lark-shared

lark-shared is a skill for Claude Code, Codex from yokingma/weclaws. It costs 48 tokens per session (1,627 once invoked), scanned A, original, MIT.

A shared skill for setting up and using lark-cli, a command-line tool for Feishu resources. It covers login, switching between user and bot identities, updates, and permission errors.

In plain words
What is it for?
Use it to initialize lark-cli, authenticate users, operate as a bot, diagnose missing permissions, and handle authorization links.
Why use it?
It explains which identity and permissions a Feishu operation needs, reducing failed requests caused by using the wrong account type or missing access.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/yokingma/weclaws/lark-shared
Any agent
npx skills add yokingma/weclaws --skill lark-shared
Clone the repo
git clone --depth 1 https://github.com/yokingma/weclaws

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for lark-shared

README.md
[![agentmods](https://agentmods.dev/badge/skills/yokingma/weclaws/lark-shared.svg)](https://agentmods.dev/skills/yokingma/weclaws/lark-shared)
Your own site
<a href="https://agentmods.dev/skills/yokingma/weclaws/lark-shared"><img src="https://agentmods.dev/badge/skills/yokingma/weclaws/lark-shared.svg" alt="Measured on agentmods" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,627 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.01627
Opus 5 $0.00024 $0.00813
Sonnet 5 $0.00010 $0.00325
Haiku 4.5 $0.00005 $0.00163

Measured 4d ago against content hash 732465dba8c1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

lark-shared scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

resources/skills/managed/lark-shared/SKILL.md · 139 lines

How it starts

The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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_urlverification_uri_completeconsole_url 等 URL 字段时,必须将 URL exactly as returned by the CLI 转发给用户,并把它视为不可修改的 opaque string;不要做 URL encode/decode,不要补 %20、空格或标点,不要重新拼接 query,不要改写成 Markdown link text,建议用只包含原始 URL 的代码块单独输出。

# 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

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 访问用户自己的资源(日历、云空间等)
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:

  • permission_violations: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
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--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,使用background方式 执行以下命令发起授权流程, 并将授权链接原样发给用户:

# 发起授权(阻塞直到用户授权完成或过期)
lark-cli auth login --scope "calendar:calendar:readonly"

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 messagecommand 等)。

当你在输出中看到 _notice.update 时,完成用户当前请求后,主动提议帮用户更新

  1. 告知用户当前版本和最新版本号
  2. 提议执行更新(同时更新 CLI 和 Skills):
    lark-cli update
    
  3. 更新完成后提醒用户:退出并重新打开 AI Agent 以加载最新 Skills

Read the full file on GitHub · 139 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 139 lines · 48 tokens per session scan A 732465dba8c1

Subscribe to this mod's changes

lark-shared is a skill published in the GitHub repository yokingma/weclaws (38 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 1,627 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

wxjava-module-selector

根据微信公众号、小程序、微信支付、企业微信、开放平台、视频号或微信小店、腾讯企点和微信智能对话等业务场景,为用户选择合适的 WxJava Maven 模块、BOM 和示例入口。适用于用户询问“该用哪个模块”、依赖坐标、产品边界或单/多账号 Starter 选择时。.

binarywang/WxJava · 86 tokens

wxjava-troubleshooter

排查 WxJava 在配置初始化、access token、签名验签、支付证书、回调通知、序列化、网络请求和多账号隔离方面的问题。适用于用户提供异常、日志、请求响应或“WxJava 为什么不能调用”的场景。.

binarywang/WxJava · 65 tokens

wxjava-api-contributor

按 WxJava 的 Maven 多模块、Java 8、公共 API 兼容性和 TestNG 约定,为微信官方接口新增或维护 SDK 支持。适用于新增 Service API、请求响应 Bean、序列化、HTTP 实现、Starter 配置或回归测试时。.

binarywang/WxJava · 68 tokens

wxjava-integration-guide

为 Java、Spring Boot 或 Solon 项目生成可验证的 WxJava 接入方案,包括模块选择、BOM、配置、最小调用代码以及单/多账号集成。适用于用户要求接入公众号、小程序、支付、企业微信、开放平台、视频号或微信小店时。.

binarywang/WxJava · 72 tokens

wxjava-upgrade-guide

规划 WxJava 的版本升级与迁移,检查 BOM、模块依赖、Java 版本、配置与公共 API 兼容性,并提供可回滚的验证步骤。适用于用户从旧版升级、切换依赖管理方式、处理兼容性告警或制定升级发布计划时。.

binarywang/WxJava · 72 tokens

WeChat Official Account Strategist

Create WeChat Official Account (微信公众号) content that grows followers and drives conversion. Platform-native strategy, writing, and growth tactics.

demo112/yunqu-ai-skills · 33 tokens