mcp-redact CLAUDE.md

mcp-redact CLAUDE.md is an instructions file for coding agents from nine710/mcp-redact. It costs 4,112 tokens per session, scanned A, original, MIT.

A project instruction file for the mcp-redact repository, a local tool that removes sensitive information from text before it is sent to an AI tool. It defines the product's purpose, boundaries, priorities, and current development scope.

In plain words
What is it for?
Use it when changing mcp-redact, especially its text-redaction engine, command-line tool, MCP server, supported sensitive-data types, or documentation. It also guides examples involving logs, environment files, JSON, YAML, HTTP headers, errors, and terminal output.
Why use it?
It gives coding agents consistent rules about what the project should and should not do. This helps prevent unrelated features, remote services, or unsupported detection methods from being added.

Instructions file

Install

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.

agentmods
npx agentmods add instructions/nine710/mcp-redact/claude-md
Clone the repo
git clone --depth 1 https://github.com/nine710/mcp-redact

Wrote 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.

agentmods badge for mcp-redact CLAUDE.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/nine710/mcp-redact/claude-md.svg)](https://agentmods.dev/instructions/nine710/mcp-redact/claude-md)
Your own site
<a href="https://agentmods.dev/instructions/nine710/mcp-redact/claude-md"><img src="https://agentmods.dev/badge/instructions/nine710/mcp-redact/claude-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 4,112 This file is loaded in full into every session.
When invoked 4,112 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.04112 $0.04112
Opus 5 $0.02056 $0.02056
Sonnet 5 $0.00822 $0.00822
Haiku 4.5 $0.00411 $0.00411

Measured 4d ago against content hash 134c711214ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mcp-redact 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 4d 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.

CLAUDE.md · 591 lines

How it starts

The opening of the file, as written. The whole thing — 591 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.

项目定位

这个仓库用于开发 mcp-redact:一个轻量、local-first 的 MCP Server 和 CLI 工具,用来在用户把日志、配置、报错、堆栈、终端输出或文件内容交给 AI 工具之前,先自动进行敏感信息脱敏。

项目的目标不是“做最强的安全平台”,而是做一个:

  • 安装简单
  • 本地可运行
  • 输出可信
  • 一眼能看懂价值
  • 10 分钟内就能试起来的开源工具

项目的核心传播点应该始终围绕:

  • “先脱敏,再喂给 AI”
  • “兼容 Claude Code / Cursor / 其他 MCP 客户端”
  • “本地运行,不把原始敏感内容先发出去”

产品边界

必须坚持的边界

  • 第一版只解决“纯文本敏感信息脱敏”问题。
  • 优先支持开发者最常复制给 AI 的内容:日志、.env、JSON、YAML、HTTP 请求头、报错信息、终端输出。
  • 优先支持高频、强感知、容易演示的敏感类型。
  • 检测与替换必须是确定性的,不能依赖 LLM 推理。
  • 默认行为必须偏保守:宁可少做炫技,也不要产生不可解释的输出。

当前阶段不应扩展的方向

以下内容默认不做,除非用户明确要求改变项目方向:

  • 云服务、账号系统、远程配置中心
  • 企业级审计、团队权限、策略编排
  • OCR、图片脱敏、音视频脱敏
  • 大规模目录递归扫描
  • IDE 插件
  • 基于 embedding、模型分类、远程 API 的检测
  • “自动修复配置安全问题”这类超出脱敏范围的功能

核心价值判断

如果某个新功能不能明显增强以下任意一点,应优先不做:

  • 更快接入 Claude Code / MCP 工作流
  • 更高频地覆盖开发者真实复制场景
  • 更低的误报和漏报
  • 更清晰的输出与更强的可解释性
  • 更低的上手门槛

MVP 目标

第一版的成功标准不是功能多,而是下面四点成立:

  1. 用户能在本地快速安装并运行。
  2. 用户能输入一段日志或一个 .env 文件并立即看到效果。
  3. 输出足够稳定,能放心贴给 AI。
  4. README 中的演示和实际工具行为一致。

MVP 功能清单

1. 核心脱敏引擎

必须提供一个共享的文本脱敏引擎,供 CLI 和 MCP Server 复用。

必做检测类型

第一批内置检测器建议分组如下:

  • secrets

    • Bearer Token
    • JWT
    • GitHub Token
    • OpenAI 风格 API Key
    • Anthropic 风格 API Key
    • AWS Access Key ID
    • 常见 password= / token= / secret= / api_key= / apikey= 键值形式
    • 多行私钥块,例如 -----BEGIN PRIVATE KEY-----
  • personal

    • 邮箱地址
    • 手机号
    • IPv4 地址
  • http

    • Authorization Header
    • Cookie Header
    • URL 中敏感 query 参数,例如 token=, key=, secret=, password=
替换要求
  • 每一类敏感内容应替换为稳定且可理解的占位符。
  • 占位符示例:
    • [REDACTED_BEARER_TOKEN]
    • [REDACTED_GITHUB_TOKEN]
    • [REDACTED_EMAIL]
    • [REDACTED_IPV4]
  • 不需要在 v1 中生成复杂的唯一 ID,但同一条规则应使用稳定的命名。
  • 占位符必须便于用户一眼判断原来是什么类型。
输出要求

脱敏结果必须同时包含:

  • 脱敏后的文本
  • 命中摘要
  • 每类规则命中了多少次

默认不输出:

  • 原始命中值
  • 原始局部片段的明文
  • 可逆的映射关系

2. CLI

CLI 是最直接的演示入口,必须尽快完成。

CLI 必备能力
  • 支持从 stdin 读取文本
  • 支持输入单个文件路径
  • 支持输出到 stdout
  • 支持输出到目标文件
  • 支持 textjson 两种格式
建议命令形态

以下命令形态是推荐目标,实际实现时保持一致性即可:

Read the full file on GitHub · 591 lines

Changes

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.

  1. 4d ago First seen · 591 lines · 4,112 tokens per session scan A 134c711214ff

Subscribe to this mod's changes

mcp-redact CLAUDE.md is an instructions file published in the GitHub repository nine710/mcp-redact (0 stars, last pushed 4mo ago), licensed MIT. It adds 4,112 tokens to every session, about $0.0206 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.

Related

Other instructions, from other repositories

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).

microsoft/vscode · 6,785 tokens

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

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.

openai/codex · 5,182 tokens

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).

microsoft/vscode · 5,001 tokens

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.

langchain-ai/langchain · 4,469 tokens

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.

deepseek-ai/deepseek-harness · 3,733 tokens