当用户没有明确指定时间范围时,根据用户意图推断合适的 relative_time,确保返回的消息能完整覆盖用户关心的内容。用户明确指定时间时直接使用用户的值。
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfeishu-im-readExecute the skills CLI command in your project's root directory to begin installation:
Fetches feishu-im-read from larksuite/openclaw-lark 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 feishu-im-read. Access via /feishu-im-read 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.
Submit your Claude Code skill and start earning
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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feishu_im_user_get_messages 中 open_id 和 chat_id 必须二选一thread_id 时,根据用户意图判断是否用 feishu_im_user_get_thread_messages 读取话题内回复feishu_im_user_fetch_resource 下载,需要 message_id + file_key + type| 用户意图 | 工具 | 必填参数 | 常用可选 |
|---|---|---|---|
| 获取群聊/单聊历史消息 | feishu_im_user_get_messages | chat_id 或 open_id(二选一) | relative_time, start_time/end_time, page_size, sort_rule |
| 获取话题内回复消息 | feishu_im_user_get_thread_messages | thread_id(omt_xxx) | page_size, sort_rule |
| 跨会话搜索消息 | feishu_im_user_search_messages | 至少一个过滤条件 | query, sender_ids, chat_id, relative_time, start_time/end_time, page_size |
| 下载消息中的图片 | feishu_im_user_fetch_resource | message_id, file_key(img_xxx), type="image" | - |
| 下载消息中的文件/音频/视频 | feishu_im_user_fetch_resource | message_id, file_key(file_xxx), type="file" | - |
当用户没有明确指定时间范围时,根据用户意图推断合适的 relative_time,确保返回的消息能完整覆盖用户关心的内容。用户明确指定时间时直接使用用户的值。
page_size 范围 1-50,默认 50has_more=true 时,可使用 page_token 继续获取下一页获取历史消息时,返回的消息中如果包含 thread_id 字段,推荐主动获取话题的最新 10 条回复(page_size: 10, sort_rule: "create_time_desc")以提供更完整的上下文。
| 场景 | 行为 |
|---|---|
| 获取历史消息并需要理解上下文(默认) | 对发现的 thread_id 调用 feishu_im_user_get_thread_messages 获取最新 10 条回复 |
| 用户要求"完整对话"、"详细讨论"、"看看回复" | 获取话题全部回复(page_size: 50, sort_rule: "create_time_asc"),需要时翻页 |
| 用户只浏览消息概览 / 用户明确说不看回复 | 跳过话题展开 |
注意:话题消息不支持时间过滤(飞书 API 限制),只能通过分页获取。
feishu_im_user_search_messages 支持跨所有会话搜索消息:
| 参数 | 说明 |
|---|---|
query |
搜索关键词,匹配消息内容 |
sender_ids |
发送者 open_id 列表 |
chat_id |
限定搜索范围的会话 ID |
mention_ids |
被@用户的 open_id 列表 |
message_type |
消息类型:file / image / media |
sender_type |
发送者类型:user / bot / all(默认 user) |
chat_type |
会话类型:group / p2p |
搜索结果每条消息额外包含 chat_id、chat_type(p2p/group)、chat_name。单聊消息还有 chat_partner(对方 open_id 和名字)。
消息内容中可能出现以下资源标记,用 feishu_im_user_fetch_resource 下载:
| 资源类型 | 内容中的标记格式 | fetch_resource 参数 |
|---|---|---|
| 图片 |  |
message_id=om_xxx, file_key=img_xxx, type="image" |
| 文件 | <file key="file_xxx" .../> |
message_id=om_xxx, file_key=file_xxx, type="file" |
| 音频 | <audio key="file_xxx" .../> |
message_id=om_xxx, file_key=file_xxx, type="file" |
| 视频 | <video key="file_xxx" .../> |
message_id=om_xxx, file_key=file_xxx, type="file" |
从消息的 message_id 字段和内容中的 file_key 组合即可调用 fetch_resource。
注意:文件大小限制 100MB,不支持下载表情包、卡片中的资源。
feishu_im_user_get_messages 和 feishu_im_user_search_messages 支持时间过滤,话题消息不支持。
| 方式 | 参数 | 示例 |
|---|---|---|
| 相对时间 | relative_time |
today、yesterday、this_week、last_3_days、last_24_hours |
| 精确时间 | start_time + end_time |
ISO 8601 格式:2026-02-27T00:00:00+08:00 |
relative_time 和 start_time/end_time 互斥,不能同时使用today、yesterday、day_before_yesterday、this_week、last_week、this_month、last_month、last_{N}_{unit}(unit: minutes/hours/days)| 参数 | 格式 | 适用场景 |
|---|---|---|
| chat_id | oc_xxx |
已知会话 ID(群聊或单聊均可) |
| open_id | ou_xxx |
已知用户 ID,获取与该用户的单聊消息(自动解析为 chat_id) |
两者必须二选一,优先使用 chat_id。
步骤 1:获取群聊消息
{ "chat_id": "oc_xxx" }
步骤 2:返回的消息中发现 thread_id,展开话题最新回复:
{ "thread_id": "omt_xxx", "page_size": 10, "sort_rule": "create_time_desc" }
{ "query": "项目进度", "chat_id": "oc_xxx" }
第一次调用返回 has_more: true 和 page_token: "xxx",继续获取:
{ "chat_id": "oc_xxx", "page_token": "xxx" }
{ "message_id": "om_xxx", "file_key": "img_v3_xxx", "type": "image" }
| 错误现象 | 根本原因 | 解决方案 |
|---|---|---|
| 消息结果太少 | 时间范围太窄或未传时间参数 | 根据用户意图推断合适的 relative_time |
| 消息不完整 | 没有检查 has_more 并翻页 | has_more=true 时用 page_token 翻页 |
| 话题讨论内容不完整 | 没有展开 thread_id | 发现 thread_id 时获取话题回复 |
| "open_id 和 chat_id 不能同时提供" | 同时传了两个参数 | 只传其中一个 |
| "relative_time 和 start_time/end_time 不能同时使用" | 时间参数冲突 | 选择一种时间过滤方式 |
| "未找到与 open_id=xxx 的单聊会话" | 没有单聊记录 | 改用 chat_id,或确认存在单聊 |
| 话题消息返回为空 | thread_id 格式不正确 | 确认为 omt_xxx 格式 |
| 图片/文件下载失败 | file_key 或 message_id 不匹配 | 确认 file_key 来自该 message_id |
| 权限不足 | 用户未授权或无权限 | 确认已完成 OAuth 授权且是会话成员 |
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
pproenca/dot-skills
mattpocock/skills
Useful defaults in feishu-im-read — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
feishu-im-read is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for feishu-im-read matched our evaluation — installs cleanly and behaves as described in the markdown.
feishu-im-read reduced setup friction for our internal harness; good balance of opinion and flexibility.
feishu-im-read is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in feishu-im-read — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added feishu-im-read from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: feishu-im-read is the kind of skill you can hand to a new teammate without a long onboarding doc.
feishu-im-read is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
feishu-im-read reduced setup friction for our internal harness; good balance of opinion and flexibility.
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