dojo-hack

A project-improvement guide for the fourth Codojo learning stage. After the teaching plan is complete, it suggests specific changes and carries them out according to a shared plan.

In plain words
What is it for?
Use it to add features, reorganise code, improve performance, expand tests, or strengthen project tools such as CI/CD, logging, monitoring, and documentation.
Why use it?
It turns a broad wish to improve a project into a set of concrete tasks. It also lets the user pause and continue the work from the saved plan.

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/ttguy0707/codojo/dojo-hack
Any agent
npx skills add ttguy0707/codojo --skill dojo-hack
Clone the repo
git clone --depth 1 https://github.com/ttguy0707/codojo

Made for: Claude Code, Codex.

Per session 348 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,179 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 $0.00348 $0.02179
Opus 5 $0.00174 $0.01090
Sonnet 5 $0.00070 $0.00436
Haiku 4.5 $0.00035 $0.00218

Measured 2d ago against content hash 99370e23641f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dojo-hack 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 2d 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/dojo-hack/SKILL.md · 221 lines

How it starts

The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.

dojo-hack — S4 魔改阶段

一句话定位:推荐魔改方向,制定计划,动手改造项目,直到用户满意。

何时使用

  • ✅ S3 教学 100% 完成,用户选择进入 S4
  • ✅ 用户说"我想魔改"、"给项目加功能"、"改造一下"
  • ❌ S3 未完成 → 提示先完成 S3,转 dojo-teach
  • ❌ 用户只是想学习不想改代码 → 不强制进入

前置条件

  • <repo-root>/.codojo/schedule.md 存在且显示 100% 完成

若前置条件不满足,提示用户先完成 S3 教学。

工作流

Step 1:项目深度分析与推荐

全面分析项目代码,从以下维度推荐魔改方向:

  • 功能扩展:基于项目已有能力,可以新增哪些实用功能
  • 代码重构:哪些地方可以优化结构、提升可读性
  • 性能优化:有没有明显的性能瓶颈可以改进
  • 测试完善:缺少哪些测试覆盖
  • 工程化改进:CI/CD、日志、监控、文档等

输出推荐列表:

## 🎯 魔改方向推荐

根据项目分析,为你推荐以下魔改方向(可多选,也可以自己提需求):

### 功能扩展类
1. **<功能名>**:<一句话描述 + 预估工作量>
2. **<功能名>**:<一句话描述 + 预估工作量>

### 代码优化类
3. **<优化点>**:<一句话描述 + 预估工作量>
4. **<优化点>**:<一句话描述 + 预估工作量>

### 工程化改进类
5. **<改进点>**:<一句话描述 + 预估工作量>

---
请回复你想做的编号(如「1 3 5」),或者直接告诉我你想做什么改动。
回复「不了」可以跳过 S4,结束整个学习流程。

推荐原则

  • 推荐 5-8 个方向,不要太多造成选择困难
  • 每个方向要有具体可落地的改动点,不要空泛
  • 按难度从低到高排列
  • 预估工作量帮助用户做选择

Step 2:制定 plan.md

根据用户选择(可多选 + 自定义),生成 <repo-root>/.codojo/plan.md

# 魔改计划

> 根据你的选择生成,AI 将按此计划协助你改造项目。

## 概要

- **选定方向**:<列出>
- **预计总工作量**:约 X 小时
- **当前状态**:进行中

## 任务清单

### 任务 1:<任务名>
- **目标**:<达成什么效果>
- **涉及文件**:<文件路径列表>
- **实施步骤**:
  1. <具体步骤>
  2. <具体步骤>
  3. <具体步骤>
- **验证方式**:<如何确认改动生效>
- **状态**:⚪ 未开始

### 任务 2:<任务名>
...

## 变更日志

<!-- 每次有新需求或计划变更时追加记录 -->

生成后向用户确认:

plan.md 已生成,共 N 个任务。先从「任务 1:<名称>」开始?

Step 3:执行改造

plan.md 任务顺序,逐个协助用户完成改造。

代码改动确认协议

每个任务开始前,AI 必须先向用户展示改动方案并等待确认:

## 🔧 任务 N:<任务名>

**改动方案**:
1. 修改 `<文件路径>` — <改动说明>
2. 新增 `<文件路径>` — <说明>
3. ...

**影响范围**:<哪些现有功能可能受影响>
**验证方式**:<如何确认改动生效>

确认开始?回复「开始」执行,或提出修改意见。

用户回复"开始"、"可以"、"执行"后,才能动手修改代码。

执行流程

  1. 讲解思路:这个改动要做什么、为什么这样做、会影响哪些文件
  2. 协作实施:AI 生成代码改动,解释每处修改的含义,引导用户理解
  3. 验证结果:指导用户验证改动效果(编译、运行、测试等)
  4. 更新 plan.md:将任务状态更新为 ✅,记录完成时间

每完成一个任务:

---
✅ 任务 1「<名称>」已完成!

📋 **魔改进度**: 1/N 任务完成
⏭️ 下一个:任务 2「<名称>」

继续?或者有新想法随时告诉我。
---

Step 4:持续迭代

S4 阶段不设终点,持续接受用户新需求:

  • 用户新增需求 → 追加到 plan.md 任务清单,在变更日志中记录
  • 用户修改需求 → 更新 plan.md 对应任务,在变更日志中记录
  • 用户取消某个任务 → 标记为 ❌ 已取消,在变更日志中记录
  • 用户说"满意了"/"结束" → 输出完成总结

Read the full file on GitHub · 221 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. 2d ago First seen · 221 lines · 348 tokens per session scan A 99370e23641f

Subscribe to this mod's changes

dojo-hack is a skill published in the GitHub repository ttguy0707/codojo (56 stars, last pushed 2mo ago), licensed MIT. It adds 348 tokens to every session and 2,179 once invoked, about $0.0017 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 2026-08-30.

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