Borrowing it
Nothing to install: this file belongs to calderbuild/ai-smooth-talker. 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/calderbuild/ai-smooth-talker/main/CLAUDE.mdgit clone --depth 1 https://github.com/calderbuild/ai-smooth-talkerWrote 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/calderbuild/ai-smooth-talker/claude-md)<a href="https://agentmods.dev/instructions/calderbuild/ai-smooth-talker/claude-md"><img src="https://agentmods.dev/badge/instructions/calderbuild/ai-smooth-talker/claude-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/calderbuild/ai-smooth-talker/claude-md"><img src="https://agentmods.dev/badge/instructions/calderbuild/ai-smooth-talker/claude-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 | $0.02349 | $0.02349 |
| Opus 5 | $0.01175 | $0.01175 |
| Sonnet 5 | $0.00470 | $0.00470 |
| Haiku 4.5 | $0.00235 | $0.00235 |
Grade A, and why
ai-smooth-talker CLAUDE.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 9d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
项目概述
这是一个基于 React + Trickle 平台的 AI 聊天助手 Web 应用,专注于提供智能化的高情商回复建议。项目使用纯前端架构(无构建系统),通过 CDN 加载 React、Babel 和 TailwindCSS,依赖 Trickle 平台的数据存储和 SiliconFlow API 的 AI 能力。
核心架构
技术栈
- 前端框架: React 18 (UMD 版本,通过 CDN 加载)
- UI 样式: TailwindCSS (CDN) + CSS 变量系统
- 图标: Lucide Icons
- 3D 组件: Spline Viewer
- AI 服务: SiliconFlow API (Qwen/Qwen2.5-72B-Instruct 模型)
- 数据存储: Trickle 平台对象存储 API
- 代理服务: Trickle Proxy API (
https://proxy-api.trickle-app.host)
项目结构
EP_Agent/
├── index.html # 主页面入口
├── admin.html # 管理后台
├── history.html # 聊天历史页面
├── app.js # 主应用入口
├── admin-app.js # 管理后台入口
├── history-app.js # 历史记录入口
├── components/ # React 组件库
│ ├── ChatSimulator.js # 核心聊天模拟器
│ ├── AICustomerService.js # AI 客服窗口
│ ├── HeroSection.js # 首页英雄区(含 3D 组件)
│ ├── SuccessStories.js # 成功案例展示
│ ├── Admin*.js # 管理后台组件
│ └── ...
├── utils/ # 工具函数
│ ├── chatHelper.js # AI 回复生成、聊天记录存储
│ ├── customerService.js # 客服 AI 对话逻辑
│ └── adminHelper.js # 管理统计数据处理
└── trickle/
├── notes/ # 项目文档和改进记录
├── rules/ # 设计规范(色彩一致性等)
└── assets/ # 静态资源配置(JSON 数据)
数据流
- 用户输入 →
ChatSimulator组件 - AI 请求 →
chatHelper.js:generateReplies()→ SiliconFlow API (通过 Trickle Proxy) - 回复生成 → 解析 JSON 响应 → 展示多种回复建议
- 用户选择 → 保存至 Trickle 数据库 (
chat_history对象) - 管理查看 →
adminHelper.js读取聊天记录 → 统计分析
关键依赖和 API
Trickle 平台 API
所有数据存储操作都通过 Trickle 平台的全局方法:
trickleCreateObject(type, data): 创建对象(如聊天记录)trickleListObjects(type, limit, reverse): 查询对象列表- 数据类型:
chat_history(存储用户输入、AI 建议、反馈)
SiliconFlow AI API
- 端点:
https://api.siliconflow.cn/v1/chat/completions - 模型:
Qwen/Qwen2.5-72B-Instruct(中文优化) - API Key: 存储在
utils/chatHelper.js:2(生产环境应移至环境变量) - 代理: 所有请求通过
https://proxy-api.trickle-app.host转发 - 降级策略: API 失败时使用
invokeAIAgent()(Trickle 内置 AI)
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.
- 9d ago First seen · 210 lines · 2,349 tokens per session scan A 10e6a7976192
ai-smooth-talker CLAUDE.md is an instructions file published in the GitHub repository calderbuild/ai-smooth-talker (5 stars, last pushed 10mo ago), licensed MIT. It adds 2,349 tokens to every session, about $0.0117 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 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.