Agent skill

Lark Shared

A skill your agent uses for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking…

AGPL-3.0Auto-check passedProductivity & Automation

Install Lark Shared

skills CLI

$ npx skills add rongxinzy/RongxinAI --skill lark-shared -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install rongxinzy/RongxinAI lark-shared --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .claude/skills && cp -r skills-src/MCPs/feishu/skills/lark-shared .claude/skills/lark-shared && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "lark-shared" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-shared into .claude/skills/lark-shared/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-shared", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-shared

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add rongxinzy/RongxinAI --skill lark-shared -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install rongxinzy/RongxinAI lark-shared --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .agents/skills && cp -r skills-src/MCPs/feishu/skills/lark-shared .agents/skills/lark-shared && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "lark-shared" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-shared into .agents/skills/lark-shared/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-shared", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add rongxinzy/RongxinAI --skill lark-shared -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install rongxinzy/RongxinAI lark-shared --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/MCPs/feishu/skills/lark-shared .cursor/skills/lark-shared && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "lark-shared" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-shared into .cursor/skills/lark-shared/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-shared", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/rongxinzy/RongxinAI.git --path MCPs/feishu/skills/lark-shared

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add rongxinzy/RongxinAI --skill lark-shared -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install rongxinzy/RongxinAI lark-shared --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/MCPs/feishu/skills/lark-shared .gemini/skills/lark-shared && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "lark-shared" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-shared into .gemini/skills/lark-shared/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-shared", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install rongxinzy/RongxinAI lark-shared

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add rongxinzy/RongxinAI --skill lark-shared -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .github/skills && cp -r skills-src/MCPs/feishu/skills/lark-shared .github/skills/lark-shared && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "lark-shared" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-shared into .github/skills/lark-shared/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-shared", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add rongxinzy/RongxinAI --skill lark-shared -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install rongxinzy/RongxinAI lark-shared --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/MCPs/feishu/skills/lark-shared .opencode/skills/lark-shared && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "lark-shared" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-shared into .opencode/skills/lark-shared/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-shared", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: lark-shared
GitHub stars: 154
Used in: 3 other repos
Token cost: ~1.7k tokens
SKILL.md length: 454 words
Files: 2 (incl. references)
Skills in repo: 94
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: AGPL-3.0

At a glance

A skill your agent uses for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking…

Works in 5 steps: 执行 lark-cli auth login --scope "xxx"… → 从 JSON 输出中提取 verification_url 和… → 生成二维码：lark-cli auth qrcode --output "xxx" → …
Lark-cli setup/auth tasks: auth login/status/logout
SKILL.md covers 配置初始化, 认证, 更新检查 and JSON 输出契约, plus 2 more sections
Calls sh

What it does

Lark Shared is an agent skill from rongxinzy/RongxinAI. Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/lark-wiki-token-routing.md`).

It sits in Productivity & Automation, covering Messaging and chat bots. The repository describes itself as: An all-in-one local AI Agent workspace with a fully self-developed stack. The licence is AGPL-3.0.

When your agent uses it

Lark-cli setup/auth tasks: auth login/status/logout
User vs bot identity
Business-domain permissions (--domain
Including all/docs/drive)

Example prompts

“/lark-shared”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

执行 lark-cli auth login --scope "xxx" --no-wait --json（必须加 --no-wait --json）
从 JSON 输出中提取 verification_url 和 device_code
生成二维码：lark-cli auth qrcode --output "xxx"
将 URL 和二维码展示给用户（先 URL，后二维码）
结束本轮对话前，必须明确告知用户："请完成授权后，回来告诉我已授权完成，我会帮你完成后续步骤"

What it can do on your machine

Read from SKILL.md and the folder at commit aa2b27b. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- sh
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Lark Shared loads about 1.7k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 454 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~57
When it runs · the whole SKILL.md, loaded when a task matches: ~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rongxinzy/RongxinAI at commit aa2b27b, republished under its AGPL-3.0 licence (© rongxinzy). 454 words, ~1,673 tokens.

Download SKILL.mdSave it as .claude/skills/lark-shared/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: lark-shared
description: Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling _notice JSON.
version: 1.0.0

lark-cli 共享规则

本技能指导你如何通过lark-cli操作飞书资源, 以及有哪些注意事项。

配置初始化

首次使用需运行 lark-cli config init 完成应用配置。

当你帮用户初始化配置时，使用background方式使用下面的命令发起配置应用流程，启动后读取输出，从中提取授权链接并发给用户。

URL 转发规则：当命令输出 verification_url、verification_uri_complete、console_url 等 URL 字段时：必须生成二维码：你必须调用 lark-cli auth qrcode 将 URL 转为二维码并展示给用户，这是必须步骤，不要跳过。优先生成 PNG 二维码（--output）；仅当用户明确要求时才使用 ASCII（--ascii）。URL 输出规则：将 URL 视为不可修改的 opaque string，不要做任何修改（包括 URL 编码/解码、添加空格或标点、重新拼接 query），二维码和链接请一起展示给用户。

bash

# 发起配置（该命令会阻塞直到用户打开链接并完成操作或过期）
lark-cli config init --new

认证

认证任务速查

认证、scope、业务域、登录态、退出登录态、撤销授权问题都走本技能。

用户意图	首选命令 / 回答
获取全部权限	`lark-cli auth login --domain all --no-wait --json`
按业务域授权	`lark-cli auth login --domain docs --domain drive --no-wait --json`；`--domain` 可重复，也可用逗号分隔
指定单个 scope 授权	`lark-cli auth login --scope "<scope>" --no-wait --json`
检查当前登录态、是谁登录、token 是否有效	`lark-cli auth status --json --verify`；回答时引用 `identity`、`verified`、`identities.user.status`、`identities.user.userName`、`identities.user.openId`（用户 open id）、`identities.user.tokenStatus`、`identities.user.scope`
快速查看当前身份状态	`lark-cli whoami`；实际生效的那一个身份
退出当前机器的用户登录态	`lark-cli auth logout --json`；`loggedOut:true` 表示注销成功
bot 缺少权限	不要执行 `auth login`；引导用户在开发者后台开通 bot scope，优先复用错误里的 `console_url`
取消用户对应用的全部服务端授权	`auth logout` 只清本机登录态；服务端授权需用户在飞书授权管理页取消
只取消一个 scope	CLI 不支持单独撤销一个已授予 scope；可重新走最小 scope 授权，或让用户在授权管理页处理

机器读取 JSON 时，为减少 _notice 干扰，可在命令前加：

bash

LARKSUITE_CLI_NO_UPDATE_NOTIFIER=1 LARKSUITE_CLI_NO_SKILLS_NOTIFIER=1 lark-cli auth status --json --verify

身份类型

两种身份类型，通过 --as 切换：

身份	标识	获取方式	适用场景
user 用户身份	`--as user`	`lark-cli auth login` 等	访问用户自己的资源（日历、云空间/云盘/云存储等）
bot 应用身份	`--as bot`	自动，只需 appId + appSecret	应用级操作,访问bot自己的资源

身份选择原则

输出的 [identity: bot/user] 代表当前身份。bot 与 user 表现差异很大，需确认身份符合目标需求：

Bot 看不到用户资源：无法访问用户的日历、云空间（云盘/云存储）文档、邮箱等个人资源。例如 --as bot 查日程返回 bot 自己的（空）日历
Bot 无法代表用户操作：发消息以应用名义发送，创建文档归属 bot
Bot 权限：只需在飞书开发者后台开通 scope，无需 auth login
User 权限：后台开通 scope + 用户通过 auth login 授权，两层都要满足

权限不足处理

遇到权限相关错误时，根据当前身份类型采取不同解决方案。

错误响应中包含关键信息：

missing_scopes：列出缺失的 scope (N选1)
console_url：飞书开发者后台的权限配置链接
hint：建议的修复命令

Bot 身份（`--as bot`）

将错误中的 console_url 原样提供给用户，引导去后台开通 scope。禁止对 bot 执行 auth login。

User 身份（`--as user`）

bash

lark-cli auth login --domain <domain>           # 按业务域授权
lark-cli auth login --scope "<missing_scope>"   # 按具体 scope 授权（推荐,符合最小权限原则）

规则：auth login 必须指定范围（--domain 或 --scope）。多次 login 的 scope 会累积（增量授权）。

Agent 代理发起认证（推荐）

当你作为 AI agent 需要帮用户完成认证时，优先使用 split-flow，避免在同一轮对话中阻塞等待用户授权：

bash

# 发起授权（立即返回 device_code 和 verification_url）
lark-cli auth login --scope "calendar:calendar:readonly" --no-wait --json

拿到 verification_url 后，将它原样作为本轮最终消息发给用户，并结束本轮/交还控制权。不要在同一轮中展示 URL 后立刻执行 --device-code 阻塞轮询；在不透传中间输出的 agent harness 里，这会导致用户永远看不到 URL。

用户回复已完成授权后，再在后续步骤执行：

bash

lark-cli auth login --device-code <device_code>

Split-Flow 完整步骤：

第一步：发起授权（当前轮）

执行 lark-cli auth login --scope "xxx" --no-wait --json（必须加 --no-wait --json）
从 JSON 输出中提取 verification_url 和 device_code
生成二维码：lark-cli auth qrcode <verification_url> --output "xxx"
将 URL 和二维码展示给用户（先 URL，后二维码）
结束本轮对话前，必须明确告知用户："请完成授权后，回来告诉我已授权完成，我会帮你完成后续步骤"

第二步：完成授权（后续轮）

等待用户回复"已完成授权"
由你（AI agent）亲自执行：lark-cli auth login --device-code <device_code>
此命令会轮询授权状态并完成登录
如果返回授权成功，流程结束

关键规则：

你必须亲自执行 --device-code 命令，不要指示用户自行执行
不要在同一轮中展示 URL 后立刻执行 --device-code，这会导致用户看不到 URL
禁止缓存 verification_url 或 device_code：每次需要授权时，必须重新执行 lark-cli auth login --no-wait --json 生成新的链接。不要将授权链接和 device code 存入上下文供后续复用

Show full SKILL.md (166 more words)Show less

更新检查

lark-cli 命令执行后，如果检测到新版本，JSON 输出中会包含 _notice.update 字段（含 message、command 等）。

除非用户正在询问更新、版本或 notice，否则不要把 _notice 原样复制为当前任务的主要答案，也不要为了 notice 中断当前任务去反复查 help。

需要稳定 JSON 给脚本或机器读取时，可以在命令前设置：

bash

LARKSUITE_CLI_NO_UPDATE_NOTIFIER=1 LARKSUITE_CLI_NO_SKILLS_NOTIFIER=1 <lark-cli command>

当你在输出中看到 _notice.update 时，先完成用户当前请求；如仍相关，再简短告知可运行：

bash

lark-cli update

重要：始终使用 lark-cli update 更新，它会同时更新 CLI 和 AI Skills。

JSON 输出契约

--format json（默认）下，成功与错误的信封结构不同：

成功信封写入 stdout（退出码 0）：

json

{ "ok": true, "identity": "user", "data": { "guid": "..." }, "meta": { "count": 1 } }

错误信封写入 stderr（退出码非 0）：

json

{ "ok": false, "identity": "user", "error": { "type": "authorization", "subtype": "missing_scope", "code": 99991679, "message": "...", "hint": "...", "missing_scopes": ["..."] } }

判断成功必须用 ok == true（或进程退出码 0），不要用 code == 0：成功信封没有顶层 code / msg 字段，code 只出现在错误信封的 error 内，含义是上游 OpenAPI 的 numeric code。按 OpenAPI 老格式 {"code": 0, "msg": "ok"} 判断会把所有成功调用误判为失败；封装写入类命令（如 task +create）时尤其危险，误判会绕过幂等逻辑导致重复创建。

安全规则

禁止输出密钥（appSecret、accessToken）到终端明文。
写入/删除操作前必须确认用户意图。
用 --dry-run 预览危险请求。
文件路径只接受相对路径：--file、--output、--output-dir、@file 等路径参数只接受 cwd 下的相对路径，传绝对路径会报 unsafe file path。数据输入（@file、大 JSON）优先用 stdin 传入，避免路径和转义问题。

高风险操作的审批协议（exit 10）

lark-cli 对高风险写操作（risk: "high-risk-write"）有强制确认门禁。当你不带 --yes 调用这类命令时，CLI 会退出码 10、并在 stderr 返回如下结构化 envelope：

json

{
  "ok": false,
  "identity": "bot",
  "error": {
    "type": "confirmation",
    "subtype": "confirmation_required",
    "message": "drive +delete requires confirmation",
    "hint": "add --yes to confirm",
    "risk": "high-risk-write",
    "action": "drive +delete"
  }
}

遇到这种情况，不要当普通错误放弃。 按以下流程处理：

识别：看到子进程 exit code = 10 且 stderr JSON 里 error.type == "confirmation"、error.subtype == "confirmation_required"
向用户确认：把 error.action、error.risk 和关键参数展示给用户，明确告知"这是高风险操作"，等待用户显式同意
用户同意 → 在你原始 argv 的末尾追加 --yes 后重试
用户拒绝 → 终止流程，不要擅自改写参数或跳过门禁

绝对不允许：

看到 exit 10 就默认加 --yes 静默重试（这等于禁用门禁）
把 confirmation_required 当网络错误/权限错误处理
在用户没明确同意的前提下追加 --yes 重试
用 sh -c 等 shell 方式拼接命令重试——用 exec.Command(argv...) 参数数组形式，避免 shell 解析把用户参数当作语法

提前预判：想先让用户 review 危险操作的具体请求，调用时加 --dry-run——它不触发门禁，会打印完整请求详情（URL / body / params），你可以把这个预览给用户看过再去真正执行。

如何识别一条命令是高风险

shortcut：lark-cli <service> +<cmd> --help 顶部会显示 Risk: high-risk-write
service 命令：lark-cli schema <service>.<resource>.<method> --format json 的返回值里 "risk": "high-risk-write"

Files

SKILL.md and 1 other file (references) in MCPs/feishu/skills/lark-shared of rongxinzy/RongxinAI.

SKILL.md
references/lark-wiki-token-routing.md

Open the folder on GitHubat commit aa2b27b

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in rongxinzy/RongxinAI, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Lark Shared next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Lark Shared compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Lark Shared this skillrongxinzy/RongxinAI	154	3 repos	~1.7k	Automated safety check: Pass	AGPL-3.0	today
Feishu Docopenclaw/openclaw	392k	—	~516	Automated safety check: Pass	MIT	today
She Love Me863401402/she-love-me	925	1 repos	~1.3k	Automated safety check: Pass	MIT	8 days ago
Feishu Docraucvr/Group-Goki	112	3 repos	~592	Automated safety check: Pass	MIT	4 mo ago
Wechat Article Extractorfreestylefly/wechat-article-extractor-skill	136	1 repos	~1k	Automated safety check: Pass	None	7 mo ago
Wechat Miniprogram Builderchenjin-cmd/wechat-miniprogram-builder	355	—	~634	Automated safety check: Pass	MIT	2 mo ago

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Feishu Doc
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392k GitHub stars~516 tokensUpdated today
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Feishu Doc
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Feishu document read/write operations. An agent skill from raucvr/Group-Goki.
112 GitHub starsUsed in 3 repos~592 tokens
Productivity & AutomationAuto-check passed
Wechat Article Extractor
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Extract metadata and content from WeChat Official Account articles.
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Productivity & AutomationAuto-check passed
Wechat Miniprogram Builder
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This skill should be used when the user wants to build, launch, monetize, or promote a WeChat mini-program with AI (vibe coding) — including topic selection, account registration & ICP filing…
355 GitHub stars~634 tokensUpdated 2 mo ago
Productivity & AutomationAuto-check passed
Slack
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A skill your agent uses when you need to control Slack from Clawdbot via the slack tool, including reacting to messages or pinning/unpinning items in Slack channels or DMs.
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Questions about Lark Shared

What does Lark Shared do?

A skill your agent uses for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking…. Lark Shared is an agent skill from rongxinzy/RongxinAI. Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.

When should I use Lark Shared?

Lark Shared fits situations like: lark-cli setup/auth tasks: auth login/status/logout; user vs bot identity; business-domain permissions (--domain; including all/docs/drive).

How do I install Lark Shared in Claude Code?

Run `npx skills add rongxinzy/RongxinAI --skill lark-shared -a claude-code`. Or copy the skill folder (MCPs/feishu/skills/lark-shared in rongxinzy/RongxinAI) into .claude/skills/lark-shared in your project. Claude Code loads it when a task matches its description.

How do I install Lark Shared in Codex?

Run `npx skills add rongxinzy/RongxinAI --skill lark-shared -a codex`. Or copy the skill folder (MCPs/feishu/skills/lark-shared in rongxinzy/RongxinAI) into .agents/skills/lark-shared in your project. Codex loads it when a task matches its description.

Can I use Lark Shared in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rongxinzy/RongxinAI --skill lark-shared -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lark-shared, .gemini/skills/lark-shared, .github/skills/lark-shared and .opencode/skills/lark-shared in your project.

What does Lark Shared need to run?

Going by SKILL.md and its folder, Lark Shared needs the command-line tools its instructions call (sh).

Does Lark Shared access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Lark Shared safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Lark Shared use?

Lark Shared is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Lark Shared use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 507 tokens, read only when the agent opens those files.

What are the alternatives to Lark Shared?

Skills that share tags, products or a category with Lark Shared: Feishu Doc (openclaw/openclaw, 392k stars), She Love Me (863401402/she-love-me, 925 stars), Feishu Doc (raucvr/Group-Goki, 112 stars) and Wechat Article Extractor (freestylefly/wechat-article-extractor-skill, 136 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lark Shared?

rongxinzy (a GitHub organization) maintains it in rongxinzy/RongxinAI, which has 154 GitHub stars. The repository holds 94 skills in this directory. The repository was last updated on October 9, 2026.

Source: rongxinzy/RongxinAI on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Lark Shared

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

lark-cli 共享规则

配置初始化

认证

认证任务速查

身份类型

身份选择原则

权限不足处理

Bot 身份（--as bot）

User 身份（--as user）

Agent 代理发起认证（推荐）

更新检查

JSON 输出契约

安全规则

高风险操作的审批协议（exit 10）

如何识别一条命令是高风险

Files

Used in 3 other repositories

Compare with similar skills

Similar skills

More from rongxinzy/RongxinAI

Related

Questions about Lark Shared

Bot 身份（`--as bot`）

User 身份（`--as user`）