code-to-7layer

code-to-7layer is a skill for Claude Code, Codex from BackToCimaCoppi/Praxis. It costs 157 tokens per session (8,758 once invoked), scanned A, original, Apache-2.0.

A planning tool that scans an existing codebase and creates a central plan for rebuilding its documentation in seven layers. It treats code as evidence about the current system, not as the final authority.

In plain words
What is it for?
It helps detect whether a project is a monorepo, identify active applications and domains, confirm the documentation structure, and create a task plan for writing the documents.
Why use it?
It gives undocumented or outdated projects a structured starting point and separates discovery from the later writing and approval of each document layer.

Skill for Claude CodeCodex

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 skills/backtocimacoppi/praxis/code-to-7layer
Any agent
npx skills add BackToCimaCoppi/Praxis --skill code-to-7layer
Clone the repo
git clone --depth 1 https://github.com/BackToCimaCoppi/Praxis

Made for: Claude Code, Codex.

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 code-to-7layer

README.md
[![agentmods](https://agentmods.dev/badge/skills/backtocimacoppi/praxis/code-to-7layer.svg)](https://agentmods.dev/skills/backtocimacoppi/praxis/code-to-7layer)
Your own site
<a href="https://agentmods.dev/skills/backtocimacoppi/praxis/code-to-7layer"><img src="https://agentmods.dev/badge/skills/backtocimacoppi/praxis/code-to-7layer.svg" alt="Measured on agentmods" height="20"></a>
Per session 157 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,758 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00157 $0.08758
Opus 5 $0.00078 $0.04379
Sonnet 5 $0.00031 $0.01752
Haiku 4.5 $0.00016 $0.00876

Measured 3d ago against content hash 35096b9e380c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

code-to-7layer 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 3d 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.

skills/code-to-7layer/SKILL.md · 552 lines

How it starts

The opening of the file, as written. The whole thing — 552 lines — stays where its author put it; the contents beside it link to each section on GitHub.

从代码冷启动生成七层文档反推总控

交付物边界(必读):本 skill 的交付物是 Phase 0–2(扫描 + 骨架确认 + 任务总控文档创建)。具体层的文档撰写属于 Phase 3,通过 /control 逐子任务推进。本 skill 是编排器,不是文档生成器。代码、实时数据库和运行行为只能形成候选规格与冲突证据;正式 L1~L7 必须经层内裁决与当前态重写后才能冻结。

不适用场景

  • 代码与现有文档的增量同步 → 用 doc-layer-system skill
  • 仅补写 L1 需求层 → 用 docs-from-code skill

依赖 skill(子任务执行时按需引用,不需要预加载):

  • doc-layer-system §0.2(项目形态与层裁剪)— 决定哪些层适用
  • doc-layer-system §0.3(业务域与功能模块)— 域/模块等价性约定
  • docs-from-code(L1 反推方法论)— L1 层子任务执行时引用
  • control(总控文档格式与推进机制)— Phase 2 生成总控文档时引用

§1 冷启动流程总览

Phase 0       Phase 0.5        Phase 1              Phase 2         Phase 3(总控驱动)
扫描代码  →  活跃面识别  →  一次性骨架确认  →  生成总控文档  →  逐层逐域逐子任务产出文档
                                                   ↑ 本 skill 止步于此

Phase 3 的执行:通过 /control <关键词> Tn 按子任务推进,每个子任务的提取指南见 §5。


§2 Phase 0:自动扫描项目结构

进入 skill 后,无需用户输入,直接扫描:

2.1 项目形态检测(三层检测)

第一层:仓库级(是否为 monorepo/多子仓)

信号 判断
pnpm-workspace.yaml / lerna.json / nx.json / rush.json monorepo,进入子项目级检测
packages/ / apps/ / services/ 下存在多个独立子目录(各自有构建文件) 多子仓,每个子目录独立判断
无上述信号 单仓,直接进入子项目级检测

第二层:子项目级(每个子仓/单仓判断框架)

信号文件 框架/语言 初步形态
pom.xml / build.gradle Java/Kotlin 后端 纯后端候选
requirements.txt / pyproject.toml Python 后端 纯后端候选
go.mod Go 后端 纯后端候选
package.jsonexpress/koa/fastify/nestjs Node 后端 纯后端候选
package.jsonreact/vue/angular/next/nuxt 前端框架 前端候选
.wxml 文件 / wx: 标签 微信小程序 前端候选
同一构建单元同时含后端框架 + 前端框架 全栈 全栈候选

第三层:运行时入口级(确认实际执行形态)

信号 判断修正
main() / Application.run() / app.listen() 确认后端服务形态
handler / serverless.yml / template.yaml(SAM) serverless 函数形态,输出接口契约但无长驻服务
Dockerfile CMD / entrypoint.sh 确认容器化形态
fetch/axios/xhr 调用(前端项目) 确认离线前端形态

异步入口检测(同步进行,不单独成层):

信号 类型
@Scheduled / @Cron / cron 表达式 定时任务
@KafkaListener / @RabbitListener / @SqsListener 消息队列消费者
@EventListener / ApplicationEvent / EventEmitter 内部事件
WebSocket handler / @SubscribeMessage WebSocket
/webhook 路由 / callback 路由 外部 Webhook 入站

Read the full file on GitHub · 552 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. 3d ago First seen · 552 lines · 157 tokens per session scan A 35096b9e380c

Subscribe to this mod's changes

code-to-7layer is a skill published in the GitHub repository BackToCimaCoppi/Praxis (7 stars, last pushed 9d ago), licensed Apache-2.0. It adds 157 tokens to every session and 8,758 once invoked, about $0.0008 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.

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