Fetches lark-mail from larksuite/cli and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/lark-mail
Restart Cursor to activate lark-mail. Access via /lark-mail in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Use when reading full content for multiple emails by message ID. Prefer this shortcut over calling raw mail user_mailbox.messages batch_get directly, because it base64url-decodes body fields and returns normalized per-message output that is easier to consume.
Use when querying a full mail conversation/thread by thread ID. Returns all messages in chronological order, including replies and drafts, with body content and attachments metadata, including inline images.
Watch for incoming mail events via WebSocket (requires scope mail:event and bot event mail.user_mailbox.event.message_received_v1 added). Run with --print-output-schema to see per-format field reference before parsing output.
Reply to a message and save as draft (default). Use --confirm-send to send immediately after user confirmation. Sets Re: subject, In-Reply-To, and References headers automatically.
Reply to all recipients and save as draft (default). Use --confirm-send to send immediately after user confirmation. Includes all original To and CC automatically.
Create a brand-new mail draft from scratch (NOT for reply or forward). For reply drafts use +reply; for forward drafts use +forward. Only use +draft-create when composing a new email with no parent message.
Use when updating an existing mail draft without sending it. Prefer this shortcut over calling raw drafts.get or drafts.update directly, because it performs draft-safe MIME read/patch/write editing while preserving unchanged structure, attachments, and headers where possible.
Forward a message and save as draft (default). Use --confirm-send to send immediately after user confirmation. Original message block included automatically.
API Resources
lark-cli schema mail.<resource>.<method># 调用 API 前必须先查看参数结构lark-cli mail <resource><method>[flags]# 调用 API
重要：使用原生 API 时，必须先运行 schema 查看 --data / --params 参数结构，不要猜测字段格式。
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
