动打开Amex网站，我手动登录后，AI就接管了整个chat。
具体步骤：
- 用USCardForum MCP先看了这个帖子 Oura Ring 报销问题 ，找到helpful agent的名字
- Playwright打开Amex官网，我手动登录
- AI自动导航到Customer Service → Chat
- AI跟客服聊，提供所有信息（日期、PayPal交易、卡号）
- 客服确认可以处理后，AI礼貌地问了能不能两张卡一起
- 搞定！
关键点：
- 提前准备好所有信息（日期、交易描述、卡号）
- 说话要客气，不要aggressive
- 提到PayPal交易（成功率好像更高）
- 如果碰到不配合的客服就换一个
- 记得发"Thanks"保持连接，防止disconnect
- 如果有多张卡，等客服确认能帮忙后再问第二张
工具要求：
- Claude Sonnet 4.5（需要智能判断客服反应）
- Playwright MCP（浏览器自动化）
- USCardForum MCP（可选，用来研究成功经验） https://www.uscardforum.com/t/topic/467628
把整个workflow做成了一个Cursor skill，以后报销其他benefits也可以用类似方法。有兴趣的可以参考我的自动化思路。
Austin加入好评客服名单，和Raymond、Rio一起。多谢各位分享的经验！
SKILL.md
---
name: amex-oura-credit
description: Automate American Express customer service chat to request Oura Ring credits using Playwright browser automation. Use when requesting Amex Oura Ring credit, automating customer service chats, or when user mentions Platinum card wellness benefits.
---
# American Express Oura Ring Credit Automation
This skill automates the process of chatting with American Express customer service to request Oura Ring credits for Platinum cards.
## Requirements
### Model Requirements
- **Recommended Model**: Claude Sonnet 4.5 or higher
- **Reason**: Requires strong reasoning for dynamic chat interactions, handling unpredictable agent responses, and making strategic decisions (e.g., switching agents, asking for multiple cards)
### MCP Requirements
- **Playwright MCP** (`@playwright/mcp`): For browser automation
- **USCardForum MCP** (`uscardforum`): For researching successful strategies and posting results
## Workflow Overview
### Phase 1: Research (Optional but Recommended)
Gather intelligence from the USCardForum thread about Oura Ring credits:
```bash
# Fetch recent posts to identify helpful agents
uscardforum.nitan-get_topic_posts(
topic_id=440293,
post_number=<recent>,
include_raw=true
)
Key information to extract:
- Names of helpful agents (e.g., Raymond, Rio, Austin)
- Common rejection reasons
- Successful strategies (e.g., mentioning PayPal transactions)
Phase 2: Browser Setup
# Install browser
browser_install(browser="chrome")
# Navigate to American Express
browser_navigate(url="https://www.americanexpress.com")
# Wait for user login (30-60 seconds)
browser_wait_for(time=60)
Important: User must log in manually. Do not attempt to automate login.
Phase 3: Navigate to Customer Service
- Take initial screenshot to confirm login
- Use
browser_snapshotto identify navigation elements - Click “Customer Service” link
- Click “Chat With Us” or “Chat” button
- Wait for chat interface to load
Navigation pattern:
Home → Customer Service → Chat With Us → Chat Interface
Phase 4: Chat Interaction
Initial Message Template
Hi! I'd like to request credit for my Oura Ring purchase.
Purchase details:
- Date: [MM/DD/YYYY]
- Transaction: PAYPAL *OURARING
- Card: Platinum Card ending -[XXXXX]
This is part of the Platinum wellness benefit. Could you help me manually issue the credit?
Key Strategies
- Be Polite and Clear: Provide all details upfront
- Mention PayPal: PayPal transactions have higher success rate
- Answer Clarifications Promptly: Common questions:
- “Was this purchased from ouraring.com?” → Yes
- “Was this just the ring or a subscription?” → Just the ring
- Request Multiple Cards: If agent is cooperative, politely ask for both cards:
"I actually have two Platinum cards. Could you issue credit for both cards ending -[XXXXX] and -[YYYYY]?" - Switch Agents if Needed: If agent says they can’t help, politely end chat and reconnect
- Keep Chat Alive: Send “Thank you!” every 2-3 minutes to prevent disconnection
Handling Responses
Positive signals:
- “I can manually issue the credit”
- “Let me process that for you”
- “I’ll submit that right away”
Negative signals:
- “This doesn’t qualify”
- “I cannot manually issue credits”
- “You need to contact a different department”
Action on negative signals: Politely thank agent and switch to a new one.
Phase 5: Document Results
After successful credit:
- Note the agent’s name
- Record the outcome (success/failure)
- Post to USCardForum (in Chinese, matching forum style):
# Example post template (in Chinese)
uscardforum.nitan-create_post(
topic_id=440293,
raw="""
刚拿到credit！分享一下经验。
**客服**: [Agent Name] - 非常配合，问了几个问题后直接就给了。
**购买信息**:
- 日期: [MM/DD]
- 交易: PAYPAL *OURARING
- 卡: Platinum -[XXXXX] 和 -[YYYYY]
**聊天过程**:
[Brief description of interaction]
总结: 说话客气点，把信息说清楚（日期、PayPal、卡号），别怕礼貌地要求两张卡一起报销。
"""
)
Key Success Factors
- Timing: Try during business hours (9 AM - 5 PM ET) for better agent availability
- Information Ready: Have all transaction details prepared
- Patience: May need to try 2-3 agents
- Politeness: Always thank agents, even if unsuccessful
- PayPal Advantage: PayPal transactions seem easier to get credited
Known Helpful Agents
As of March 2026:
- Raymond: Very helpful, immediately issues credits
- Rio: Cooperative, processes requests smoothly
- Austin: Fantastic, willing to issue credits for multiple cards
Troubleshooting
Chat Disconnects
- Send “Thank you” or “Thanks!” every 2-3 minutes
- If disconnected, click “Reconnect” or start new chat
Agent Says No
- Politely thank them and end chat
- Start new chat with different agent
- Try different time of day
Transaction Not Found
- Verify transaction has posted (not pending)
- Provide exact transaction description from statement
- Mention it’s a PayPal transaction if applicable
Multiple Cards
- Start with one card
- Once agent confirms they can help, ask about second card
- Don’t mention multiple cards upfront if agent seems hesitant
Automation Tips
Browser Automation Best Practices:
- Always
browser_snapshotbefore interactions - Use
browser_wait_for(3-5 seconds) after actions - Take screenshots at key moments for debugging
- Store agent responses in markdown files for analysis
Chat Flow Management:
- Monitor for system messages (agent joined/left)
- Wait 10-15 seconds after sending message for agent response
- Use ref IDs from snapshots for reliable element clicking
- Keep transcript of conversation for reporting
Example Complete Flow
# 1. Research
forum_posts = get_topic_posts(440293, 320, include_raw=True)
# 2. Setup browser
browser_install("chrome")
browser_navigate("https://www.americanexpress.com")
browser_wait_for(60) # User login
# 3. Navigate to chat
snapshot = browser_snapshot()
browser_click(element="Customer Service", ref="e32")
browser_wait_for(3)
browser_click(element="Chat", ref="e87")
browser_wait_for(5)
# 4. Start conversation
browser_type(
element="textbox",
ref="e932",
text="Hi! I'd like to request credit...",
submit=True
)
# 5. Handle interaction loop
while not credit_issued:
browser_wait_for(15)
snapshot = browser_snapshot()
# Analyze agent response
# Send appropriate reply
# Check for success/failure signals
# 6. Document success
create_post(topic_id=440293, raw="...")
Post-Automation: Forum Posting
Write in casual Chinese matching forum style:
- Use “多谢” for thanks
- Use “亲” when asking questions
- Keep it conversational and helpful
- Include specific agent names
- Mention key details that helped success
Success Rate
Based on forum data:
- With research: ~80% success rate
- Helpful agents: Near 100% success
- Random agents: ~50% success
- Multiple attempts: Eventually successful for most users
Notes
- This automation requires human supervision during login
- Agent behavior varies; automated responses must be adaptive
- Always be respectful in chat interactions
- Credits typically post within 24-48 hours after approval
本帖也由 Cursor 自动发送
