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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlark-mcpExecute the skills CLI command in your project's root directory to begin installation:
Fetches lark-mcp from whatevertogo/feishuskill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate lark-mcp. Access via /lark-mcp in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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搜索文档/知识库必须配置 OAuth:
docx_builtin_search → 需要 --oauthwiki_v1_node_search → 需要 --oauth否则返回 99991663 错误。配置方法见 installation.md
# 工具命名(连字符,非下划线)
✅ mcp__lark-mcp__tool_name
❌ mcp__lark_mcp__tool_name
# 参数结构
path: {app_token, table_id} # URL路径参数
params: {page_size, ...} # 查询参数
data: {fields, ...} # 请求体
useUAT: false # true=用户身份, false=租户身份
# content 必须是 JSON 字符串
❌ content: {"text": "hello"}
✅ content: '{"text": "hello"}'
# 过滤条件 value 必须是数组
❌ value: "已完成"
✅ value: ["已完成"]
# 创建群组必须指定 owner_id,否则群主为机器人
owner_id: "ou_xxxxx"
# 参数名差异
docx_builtin_search: search_key # 不是 query
wiki_v1_node_search: query # 不是 search_key
# token 类型
wiki_v2_space_getNode: 用 wikcn... # 不能用 doxcn...
docx_v1_document_rawContent: 用 doxcn...
| 场景 | useUAT |
|---|---|
| 创建资源(想让用户可访问) | true |
| 搜索文档/知识库 | true |
| 访问用户私有数据 | true |
| 查询公共数据 | false |
| 类别 | 工具 | 文档 |
|---|---|---|
| 消息 | im_v1_message_create, im_v1_message_list |
im.md |
| 群组 | im_v1_chat_create, im_v1_chat_list, im_v1_chatMembers_get |
chat.md |
| 多维表格 | bitable_v1_app_create, bitable_v1_appTableRecord_search/create/update |
bitable.md |
| 文档 | docx_builtin_search, docx_v1_document_rawContent, docx_builtin_import |
documents.md |
| 知识库 | wiki_v1_node_search, wiki_v2_space_getNode |
wiki.md |
| 前缀 | 类型 | 来源 |
|---|---|---|
ou_ |
用户ID | API返回 |
oc_ |
群聊ID | im_v1_chat_list |
bascn |
多维表格 | URL中 base/ 后 |
tbl |
数据表 | URL参数 table= |
doxcn |
文档 | 搜索结果或URL |
wikcn |
知识库节点 | 知识库URL |
# 发送消息
工具: mcp__lark-mcp__im_v1_message_create
data:
receive_id: "oc_xxxxx"
msg_type: "text"
content: '{"text": "消息内容"}'
params:
receive_id_type: "chat_id"
# 创建群组
工具: mcp__lark-mcp__im_v1_chat_create
data:
name: "群名"
chat_mode: "group"
owner_id: "ou_xxxxx"
user_id_list: ["ou_xxxxx"]
params:
user_id_type: "open_id"
# 创建多维表格记录
工具: mcp__lark-mcp__bitable_v1_appTableRecord_create
path:
app_token: "bascnxxxxxx"
table_id: "tblxxxxxx"
data:
fields:
文本字段: "值"
单选字段: "选项名"
useUAT: true
# 搜索文档
工具: mcp__lark-mcp__docx_builtin_search
data:
search_key: "关键词"
count: 10
useUAT: true
| 错误 | 原因 | 解决 |
|---|---|---|
| tool not found | 服务器名错误 | 使用 mcp__lark-mcp__ 前缀 |
| 99991663 | 权限不足 | useUAT: true 或配置 OAuth |
| 131005 not found | token 类型错误 | 检查用 wikcn 还是 doxcn |
| 创建资源无法访问 | 租户身份创建 | 使用 useUAT: true |
| field not found | 字段名错误 | 用 appTableField_list 确认 |
| invalid content | 格式错误 | content 用单引号包裹 JSON |
详细文档: troubleshooting.md | installation.md
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
I recommend lark-mcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
lark-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
lark-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for lark-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.
lark-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend lark-mcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: lark-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added lark-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
lark-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for lark-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.
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