lark-im▌
larksuite/cli · updated Apr 8, 2026
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CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理
im (v1)
CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理
Core Concepts
- Message: A single message in a chat, identified by
message_id(om_xxx). Supports types: text, post, image, file, audio, video, sticker, interactive (card), share_chat, share_user, merge_forward, etc. - Chat: A group chat or P2P conversation, identified by
chat_id(oc_xxx). - Thread: A reply thread under a message, identified by
thread_id(om_xxx or omt_xxx). - Reaction: An emoji reaction on a message.
Resource Relationships
Chat (oc_xxx)
├── Message (om_xxx)
│ ├── Thread (reply thread)
│ ├── Reaction (emoji)
│ └── Resource (image / file / video / audio)
└── Member (user / bot)
Important Notes
Identity and Token Mapping
--as usermeans user identity and usesuser_access_token. Calls run as the authorized end user, so permissions depend on both the app scopes and that user's own access to the target chat/message/resource.--as botmeans bot identity and usestenant_access_token. Calls run as the app bot, so behavior depends on the bot's membership, app visibility, availability range, and bot-specific scopes.- If an IM API says it supports both
userandbot, the token type changes who the operator is. The same API can succeed with one identity and fail with the other because owner/admin status, chat membership, tenant boundary, or app availability are checked against the current caller.
Sender Name Resolution with Bot Identity
When using bot identity (--as bot) to fetch messages (e.g. +chat-messages-list, +threads-messages-list, +messages-mget), sender names may not be resolved (shown as open_id instead of display name). This happens when the bot cannot access the user's contact info.
Root cause: The bot's app visibility settings do not include the message sender, so the contact API returns no name.
Solution: Check the app's visibility settings in the Lark Developer Console — ensure the app's visible range covers the users whose names need to be resolved. Alternatively, use --as user to fetch messages with user identity, which typically has broader contact access.
Card Messages (Interactive)
Card messages (interactive type) are not yet supported for compact conversion in event subscriptions. The raw event data will be returned instead, with a hint printed to stderr.
Shortcuts(推荐优先使用)
Shortcut 是对常用操作的高级封装(lark-cli im +<verb> [flags])。有 Shortcut 的操作优先使用。
| Shortcut | 说明 |
|---|---|
+chat-create |
Create a group chat; user/bot; creates private/public chats, invites users/bots, optionally sets bot manager |
+chat-messages-list |
List messages in a chat or P2P conversation; user/bot; accepts --chat-id or --user-id, resolves P2P chat_id, supports time range/sort/pagination |
+chat-search |
Search visible group chats by keyword and/or member open_ids (e.g. look up chat_id by group name); user/bot; supports member/type filters, sorting, and pagination |
+chat-update |
Update group chat name or description; user/bot; updates a chat's name or description |
+messages-mget |
Batch get messages by IDs; user/bot; fetches up to 50 om_ message IDs, formats sender names, expands thread replies |
+messages-reply |
Reply to a message (supports thread replies); user/bot; supports text/markdown/post/media replies, reply-in-thread, idempotency key |
+messages-resources-download |
Download images/files from a message; user/bot; downloads image/file resources by message-id and file-key to a safe relative output path |
+messages-search |
Search messages across chats (supports keyword, sender, time range filters) with user identity; user-only; filters by chat/sender/attachment/time, supports auto-pagination via --page-all / --page-limit, enriches results via batched mget and chats batch_query |
+messages-send |
Send a message to a chat or direct message; user/bot; sends to chat-id or user-id with text/markdown/post/media, supports idempotency key |
+threads-messages-list |
List messages in a thread; user/bot; accepts om_/omt_ input, resolves message IDs to thread_id, supports sort/pagination |
API Resources
lark-cli schema im.<resource>.<method> # 调用 API 前必须先查看参数结构
lark-cli im <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。
chats
create— 创建群。Identity:botonly (tenant_access_token).get— 获取群信息。Identity: supportsuserandbot; the caller must be in the target chat to get full details, and must belong to the same tenant for internal chats.link— 获取群分享链接。Identity: supportsuserandbot; the caller must be in the target chat, must be an owner or admin when chat sharing is restricted to owners/admins, and must belong to the same tenant for internal chats.list— 获取用户或机器人所在的群列表。Identity: supportsuserandbot.update— 更新群信息。Identity: supportsuserandbot.
chat.members
create— 将用户或机器人拉入群聊。Identity: supportsuserandbot; the caller must be in the target chat; forbotcalls, added users must be within the app's availability; for internal chats the operator must belong to the same tenant; if only owners/admins can add members, the caller must be an owner/admin, or a chat-creator bot withim:chat:operate_as_owner.delete— 将用户或机器人移出群聊。Identity: supportsuserandbot; only group owner, admin, or creator bot can remove others; max 50 users or 5 bots per request.get— 获取群成员列表。Identity: supportsuserandbot; the caller must be in the target chat and must belong to the same tenant for internal chats.
messages
delete— 撤回消息。Identity: supportsuserandbot; forbotcalls, the bot must be in the chat to revoke group messages; to revoke another user's group message, the bot must be the owner, an admin, or the creator; for user P2P recalls, the target user must be within the bot's availability.forward— 转发消息。Identity:botonly (tenant_access_token).merge_forward— 合并转发消息。Identity:botonly (tenant_access_token).read_users— 查询消息已读信息。Identity:botonly (tenant_access_token); the bot must be in the chat, and can only query read status for messages it sent within the last 7 days.
reactions
batch_query— 批量获取消息表情。Identity: supportsuserandbot.Must-readcreate— 添加消息表情回复。Identity: supportsuserandbot; the caller must be in the conversation that contains the message.Must-readdelete— 删除消息表情回复。Identity: supportsuserandbot; the caller must be in the conversation that contains the message, and can only delete reactions added by itself.Must-readlist— 获取消息表情回复。Identity: supportsuserandbot; the caller must be in the conversation that contains the message.Must-read
images
create— 上传图片。Identity:botonly (tenant_access_token).
pins
create— Pin 消息。Identity: supportsuserandbot.delete— 移除 Pin 消息。Identity: supportsuserandbot.list— 获取群内 Pin 消息。Identity: supportsuserandbot.
权限表
| 方法 | 所需 scope |
|---|---|
chats.create |
im:chat:create |
chats.get |
im:chat:read |
chats.link |
im:chat:read |
chats.list |
im:chat:read |
chats.update |
im:chat:update |
chat.members.create |
im:chat.members:write_only |
chat.members.delete |
im:chat.members:write_only |
chat.members.get |
im:chat.members:read |
messages.delete |
im:message:recall |
messages.forward |
im:message |
messages.merge_forward |
im:message |
messages.read_users |
im:message:readonly |
reactions.batch_query |
im:message.reactions:read |
reactions.create |
im:message.reactions:write_only |
reactions.delete |
im:message.reactions:write_only |
reactions.list |
im:message.reactions:read |
images.create |
im:resource |
pins.create |
im:message.pins:write_only |
pins.delete |
im:message.pins:write_only |
pins.list |
im:message.pins:read |
How to use lark-im on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add lark-im
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches lark-im from GitHub repository larksuite/cli and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate lark-im. Access the skill through slash commands (e.g., /lark-im) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★29 reviews- ★★★★★Ganesh Mohane· Dec 4, 2024
lark-im fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 23, 2024
Registry listing for lark-im matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sophia Lopez· Nov 23, 2024
Solid pick for teams standardizing on skills: lark-im is focused, and the summary matches what you get after install.
- ★★★★★Chaitanya Patil· Oct 14, 2024
lark-im reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Omar Sanchez· Oct 14, 2024
lark-im has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Anaya Lopez· Sep 25, 2024
I recommend lark-im for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Henry Thomas· Aug 20, 2024
I recommend lark-im for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Anaya Haddad· Aug 16, 2024
Useful defaults in lark-im — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yash Thakker· Jul 15, 2024
lark-im has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Harper Menon· Jul 11, 2024
Keeps context tight: lark-im is the kind of skill you can hand to a new teammate without a long onboarding doc.
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