lark-im

larksuite/cli · updated Apr 8, 2026

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$npx skills add https://github.com/larksuite/cli --skill lark-im
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summary

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

skill.md

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 user means user identity and uses user_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 bot means bot identity and uses tenant_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 user and bot, 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: bot only (tenant_access_token).
  • get — 获取群信息。Identity: supports user and bot; 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: supports user and bot; 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: supports user and bot.
  • update — 更新群信息。Identity: supports user and bot.

chat.members

  • create — 将用户或机器人拉入群聊。Identity: supports user and bot; the caller must be in the target chat; for bot calls, 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 with im:chat:operate_as_owner.
  • delete — 将用户或机器人移出群聊。Identity: supports user and bot; 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: supports user and bot; 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: supports user and bot; for bot calls, 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: bot only (tenant_access_token).
  • merge_forward — 合并转发消息。Identity: bot only (tenant_access_token).
  • read_users — 查询消息已读信息。Identity: bot only (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: supports user and bot.Must-read
  • create — 添加消息表情回复。Identity: supports user and bot; the caller must be in the conversation that contains the message.Must-read
  • delete — 删除消息表情回复。Identity: supports user and bot; the caller must be in the conversation that contains the message, and can only delete reactions added by itself.Must-read
  • list — 获取消息表情回复。Identity: supports user and bot; the caller must be in the conversation that contains the message.Must-read

images

  • create — 上传图片。Identity: bot only (tenant_access_token).

pins

  • create — Pin 消息。Identity: supports user and bot.
  • delete — 移除 Pin 消息。Identity: supports user and bot.
  • list — 获取群内 Pin 消息。Identity: supports user and bot.

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

How to use lark-im on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/larksuite/cli --skill lark-im

The skills CLI fetches lark-im from GitHub repository larksuite/cli and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/lark-im

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

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.629 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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