dingtalk-skills — Codex Skill by breath57

by breath57 · Codex Skill · ★ 83

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

About dingtalk-skills

钉钉 Agent 技能库(dingtalk-skills) English | 中文 已适配 OpenClaw 🦞 & Hermes,可从 ClawHub · Skills.sh 一键安装。 让 AI Agent 直接操作钉钉——无需手写 API 调用,无需手动管理 Token,对话即操作。 基于 Anthropic skills 规范 构建,仅依赖 ,无需安装 Python、SDK 或任何第三方库。安装一行命令,Agent 即可理解"什么时候该调钉钉 API、该调哪个、参数怎么填",并自动管理配置、错误处理。 为什么用这个 对话即操作:"帮我在任务表里加三条记录" → Agent 自动完成,无需你知道任何 API 零依赖:仅使用 发起 HTTP 请求,无需安装 Python、SDK 或任何第三方库 一次配置,永久生效:首次使用时 Agent 统一询问 appKey/appSecret/operatorId,写入 ,后续所有技能直接复用,不再重复问 长期目标 本项目有两条并行的长期主线: 1. 永远只依赖 不引入任何 SDK、运行时或第三方库。只要系统有 ,技能就能运行。这保证了最大的可移植性,也让技能在任何 Agent 环境中都能免安装直接使用。 2. 将每次调用消耗的 Token 压到极限 Agent 每次执行任务都需要将技能文件装入上下文,skill 文件本身就是成本。我们的目标不只是「能用」,而是在保证正确率的前提下,把 和 写得尽可能精炼——删掉所有冗余解释,用最短的指令表达最完整的语义。每一个字都要赚回自己的位置。 技能纵览 [🦞 ClawHub](https://cla

agent-skillsai-agentai-tableanthropic-skillsdingtalkdingtalk-apidocumentopenclawopenclaw-skillskills-sh

Quick Facts

Stars83
Forks9
LanguagePython
CategoryCodex Skill
Quality Score60.3319014554319/100
Open Issues3
Last Updated2026-06-26
Created2026-03-05
Platformspython
Est. Tokens~16k

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Frequently Asked Questions

What is dingtalk-skills?

dingtalk-skills is 【Openclaw & Hermes & 企业机器人🤖】钉钉Skill库,含:钉钉文档、知识库、AI(多维)表格、群(单)聊、会议室、日程、待办、通讯录、AI搜索等,自动管理配置,轻量级—仅依赖curl, skill经过严格测试。Lightweight DingTalk Skill Library (curl only). Includes AI search, docs, sheets, ch. It is categorized as a Codex Skill with 83 GitHub stars.

What programming language is dingtalk-skills written in?

dingtalk-skills is primarily written in Python. It covers topics such as agent-skills, ai-agent, ai-table.

How do I install or use dingtalk-skills?

You can find installation instructions and usage details in the dingtalk-skills GitHub repository at github.com/breath57/dingtalk-skills. The project has 83 stars and 9 forks, indicating an active community.

What are the best alternatives to dingtalk-skills?

The top alternatives to dingtalk-skills on Agent Skills Hub include decodo-openclaw-skill, skillport, golembot. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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