openlark-code-standards — feishu openlark-code-standards, open-lark, community, feishu, ide skills, feishu-bot, feishu-sdk, lark-bot, lark-sdk, Claude Code, Cursor

v1.0.0
GitHub

About this Skill

Perfect for Rust-based AI Agents needing to enforce OpenLark code standards and best practices in their projects. 飞书开放平台的非官方 Rust SDK，支持自定义机器人、长连接机器人、云文档、飞书卡片、消息、群组等 API 调用。

# Core Topics

feishu feishu-bot feishu-sdk lark-bot lark-sdk
foxzool foxzool
[66]
[20]
Updated: 2/28/2026

Agent Capability Analysis

The openlark-code-standards skill by foxzool is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for feishu, feishu-bot, feishu-sdk.

Ideal Agent Persona

Perfect for Rust-based AI Agents needing to enforce OpenLark code standards and best practices in their projects.

Core Value

Empowers agents to enforce consistent coding standards across OpenLark projects, utilizing Rust SDK for comprehensive API checks, including custom robot, long connection robot, cloud document, and message API calls, ensuring adherence to established conventions and minimizing style drift.

↓ Capabilities Granted for openlark-code-standards

Automating code reviews for OpenLark projects
Validating API implementations against OpenLark standards
Debugging inconsistencies in coding styles across different crates

! Prerequisites & Limits

  • Requires access to OpenLark project codebase
  • Limited to Rust-based projects
  • Priority coverage of specific crates like openlark-core, openlark-client, openlark-docs, and openlark-communication
Labs Demo

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Boot Container Sandbox

openlark-code-standards

Install openlark-code-standards, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

OpenLark 代码规范检查（Skill）

适用场景

  • 用户要求“检查项目代码规范”
  • 新增 API 前想确认实现套路是否一致
  • 评审 PR 时需要快速验证是否遵循 OpenLark 既有约定
  • 发现模块风格漂移，想做一次统一体检

目标

输出一份可落地的规范检查结果，包含：

  • 规范结论（通过/风险）
  • 规则-证据对（每条规则附 path:line）
  • 风险分级（P0/P1/P2）
  • 新 API 最小检查清单（可直接用于 PR Review）

检查范围

优先覆盖：

  • crates/openlark-core
  • crates/openlark-client
  • crates/openlark-docs
  • crates/openlark-communication

可按参数缩小为某个 crate 或目录。

核心检查项

1) API 实现套路一致性

  • 是否使用 Request/Response + Builder 模式
  • 是否提供 execute() 与 execute_with_options(RequestOption)
  • 是否通过 Transport::request(...) 发送请求

2) 端点定义规范

  • 是否使用 ApiEndpoint 枚举/端点常量
  • 是否避免手写业务 URL
  • 是否通过 to_url() 或统一端点入口生成路径

3) 参数校验规范

  • 必填校验是否统一用 openlark_core::validate_required!
  • 字符串是否优先 trim() 后再校验
  • 列表字段是否校验非空与长度上限（如 validate_required_list!）

4) 命名与公开 API 表达

  • Client/Service/Resource/Request/Builder 命名是否语义清晰
  • 对外入口是否统一（避免同义入口并存）
  • meta 调用链命名是否与仓库约定一致

5) 导出与 feature gating

  • mod.rs 与 prelude 是否完整导出新增 API
  • Cargo.toml feature 与 #[cfg(feature = "...")] 是否对齐
  • 是否存在导出但不可编译或不可访问路径

输出模板（必须）

  1. 结论概览（3-6 条）
  2. 规则-证据对（至少 8 条）
    • 规则
    • 证据（path:line）
    • 风险等级（P0/P1/P2）
    • 修复建议
  3. 新 API 最小检查清单（5-8 条）
  4. 建议行动项（按优先级排序）

推荐执行顺序

  1. 先读 AGENTS.md 与目标 crate 的 AGENTS.md/CLAUDE.md
  2. 再扫规则高频证据：api_endpoints、execute_with_options、validate_required、prelude、mod.rs
  3. 最后输出规则-证据对与整改建议

重点提醒

  • 结论必须基于代码证据，不做“纯经验判断”
  • 证据至少精确到文件路径，推荐精确到 path:line
  • 不在规范检查中进行大规模重构，先给出可执行清单

与其他技能的关系

  • 需要审查整体架构与公共 API：openlark-design-review
  • 只聚焦校验写法统一：openlark-validation-style
  • 需要新增/重构具体 API：openlark-api
  • 需要做覆盖率统计：openlark-api-validation

技能分流决策表

场景优先技能何时转交
只想做一次项目规范体检（给出规则-证据对和风险清单）openlark-code-standards若发现架构级冲突/公共 API 设计分歧，转 openlark-design-review
重点是架构收敛、范式选型、兼容策略（含 breaking 评估）openlark-design-review若需要补充全仓规范一致性证据，可回补 openlark-code-standards
只处理 validate()、validate_required!、空白字符串与校验聚合openlark-validation-style若校验问题已扩展到命名/导出/端点体系，转 openlark-code-standards
新增或重构某个具体 API 文件（Request/Response/Builder/导出）openlark-api若实现前需先做规范体检，先跑 openlark-code-standards
关注 API 覆盖率、实现数量、缺失清单openlark-api-validation若覆盖率问题背后是设计不一致，转 openlark-design-review

快速判断

  • 问题是“这个项目现在规范是否一致” → openlark-code-standards
  • 问题是“这个设计该怎么收敛” → openlark-design-review
  • 问题是“这个校验写法到底怎么统一” → openlark-validation-style
  • 问题是“我现在就要实现某个 API” → openlark-api

关键词触发映射

用户关键词/表述建议技能
代码规范、规范检查、风格一致性、体检、对齐约定openlark-code-standards
架构设计、public API、收敛方案、feature gating、兼容策略、breaking changeopenlark-design-review
validate、必填校验、validate_required、空白字符串、校验聚合openlark-validation-style
新增 API、重构 API、Builder、Request/Response、mod.rs 导出openlark-api
覆盖率、实现数量、缺失 API、统计、对比 CSVopenlark-api-validation

组合关键词优先级

  • 同时出现“规范检查 + 覆盖率”时，先用 openlark-api-validation 产出缺失清单，再用 openlark-code-standards 做规范归因。
  • 同时出现“规范检查 + 架构收敛”时，先用 openlark-code-standards 做现状证据，再转 openlark-design-review 定迁移方案。
  • 同时出现“新增 API + 校验统一”时，实现阶段用 openlark-api，校验规则判定用 openlark-validation-style。

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is openlark-code-standards?

Perfect for Rust-based AI Agents needing to enforce OpenLark code standards and best practices in their projects. 飞书开放平台的非官方 Rust SDK，支持自定义机器人、长连接机器人、云文档、飞书卡片、消息、群组等 API 调用。

How do I install openlark-code-standards?

Run the command: npx killer-skills add foxzool/open-lark. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for openlark-code-standards?

Key use cases include: Automating code reviews for OpenLark projects, Validating API implementations against OpenLark standards, Debugging inconsistencies in coding styles across different crates.

Which IDEs are compatible with openlark-code-standards?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for openlark-code-standards?

Requires access to OpenLark project codebase. Limited to Rust-based projects. Priority coverage of specific crates like openlark-core, openlark-client, openlark-docs, and openlark-communication.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add foxzool/open-lark. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use openlark-code-standards immediately in the current project.

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