ReMe is a local-first memory management system that turns conversations and other resources into searchable, editable Markdown knowledge shared across AI agents. It is for users and agent runtimes that need durable, interconnected knowledge they can inspect and maintain. The catalogue add-ons connect coding agents and other agents to ReMe through skills, hooks, plugins, MCP, and related interfaces.
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.
npx skills add agentscope-ai/ReMe --skill dingtalk-messagegit clone --depth 1 https://github.com/agentscope-ai/ReMeWrote 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.
[](https://agentmods.dev/skills/agentscope-ai/reme/dingtalk-message)<a href="https://agentmods.dev/skills/agentscope-ai/reme/dingtalk-message"><img src="https://agentmods.dev/badge/skills/agentscope-ai/reme/dingtalk-message/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentscope-ai/reme/dingtalk-message"><img src="https://agentmods.dev/badge/skills/agentscope-ai/reme/dingtalk-message.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.02423 |
| Opus 5.5 | $0.00024 | $0.00969 |
| Sonnet 5.5 | $0.00012 | $0.00485 |
| Haiku 4.5 | $0.00006 | $0.00242 |
Grade A, and why
dingtalk-message 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
钉钉消息发送技能
概述
支持两种接入方式：
- Webhook 自定义机器人：通过 access_token 向群聊发送消息，接入简单
- 企业内部机器人：通过 app_key/app_secret 发送单聊、群聊消息，功能更全
支持多机器人管理，只有一个时自动使用，多个时按优先级自动选择。
环境要求
- Python 3.7+
pip install requests
首次配置
引导流程
首次使用此技能时，必须按以下流程引导用户完成配置：
-
询问用户的机器人类型和凭证信息：
- Webhook 机器人：需要
access_token（和可选的加签密钥secret） - 企业内部机器人：需要
app_key、app_secret、robot_code（和可选的agent_id）
- Webhook 机器人：需要
-
执行配置命令：
python scripts/dingtalk.py robot-add --name "机器人名" --type webhook ... -
验证配置：
python scripts/dingtalk.py config --show
配置文件路径
配置统一存储在系统配置目录，所有 AI agent 共享，无需重复配置：
| 平台 | 配置文件 | 状态文件 |
|---|---|---|
| macOS / Linux | ~/.config/dingtalk/config.json |
~/.config/dingtalk/state.json |
| Windows | %APPDATA%\dingtalk\config.json |
%APPDATA%\dingtalk\state.json |
手动编辑配置文件
也可直接编辑配置文件，每个机器人用 name 标识（建议用群名、用途等有意义的名称），description 描述用途，方便智能匹配：
{
"default_robot": "技术告警群",
"robots": [
{
"name": "技术告警群",
"type": "webhook",
"description": "发送技术告警到后端技术群",
"webhook_token": "你的access_token",
"webhook_secret": ""
},
{
"name": "内部通知机器人",
"type": "app",
"description": "企业内部机器人，支持单聊和群聊",
"app_key": "你的AppKey",
"app_secret": "你的AppSecret",
"robot_code": "你的机器人编号",
"agent_id": ""
}
]
}
webhook_token填 access_token 即可，脚本自动拼接完整 URL。
通过命令行添加
# 添加 Webhook 机器人
python scripts/dingtalk.py robot-add --name "技术告警群" --type webhook --webhook-token "access_token_xxx" --desc "发送告警到后端技术群"
# 添加企业内部机器人
python scripts/dingtalk.py robot-add --name "内部通知" --type app --app-key dingxxx --app-secret xxx --robot-code robot-xxx --desc "支持单聊群聊"
验证配置
python scripts/dingtalk.py config --show
机器人管理
选择逻辑
- 只配置一个机器人时，自动使用
- 多个机器人时：
--robot指定 >default_robot> 最近使用过的 > 第一个可用的 - 用户未明确指定时，可根据机器人的
description和最近消息记录智能匹配，或询问用户
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 269 lines · 61 tokens per session scan A 4b9c165d2366
dingtalk-message is a skill published in the GitHub repository agentscope-ai/ReMe (3,656 stars, last pushed yesterday), licensed Apache-2.0. It adds 61 tokens to every session and 2,423 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
add-new-entry-from-temp-md
Format new entries from temp.md into tempentries.md, ready to insert into the section files. USE FOR: adding new resources to the knowledge base. DO NOT USE FOR: editing existing entries or restructuring sections.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
browserwing-executor
Control browser automation through HTTP API. Supports page navigation, element interaction (click, type, select), data extraction, accessibility snapshot analysis, screenshot, JavaScript execution, and batch operations.
dev-browser
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website"…
ask-user-question
Ask users questions via the UI. Use when you need clarification, user preferences, or confirmation before proceeding. The user CANNOT see CLI output - this tool is the ONLY way to communicate with them.
pixelbrowse
Screenshot and visually read any web page or document using pixelshot. Use instead of fetching raw HTML when you need to see what a page looks like, read visual content (charts, diagrams, infographics), check layouts, or verify UI. Triggers: "look at this page", "screenshot", "what does this site look like", "check…