CloudBase AI Toolkit is an integration layer that lets AI coding tools use Tencent CloudBase as an application backend, including databases, authentication, functions, storage, and deployments. Developers use it to have coding agents build and operate CloudBase-backed applications through skills, plugins, commands, and chat-accessible tools. The catalogue entries are the toolkit’s own skills, agents, hooks, plugins, rules, instructions, and MCP tools.
Borrowing it
Nothing to install: this file belongs to TencentCloudBase/CloudBase-AI-Toolkit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TencentCloudBase/CloudBase-AI-Toolkit/main/AGENTS.mdgit clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-ToolkitWrote 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/instructions/tencentcloudbase/cloudbase-ai-toolkit/agents-md)<a href="https://agentmods.dev/instructions/tencentcloudbase/cloudbase-ai-toolkit/agents-md"><img src="https://agentmods.dev/badge/instructions/tencentcloudbase/cloudbase-ai-toolkit/agents-md/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/instructions/tencentcloudbase/cloudbase-ai-toolkit/agents-md"><img src="https://agentmods.dev/badge/instructions/tencentcloudbase/cloudbase-ai-toolkit/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.07052 | $0.07052 |
| Opus 5.5 | $0.02821 | $0.02821 |
| Sonnet 5.5 | $0.01410 | $0.01410 |
| Haiku 4.5 | $0.00705 | $0.00705 |
Grade A, and why
CloudBase-AI-Toolkit AGENTS.md 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 yesterday.
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 — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
# 需求文档
## 介绍
需求描述
## 需求
### 需求 1 - 需求名称
**用户故事:** 用户故事内容
#### 验收标准
1. 采用 ERAS 描述的子句 While <可选前置条件>, when <可选触发器>, the <系统名称> shall <系统响应>,例如 When 选择"静音"时,笔记本电脑应当抑制所有音频输出。
2. ...
...
-
技术方案设计:对于需要 spec 的需求,在完成需求设计之后,你会根据当前的技术架构和前面确认好的需求,进行技术方案设计,保存在
specs/spec_name/design.md中,精简但是能够准确描述技术架构(例如架构、技术栈、技术选型、数据库/接口设计、测试策略、安全性),必要时可以用 mermaid 来绘图,跟我确认清楚后,才进入下阶段。对于不需要 spec 的小需求,可以直接在对话中给出精简方案并继续执行。 -
任务拆分:对于需要 spec 的需求,在完成技术方案设计后,你会根据需求文档和技术方案,细化具体要做的事情,保存在
specs/spec_name/tasks.md中,跟我确认清楚后,才开始正式执行任务,同时更新任务状态。对于不需要 spec 的小需求,可以直接给出精简任务说明或直接执行。注:
specs/已加入.gitignore,spec 文档只写在本地、不要git add到公开仓库(含内部信息,完整历史归档在私有仓库cloudbase-mcp-specs-archive)。
格式如下
# 实施计划
- [ ] 1. 任务信息
- 具体要做的事情
- ...
- _需求: 相关的需求点的编号
<project_rules>
-
项目结构
- doc 存放对外的文档
- mcp 核心的 mcp package
- config 用来给 AI IDE 提供的规则和 mcp 预设配置
- tests 自动化测试
- skills 项目级 skills 源目录
- specs 需求/设计/任务文档(本地目录,不提交到公开仓库。含内部信息,仅保留
plugin-marketplace-listing/与npm-supply-chain-security-hardening/两个被代码或规范依赖的子目录;完整历史归档在私有仓库cloudbase-mcp-specs-archive)
-
AGENTS 文件约定
AGENTS.md为项目及子目录的唯一可信源CLAUDE.md、CODEBUDDY.md均为指向AGENTS.md的软链- 新增子目录时,只需创建
AGENTS.md,用软链补齐CLAUDE.md
-
Skills & Rules 目录约定
.agents/skills为 skills 的唯一可信源,.codebuddy/skills、.claude/skills软链至此.agents/rules为 rules 的唯一可信源(尚未创建时以.agents/rules为目标)- 新增 skills 请直接添加到
skills/目录,.agents/skills/下的软链会自动关联 - 如果使用
npx skills命令添加 skills 时保留Universal选项,不用重复添加claude和codebuddy选项 - 某个目录中只要有
AGENTS.md、CLAUDE.md和.agents/skills、.claude/skills中的任意一个,就需要自动补齐
-
项目子目录规则
dsh-plugin/已有自己的AGENTS.md(源)+CLAUDE.md、CODEBUDDY.md软链;新增子包照此补齐mcp/不设自己的AGENTS.md—— mcp 包的约定已全部写在根文件里(mcp_tool_schema_rules、add_aiide、ide_mcp_upgrade、cloud_api_backend_rules等段都是针对它的),该目录下不得出现任何 IDE 兼容产物 </project_rules>
- 真源在仓库
plugins/experts/<expert-name>/(一个专家一个子目录,版本控制);与对外发布目录plugin/(CodeBuddy plugin)区分 - WorkBuddy 专家目录
~/.workbuddy/plugins/marketplaces/my-experts/plugins/是同步产物(build output),禁止手改(会被覆盖);修改一律走"改仓库源码 → 同步命令" - 同步:
npm run experts:sync [expert-name](脚本scripts/sync-experts.mjs:rsync --delete 镜像 → validate_expert.py → register_expert.py);expert-manager 脚本在 WorkBuddy app bundle 内,路径可用EXPERT_MANAGER_SCRIPTS_DIR覆盖 - 内容原则:精简有效、引用为主——Agent MD 只留角色/SOP/铁律/路由,领域知识引用运行时官方 skills,不复制进包防漂移;踩坑经验官方 skill 未覆盖的才留包内
- 后续加新专家 = 在
plugins/experts/建目录(手工建骨架,init_expert.py强制写 my-experts 目录与仓库真源冲突)→npm run experts:sync;删除专家 = 删源码目录 → 全量npm run experts:sync(脚本自动清理 my-experts 孤儿产物) - plugin.json 的
displayDescription限制 40-50 字,超长会校验 warning;tags必须恰好 3 个;quickPrompts必须恰好 3 条;defaultInitPrompt各语言须与quickPrompts[0]完全一致 - 头像统一 512×512 PNG,放
avatars/<expert-name>.png,与plugin.json的avatar字段同名同路径
<internal_dirs>
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.
- yesterday Changed · +1 lines · +50 tokens per session 3311566dd379
- 5d ago Changed · +6 lines · +389 tokens per session bc93056b2ee4
- 23d ago Changed · +1 lines · +73 tokens per session 226069cdd283
- 1mo ago Changed · +11 lines · +411 tokens per session 4359b2ae360e
- 1mo ago First seen · 313 lines · 6,129 tokens per session scan A a88b13e8de5e
CloudBase-AI-Toolkit AGENTS.md is an instructions file published in the GitHub repository TencentCloudBase/CloudBase-AI-Toolkit (1,138 stars, last pushed today), licensed MIT. It adds 7,052 tokens to every session, about $0.0282 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.
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